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Article Contents

1. introduction, 2. systematic literature review: methods and materials, 3. results of the bibliographic analysis of data, 4. applications of nanotechnology in ev, 5. summary of key research findings, 6. conclusions, declaration of competing interest, acknowledgements, author contributions.

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A systematic review of nanotechnology for electric vehicles battery

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Pulkit Kumar, Harpreet Kaur Channi, Atul Babbar, Raman Kumar, Javed Khan Bhutto, T M Yunus Khan, Abhijit Bhowmik, Abdul Razak, Anteneh Wogasso Wodajo, A systematic review of nanotechnology for electric vehicles battery, International Journal of Low-Carbon Technologies , Volume 19, 2024, Pages 747–765, https://doi.org/10.1093/ijlct/ctae029

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Nanotechnology has increased electric vehicle (EV) battery production, efficiency and use. Nanotechnology is explored in this electric car battery illustration. Nanoscale materials and topologies research has increased battery energy density, charge time and cycle life. Nanotubes, graphene and metal oxides improve energy storage, flow and charging/discharge. Solid-state and lithium-air high-energy batteries are safer, more energy dense and more stable using nanoscale catalysts. Nanotechnology improves battery parts. Nanostructured fluids reduce lithium dendrite, improving batteries. Nanocoating electrodes may reduce damage and extend battery life. Nanotechnology benefits the planet. Nanomaterials allow battery parts to employ ordinary, safe materials instead of rare, harmful ones. Nanotechnology promotes battery recycling, reducing waste. Change does not influence stable, cost-effective or scalable items. Business opportunities for nanotechnology-based EV batteries need more research. High-performance, robust and environmentally friendly batteries might make electric cars more popular and transportation more sustainable with research and development. An outline of EV battery nanotechnology researchexamines the publication patterns, notable articles, collaborators and contributions. This issue was researched extensively, indicating interest. Research focuses on anode materials, energy storage and battery performance. A research landscape assessment demonstrates EV battery nanotechnology’s growth and future. A comprehensive literature review examined nanosensors in EVs. Our study provides a solid foundation for understanding the current state of research, identifying major trends and discovering nanotechnology breakthroughs in EV sensors by carefully reviewing, characterizing and rating important papers.

The future of nanotechnology with electric vehicles (EVs) is uncertain. Researchers and engineers use nano-manipulating materials to boost EVs’ speed, efficiency and longevity [ 1 ]. Nanotechnology makes coatings for EVs, battery technology, energy harvesting, sensors, catalysis and lightweight materials possible [ 2 ]. By enhancing energy storage, charging speed, component weight and durability, nanotechnology makes electric automobiles more efficient and practical, which could help circumvent transportation system limitations. A cleaner, greener and more sustainable transportation future will be ushered in as nanotechnology improves the capabilities of electric automobiles and increases their widespread acceptability. Nanotechnology’s quick growth has generated exciting potential for the EV revolution [ 3 ]. The use of nanotechnology can improve the performance, efficiency and longevity of EVs. Battery technology, lightweight materials, energy harvesting, sensors, catalysis and coatings are all examples of applications where scientists and engineers have taken advantage of nanoparticles’ unique capabilities [ 4 ]. The batteries used in EVs are improving because of nanotechnology [ 5 ].

Graphene and carbon nanotubes are two examples of nanoscale materials that improve energy storage, electrode surface area and electrode conductivity. The lightweight materials made possible by nanotechnology may also impact EV design [ 6 ]. Nanomaterial composites, including carbon nanotubes and nanofibers, have superior strength-to-weight ratios. Lightening the load improves the vehicle’s mileage, range and handling. EVs use nanotechnology for energy collection. Nanogenerators convert the kinetic energy produced by the car’s motion and vibrations into electrical power. Nanostructured solar panels on the vehicle’s surface capture sun energy for EV charging [ 7 ].

Nanotechnology allows for the development sophisticated sensors that keep tabs on and control the operation of EVs. The susceptible and accurate nanosensors can measure temperature, pressure and gas concentrations, increase safety, give better battery management and enhance vehicle performance. Catalysis and fuel cell technologies, which provide alternatives to EV batteries, rely heavily on nanotechnology [ 8 ]. Using nanocatalysts, fuel cells can convert energy more cleanly. Fuel cells are an option for EVs because of their longer ranges and shorter recharging times [ 9 ]. Intelligent coatings and self-repairing materials can be produced via nanotechnology for EVs. Scratches, corrosion and weathering are all thwarted by nanocoating, and the coating’s self-healing properties repair minor damage [ 10 ]. These coatings’ durability, hydrophobicity and UV resistance increase EVs’ aesthetics, longevity and sustainability. As progress in nanotechnology advances, the outlook for EVs improves. Nanotechnology can make transportation systems more effective, practical and environmentally friendly [ 11 ]. Figure 1 shows the few primary ways nanotechnology can be applied to EVs. Cleaner, greener transportation may lie in the hands of EVs that utilize nanoparticles and nanoscale engineering.

Nanotechnology in EVs.

Nanotechnology in EVs.

To further explore how EV batteries might be improved, it is vital to provide a more comprehensive explanation of the specific roles played by nanotubes, graphene, metal oxides and nanoscale catalysts. Nanomaterials are crucial in enhancing different performance characteristics of EV batteries [ 12 ]. The remarkable conductivity features of nanotubes and graphene enable efficient electron transit, hence increasing the overall conductivity of the battery. Metal oxides enhance batteries’ energy storage capacity, improving their efficiency in storing and releasing energy during charge and discharge processes [ 13 ]. In addition, catalysts at the nanoscale operate as promoters to enhance the speed of reaction kinetics, optimizing the charge and discharge efficiency. Furthermore, integrating these nanomaterials improves EV batteries’ security and durability by addressing concerns regarding excessive heat generation [ 14 ] and establishing a more substantial structural foundation [ 15 ]. An in-depth investigation into the unique effects of each nanomaterial on energy storage [ 16 ], conductivity, charge/discharge processes, safety and stability issues offers a detailed comprehension of their combined influence on the advancement of EV battery technology [ 17 ].

Nanotechnology has become a major force in improving the performance and economy of batteries for EVs. Materials like nanotubes, graphene, metal oxides and nanoscale catalysts play key roles in this. Nanotubes and graphene are great for storing energy because they are highly conductive and have a lot of surface area [ 15 ]. This makes them better electrode materials for lithium-ion batteries, which are widely used in EVs. These materials make it easier for electrons and ions to move faster, which leads to more energy and faster charging and discharging. On the other hand, metal oxides are great alternatives to standard cathode materials because they have higher capacity and better cycling stability. Their nanoscale size makes charge transfer even more efficient, which lowers the energy lost during cycles [ 18 ]. Graphene is a single layer of carbon atoms grouped in a hexagonal lattice. It is very good at conducting electricity and is also very strong. It makes the conductivity better when added to battery electrodes, which speeds up the charging and discharging processes. Nanotubes, like carbon nanotubes, have a lot of surface area and can carry electricity well. This has helped make electrode materials that are light and work well. Metal oxides, such as manganese oxide and titanium oxide, have better electrochemical qualities [ 19 ]. This meets the need for materials in EV batteries that have a higher energy density and last longer between cycles.

Moreover, tiny catalysts have completely changed the electrochemical processes that happen inside batteries. These helpers, which are usually made of precious metals like platinum and palladium, make the processes that reduce and release oxygen easier in fuel cells and metal–air batteries. This makes them work better overall. Nanostructuring these catalysts also makes them more active, which means they can convert and store energy more efficiently. When it comes to safety, nanomaterials help make improved battery systems possible [ 20 ]. Nanoscale coatings and additives can make lithium-ion batteries safer by making them more stable at high temperatures, lowering the risk of burning, and stopping dendrites from forming. Nanomaterials also make things more stable, which means batteries last longer. This means they don’t need to be replaced as often, and throwing away batteries has less of an effect on the world [ 21 ]. According to the researchers, adding nanotubes, graphene, metal oxides and tiny catalysts to the design and make-up of EV batteries is a big step forward for making them safer, more stable and better at storing energy and conducting electricity. As scientists continue to study and improve these nanotechnology uses, the chances of making electric transportation more efficient and environmentally friendly grow [ 22 ].

1.1. Importance of nanosensors

Sanguesa et al. (2021) reviewed EV battery technological trends, charging techniques and future research challenges and prospects. They discussed the pros and cons of lead-acid, nickel-metal hydride, lithium-ion and lithium-sulfur EV batteries. They also discussed EV charging standards and modes such as AC and DC, slow and rapid, wireless and bidirectional. They discussed nanosensors’ high sensitivity, low power consumption, miniaturization, fabrication, integration and calibration concerns for battery management [ 23 ]. A literature overview on EV technology and its various uses, including smart grid, vehicle to grid, and vehicle to home, was presented. Energy efficiency, emissions reduction, grid stability, battery cost, range anxiety and charging infrastructure were only some of the topics covered. Several global EV programs and projects were highlighted, including Tesla, Nissan Leaf and Dubai Smart City [ 24 ]. A systematic literature review (SLR) on EV consumer acceptance was published by Han et al. [ 25 ]. They synthesized the methodology, ideas and variables from 57 peer-reviewed publications published between 2015 and 2022 on EV purchase, behavior and usage intentions. They found that attitudes, norms, perceived behavioral control, awareness, knowledge, personal values, emotions and social influence affect EV adoption. They also highlighted literature gaps and suggested additional research [ 25 ]. The authors of Basu et al. (2018) provided a synopsis of EVs and EV sensors. The advantages and disadvantages of the various varieties of EVs, including fuel cell EVs (FCEVs), battery EVs (BEVs) and hybrid EVs (HEVs), were discussed. Additionally, they deliberated on the evolving landscape of micro-electro-mechanical system- (MEMS) based miniaturization for sensors and devices utilized in diverse EV applications and the various types of sensors associated with battery and position monitoring [ 26 ].

1.2. Literature review

The low-carbon mobility transition relies on plug-in EVs, including battery and hybrid EVs. Daramy-Williams et al. (2019) found many user experience topics, including driving and travel behaviors, vehicle interactions and subjective factors. EVs have increased the utilization of electric batteries. Many studies have developed and improved battery cell voltage equalization methods by adding noteworthy features [ 27 ]. These cell equalization techniques were the subject of a thorough and methodical review (Das et al., 2020). This SLR focuses on recent efforts that have built energy management storage system (EMSs) for HEVs [ 28 ]. The study conducted by Torreglosa et al. (2020) aimed to provide a quantitative analysis of the chosen works. Notwithstanding advancements in driving range and recharge alternatives, these and additional market impediments persist, rendering the present market share of BEVs inconsequential [ 29 ]. In light of the advancement of hydrogen fuel cell stacks, an emerging powertrain architecture concept for N1 class-type cars was described (Castillo et al., 2020). The fuel cell extended range electric vehicles (FC-EREV)concept combines a battery-electric arrangement with a hydrogen-powered fuel cell stack that acts as a range extender. The lithium-ion battery in EVs can sustain an operational temperature range of 15°C to 35°C employing a battery thermal management system (BTMS) [ 30 ].

Tete et al. (2021) conducted a comprehensive review of experimental and numerical analyses of BTMS utilized in electric and hybrid vehicles. This review encompassed approaches such as air, liquid, phase change material, heat pipe and refrigeration cooling for battery cooling systems. EVs need high-energy batteries. The maximum capacity of lithium-air battery theory using graphene under optimal electron conduction conditions and the experimental maximum obtained by optimizing the structure geometry, examples of structural engineering using carbon fiber and carbon nanotubes in cathode fabrication to perform the reaction properly while providing space for lithium oxide placement, are examined [ 31 ]. Suryatna et al. (2022) described the battery’s mechanism and analyze its constituent parts. As a result of the growth of green logistics and the support of new energy car development policies both domestically and globally, logistics and distribution have embraced EVs as an alternative to traditional fuel vehicles [ 32 ]. Ye et al. (2022) examined the most recent breakthroughs in EV routing models and solution algorithms in logistics and distribution. Passenger vehicles contribute significantly to glasshouse gas (GHG) emissions; thus, precise and current estimates of the comparative emissions of the key types of alternative power trains are essential to support evidence-based policy recommendations [ 33 ]. A systematic review and harmonization of the most recent scientific literature on this subject was presented in Raugei (2022). The findings show that battery BEVs are the most promising option for decarbonizing the passenger vehicle fleet across all global regions studied, with the potential for −70% reductions in GHG emissions compared to conventional gasoline-powered internal combustion engine vehicles. EVs are becoming increasingly prevalent as many nations set net-zero carbon targets for the foreseeable future [ 34 ].

The status, characteristics and application scope of global lithium ion battery (LIB) safety standards and regulations were examined by Lai et al. (2022). The rational test standard upgrade is reviewed in light of recent EV and energy storage power plant fires. Direct drive offers improved systematic economy, more flexible wheel control and better passenger comfort by eliminating the gearbox and transmission [ 35 ]. Cai et al. (2022) reviewed vehicle direct-drive methods and contemporary electrical machine improvements for direct-drive propulsion systems. FCEVs for long-haul applications can reduce road freight CO 2 emissions until long-distance battery-electric mobility matures, depending on the hydrogen fuel source [ 36 ]. Pardhi et al. (2022) examined FCEV powertrain topologies for long-distance HD applications, as well as their operating constraints, cooling requirements, waste heat recovery methods, cutting-edge powertrain control, energy and thermal management strategies, as well as over-the-air route data-based predictive powertrain management with V2X connectivity. Batteries powering EVs provide a promising way to reduce pollution and uncertainty.

In contrast to battery degeneration’s scalability and temporal constraints, machine learning (ML) approaches offer a non-invasive, accurate and low-processing solution [ 37 ]. Sharma and Bora (2023) evaluated these problems objectively and comprehensively. EVs are becoming mainstream as more governments set near-term net-zero carbon ambitions [ 38 ]. Senol et al. (2023) conducted a comprehensive literature analysis in their study, which focused on integrating power networks and Li-ion battery technologies, particularly emphasizing their performance under unfavorable weather circumstances [ 39 ]. Increasing multiscale modeling and design for battery efficiency and safety management was reviewed by Kiran MD et al. (2024). This article shows how machine learning-based data analysis in battery research has advanced, setting the groundwork for cloud and digital battery management to produce trustworthy onboard applications [ 40 ]. Electron and ion transport affect the battery’s energy production under application conditions and how much energy can be used [ 41 ]. The transport mechanisms of ions and electrons for active materials, in addition to positive and negative composite electrodes, were examined by Quilty et al. (2023). Simultaneously, contemporary EVs exhibit a confident capacity to reduce fossil fuel consumption [ 42 ]. Gevorkov et al. (2023) reviewed and analyzed multiport converters’ key characteristics, topologies, pros and cons and applications. Operando characterization is not new, but techniques that can track commercial battery properties under realistic conditions have unlocked a trove of chemical, thermal and mechanical data that could revolutionize lithium-ion device development and use [ 43 ]. The innovative dual-ion battery that is built on aluminum and has a cathode made of three-dimensional graphene possesses high-energy density, low cost and the ability to charge and discharge faster than other batteries, making it an ideal choice for grid storage and personal gadgets by Zhang et al. (2016) [ 44 ]. A novel calcium-ion battery with a high discharge voltage and 95% capacity retention can function consistently at room temperature. This battery has the potential to serve as an alternative to lithium-ion batteries by Wang et al. (2018) [ 45 ]. For sodium-based energy storage applications, this study conducted by Mu et al. (2020) successfully synthesizes high-performance anodes with high-fraction active materials. These anodes achieve good rate capability and cycling stability [ 46 ]. Zhang et al. (2023) examined the difficulties encountered by power systems that heavily rely on inverter-based resources (IBR), specifically emphasizing the inadequate capacity to support voltage and frequency. The authors suggest reorganizing the virtual synchronous generator (VSG’s) control blocks by aligning the control blocks of a VSG with the control perspective of conventional synchronous generators. The reorganization above streamlines and clarifies the control pathway governing the system’s active and reactive power output, enabling easier virtual inertia and attenuation parameters to be adjusted. The article presents a compact signal model for IBR controlled by VSG, including voltage and current loops modules, instantaneous power calculation and an LC (inductors (L) and capacitors (C)) filter [ 47 ]. A current discrepancy that occurs during charge and discharging in high-temperature superconducting (HTS) non-insulation closed-loop coils may result in novel phenomena, such as a rapid decrease or increase in magnetic field at particular positions, and may influence operational current judgment by Lu et al. (2022) [ 48 ]. Wu et al. (2023) centers on the advancements in rare earth-barium-copper oxides superconducting tapes, with a specific emphasis on the nanocomposite EuBa2Cu3O7-δ superconducting films. These films hold significant importance in the context of high-field magnet applications. The scientists utilize a pulsed laser deposition methodology with an exceedingly rapid growth rate of up to 100 nm/s—two orders of magnitude higher than traditional approaches. This enables them to accomplish both rapid growth and a substantial capacity to transport field current [ 49 ]. Shen et al. (2024) have presented an innovative energy management approach that seeks to optimize the functionality of energy storage systems in EVs, specifically focusing on reducing the aging of lithium-ion batteries caused by high-frequency power requirements. Fuzzy logic control and ensemble empirical mode decomposition are incorporated into the proposed method. At the outset, the power demand of EVs is decomposed into intrinsic mode function components. Subsequently, permutation entropy is utilized to reconstruct each component into low- or high-frequency components [ 50 ]. A proposal was made by Zhao et al. (2023) for the preview-based human-like trajectory planning model (PHTPM), which is subsequently evaluated and assessed through comparative and generalizability tests. The findings indicate that the implementation of the driver preview feature empowers PHTPM to precisely emulate the attributes of proficient drivers during left turns while surpassing them during right turns [ 51 ]. A critical scenario search technique for intelligent vehicle testing that is based on the social cognitive optimization algorithm has been suggested, and the findings show that the proposed method has the potential to increase both the search efficiency, and the coverage of important scenarios was figured out by Zhu et al. (2023) [ 52 ]. This research presents a novel model that has the potential to improve our understanding of the behavior of lithium deposition and pave the way for the development of stable and secure lithium metal anodes through the utilization of bimetallic metal organic framework- (MOF) derived materials in the construction of three-dimensional frameworks studied by Wei et al. (2023) [ 53 ]. Based on the findings of the analysis conducted by Yue et al. (2023), it can be concluded that the stability of the road system in the presence of incident effects is directly connected to the severity of the event, the signal control strategy, the penetration rate and the spatial distribution of autonomous vehicles. In conclusion, simulation results are carried out to demonstrate the efficacy of our suggested event management policy in enhancing the rate of recovery and the stability of road networks [ 54 ]. In order to solve the issue in which the response quality is decreased as a result of factors such as parameter mismatch and disturbance, an adaptive disturbance observer-based improved super-twisting sliding mode control (ISTSMC-ADOB) has been developed. With the purpose of achieving adaptive compensation, avoiding the usage of high-gain feedback, and expanding the applicability of the conventional disturbance observer, an adaptive disturbance observer, also known as an ADOB, was intentionally constructed by Lu et al. (2022) [ 55 ]. Yu et al. (2023) has discussed the difficulties that arise when attempting to implement aqueous zinc-ion batteries in practical applications. The paper focuses on the unstable electrode/electrolyte interface that is related to inhomogeneous zinc deposition and side reactions. The solution that has been offered involves the utilization of L-carnitine (L-CN) as an effective addition for the purpose of stabilizing electrodes and extending the lifespan of batteries. When L-CN is present in minute quantities, it exhibits a remarkable synergy between quaternary ammonium cations, COO− anions and hydroxyl groups. This synergy can influence the electrochemical deposition/insertion of Zn 2+ and the activity of water molecules [ 56 ]. Hou et al. (2023) have centered on developing a self-powered, lightweight biomimetic mouse whisker sensor (BMWS) that draws inspiration from the extraordinarily perceptive whisker detection exhibited by mice. The BMWS, unlike its predecessors that attempted to imitate animal whiskers, surmounts drawbacks, including a cumbersome design, dependence on external power and restricted application scenarios. By utilizing the triboelectric effect and a meticulously engineered framework, the BMWS exhibits exceptional capabilities in detecting collisions and maintaining signal stability. Intelligent early warning, direction identification, hole width discrimination and real-time distance sensing are among the many duties in which it excels [ 57 ]. A novel mechanism and technology for directed energy deposition-arc in the production of large parts, referred to as alternating-arc-based additive manufacturing and enabled by a polarity-switching self-adaptive shunt, are presented Yan et al. (2023). The experimental outcomes demonstrate that the proposed system facilitates the alternating passage of current through the welding wire and the substrate, generating electronegative arcs at the anode with the wire and electropositive arcs at the cathode with the substrate. By modulating the arcs, decoupling control between thermal force and mass transfer is accomplished. The electropositive arc is responsible for cathode cleaning and molten pool temperature, whereas the electronegative arc regulates wire melting, particle size and temperature [ 58 ]. A battery–supercapacitor hybrid energy storage system and an accompanying energy management strategy are proposed in the article by Wang et al. (2024) in a response to the growing need for compact motors with high output torque, specifically for use in mobile robotics. This method draws inspiration from automobiles’ high-capacity hybrid energy systems. In conditions of minimal torque, the motor operates on battery power, with any excess power being directed toward charging the supercapacitor. When faced with torque overload conditions, the motor is supplied with high current by swiftly discharging the supercapacitor, which guarantees an instantaneous increase in output power. The objective of the energy management strategy is to regulate the supercapacitor’s charging and discharging processes to prevent interference with the motor’s power supply from the battery and maintain current stability and control during discharge [ 59 ].

Process of SLR.

Process of SLR.

Flow chart of selection of data for bibliometric analysis.

Flow chart of selection of data for bibliometric analysis.

The resources utilized in this SLR comprise bibliometric data extracted from various sources, such as journal articles, conference papers, scholarly surveys, books, essays and review articles. These sources are analyzed to determine author affiliations, country collaboration maps, citation patterns, keywords plus and the frequency of author keywords. The present study employs a systematic procedure to select research publications about nanomaterials in EVs. The literature search involved comprehensive exploration using databases like Scopus. Key search terms included ‘nanotechnology in EV batteries,’ ‘electrode nanocoating’ and related variations. Potential biases in article selection were mitigated by employing rigorous inclusion criteria, prioritizing peer-reviewed studies and avoiding undue influence from commercial interests. Figure 2 shows the process of SLR.

The Scopus article research selections were restricted in the first step, as shown in Figure 3 and Table 1 . From 1991 to July 2023, this analysis uncovered 2361 publications concerning EVs, nanotechnology, nanomaterials and sustainable development. We have 2322 articles remaining after eliminating concise surveys, notes, erratum editorials and letters. After publication status-based exclusions of research papers, 2309 remained. The final subjects that were excluded from consideration were the following: astronomy and physics, pharmacology, toxicology, pharmaceutics, earth and planetary science, health profession, agriculture and biological science, neuroscience, immunology, microbiology, business and management, accounting, economics, econometrics and finance, arts and humanities and dentistry. One thousand five hundred forty products were remaining.

Bibliometric analysis is a method that quantifies scientific publications by examining citation patterns. Citations, according to a bibliometric study, reveal the impact, influence and connections of academic journals [ 60 ]. The bibliometric analysis theory posits that scientific knowledge is transmitted through scholarly publications, wherein the number and caliber of citations gauge the scientific significance of an article or publication it garners [ 61 ]. Bibliometric researchers utilize citation data to quantify author output, journal influence, collaboration patterns and research trends. According to this theory, more people cite influential articles [ 62 ].

Additionally, intellectual networks between authors, institutions and fields may be reflected in citations [ 63 , 64 ]. By quantifying these citation patterns, bibliometric analysis evaluates research output, influence and collaboration [ 63 ]. Bibliometric research is regulated by mathematical and statistical models, including co-citation analysis, citation counts, h-index and bibliographic coupling. Academics can quantify and contrast scholastic output, discern notable works and authors and monitor the evolution of specific publications or research domains through these techniques. Citation patterns can disclose the structure and dynamics of scientific knowledge, aiding policymakers, institutions and researchers in making informed decisions regarding funding allocation, research evaluation and scholarly communication, according to bibliometric analysis [ 65 ].

By examining the bibliography, the most cited journals are determined. Energy and environmental science, nanoenergy, ACS Applied Materials and Interfaces and the Journal of Materials Chemistry , all of which have contributed to the study of nanomaterials in EVs, are utilized in the proposed research. Numerous scholarly periodicals indicate that nanomaterials are an emerging trend for energy storage and lightweight materials to reduce the weight of EVs, as demonstrated by this review [ 66 ]. Bibliometric analysis is an expert methodology that assists researchers in assessing the advancement of various approaches through the statistical distribution of data about citation analysis, keywords, author affiliation and country of contribution. The top journals are shown in Table 2 .

Detailed analysis of nanomaterials in EV data is presented in this section. In recent decades, the quantity of publications and literary sources is evaluated in Sections 3.1 and 3.2. Additionally, the country production analysis is presented in Section 3.3. The thematic evaluation of the keywords in the publications is presented in Section 3.4. Additionally, the collaboration map of the top 15 nations, most cited countries, country production over time and source dynamics is provided in Section 3.5. Section 3.6 provides a comprehensive analysis of the findings regarding author keywords, most common terms, a tree map of keywords and word clouds. Section 3.7 presents three field plots depicting the relationships between author keywords, author countries and keywords plus.

3.1. Literature analysis of publications

Figure 4 illustrates the annual distribution of publications from 1991 to 2023. However, upon examining the graph, we have identified a substantial fluctuation in the number of publications on nanomaterials in EVs. The number of publications in this field was ‘0’ before 2003 but rose after 2005. However, between 2012 and 2021, there has been a significant increase in the number of publications devoted to investigations. The calculated annual growth rate was approximately 12.79%.

3.2. Institutions

A visual representation of the collaborations between the five most prestigious institutions worldwide and their publications over the last 30 years is presented in Figure 5 . As stated, five major institutions contribute to the Scopus Journal, with Nanyang Technological University ranking first with 945 publications and Beijing University ranking second with 586 publications. Simultaneously, the University of Science and Technology of China, the School of Material Science and Engineering and Tsinghua University ranked third, fourth and fifth in the Scopus database from 1991 to 2023, with net publications of 528, 475 and 409, respectively.

Main information of data taken from Scopus.

Top 15 journals in the research area under the study, 3.3. analysis of countries.

Diverse nations have contributed to academic journals concerning the application of nanotechnology to EVs. The cumulative number of publications originates from the 10 countries in Figure 6 . Based on the analysis, China holds the highest position regarding the total number of articles. Following that, India and the United States occupied the second and third positions, respectively, with 1584 and 480 publications, while China produced 2370 articles within the specified period.

3.4. Thematic evaluation of keywords

Based on the evaluation of keywords from 1991 to 2023, as shown in Figure 7 , several authors have repeatedly utilized a small number of keywords. The following terms were examined following the analysis: self-powered sensors, batteries, triboelectric nanogenerator, nanotechnology, renewable energy, lithium-ion batteries, lithium-ion batteries, electrode material and electrochemical performance.

Annual distribution of the publications.

Annual distribution of the publications.

Top 5 productive institutions.

Top 5 productive institutions.

Top 15 countries with the maximum number of publications.

Top 15 countries with the maximum number of publications.

Thematic evaluation of keywords.

Thematic evaluation of keywords.

3.5. Country collaboration

The collaborative efforts of numerous nations are illustrated in Figure 8 , and Figure 9 shows the cloud-based structure of the various country collaborations. Based on the bibliometric survey’s analysis, the following countries have been identified as having the greatest number of collaborations in the field of nanotechnology for EVs: China, India, the United States, South Korea, Germany, Japan, the United Kingdom, the Philippines, Singapore, France, Australia, Canada, Italy, Spain and Malaysia.

Country collaboration map.

Country collaboration map.

Cloud-based structure of the various country collaborations.

Cloud-based structure of the various country collaborations.

3.6. Keywords

Each node in Figure 10a corresponds to a distinct keyword. We opted to include only the top 50 out of 1000 keywords. The prevailing research topics identified were ‘nanotechnology,’ ‘lithium,’ ‘electrodes,’ ‘electric batteries,’ ‘nanostructured materials’ and ‘energy efficiency.’ A cloud structure of keywords is depicted in Figure 10b , which resembles the analysis in Figure 10a ; however, the distinction between the two figures lies in the quantity of keywords. The authors spent the most time gathering the following 50 keywords for the cloud of keywords: ‘nanotechnology,’ ‘lithium-ion batteries,’ ‘lithium,’ ‘electrodes,’ ‘electric batteries’ and lithium compounds. In contrast, Figure 10c illustrates the tree map structure of keywords as they appear in the publications of various authors.

a. Keyword co-occurrence network. b Keywords cloud of top 50 keywords. c. Tree map of keywords.

a. Keyword co-occurrence network. b Keywords cloud of top 50 keywords. c. Tree map of keywords.

3.7. Three-field plot

The three field plots between author keywords, author countries and keywords plus were depicted in Figure 11 . The preceding 20-author keywords, utilized by most authors in their publications, are displayed on the left side of the plot. The author countries to which they belong are then indicated in the middle. The top 20 countries were analyzed, with China, the United States, Korea and India ranking the highest. The concluding segment of the narrative presents keywords plus, which includes the top 20 keywords and those used by the author. Concerning the keywords, this plot is structured to examine the research field and the authors’ collaborative network.

Three field plots between author keywords, author countries and keywords plus.

Three field plots between author keywords, author countries and keywords plus.

3.8. Bibliometric analysis challenges

Researchers may encounter several problems and restrictions when using R-Studio Biblioshiny for bibliometric analysis. One big problem is that bibliographic records are very different from one another. This is because different sources may use different formats and terminologies, which makes it hard to combine and standardize data. It might also be hard to ensure the bibliometric study’s accuracy if the data’s quality and completeness vary from database to database [ 67 ]. Another problem is that scientific literature is always changing; new papers are always being added, and databases might not be updated very often, which could mean the results are incomplete or outdated. Also, full-text articles may not always be available or easily accessible, which could affect the study’s completion. Researchers may also have trouble developing relevant search queries because the buzzwords and the criteria they use to include or leave out information can greatly affect the results [ 68 ]. Even with these problems, R-Studio Biblioshiny for bibliometric analysis is still a useful tool that helps researchers get around many of these issues and learn important things about the scholarly world.

Additionally, picking the right bibliometric indicators and measures presents different problems. To measure the effect, output and teamwork, researchers must be meticulous in picking the right factors. If you misinterpret metrics or only use a small group of indicators, you might come to the wrong conclusions about the importance of certain study topics or authors [ 69 ]. Also, because some fields are changing and combining different areas of study, it might be hard to correctly group papers into the right research domains. When doing bibliometric analysis, it is also very important to think about ethics, especially when there are disagreements about who wrote a paper or when someone cites themselves in a paper. To ensure the results are accurate, it is important to balance automated data extraction and human verification [ 70 ]. There may also be problems with how bibliometric analyses can be repeated since different researchers may get different results depending on how they read parameters or how the data sources are changed. Even with these problems and restrictions, R-Studio Biblioshiny has an easy-to-use design and many tools that can help with many of them. As software is constantly updated and made better, some problems may be solved. This makes bibliometric analysis a useful and ever-evolving way to understand the scholarly scene. As the scientific literature changes quickly, researchers should stay alert and deal with these problems to ensure their results are strong and correct [ 71 ].

Nanotechnology is currently leading the way in transforming numerous aspects of EV technology. Nanoscale materials, including carbon nanotubes and graphene, are important in battery technology because they improve electrode conductivity, surface area and energy storage. This leads to enhanced energy density, prolonged battery life and accelerated charging, effectively mitigating apprehensions regarding charging durations and range anxiety [ 72 ]. In addition to batteries, nanomaterials can generate lightweight and robust materials for EV construction. This is demonstrated through the integration of carbon nanotubes and nanofibers into composites. This phenomenon fortifies the structure and positively impacts the vehicle’s energy efficiency, range and overall performance [ 73 ]. Energy harvesting in EVs is further facilitated by nanotechnology, as nanostructured solar panels capture solar radiation, and nanogenerators convert mechanical energy into electrical power, thereby contributing to sustainable electricity generation [ 74 ]. Incorporating nanomaterials into supercapacitors and energy storage improves regenerative braking and power delivery, increasing energy storage and charge–discharge rates [ 75 ]. Nanosensors, renowned for their accuracy and sensitivity, provide advantages to EV control and monitoring systems by facilitating the observation of gas, pressure, temperature and concentrations for enhanced performance and safety.

Furthermore, progress in catalysis and fuel cell technologies is facilitated by nanotechnology, which has the potential to supplant conventional EV batteries and enhance the efficiency of energy conversion [ 76 ]. Intelligent coatings and self-healing materials, enabled by nanotechnology, safeguard EV surfaces against corrosion, weathering and abrasion by forming hydrophobic, UV-resistant and durable coatings that increase longevity. Fundamentally, nanotechnology manifests as a paradigm-shifting influence, initiating an era of enhanced EV performance, sustainability and innovation [ 77 ]. Figure 12 shows nanotechnology’s applications, challenges and future perspectives in EVs.

Nanotechnology for EVs.

Nanotechnology for EVs.

4.1. Challenges of nanotechnology in EVs

Although nanotechnology can greatly enhance EVs, it presents considerable obstacles that must be thoughtfully assessed and resolved inventively. The significant challenge of scalability arises from the difficulties associated with mass-producing nanotechnology processes, which hinder the consistent and superior integration of nanomaterials into EV components. The complexities inherent in nanotechnology further complicate issues about scalability, thereby demanding progress in manufacturing methodologies to guarantee extensive implementation [ 78 , 79 ]. Nanotechnology materials and processes are more expensive to manufacture; thus, cost-effectiveness is essential. Nanomaterials in EV batteries and lightweight components may raise production costs, affecting EV affordability and consumer acceptability. Another major challenge is EV nanoparticle and nanostructure stability. Nanomaterials must be durable and reliable to maintain EV performance and safety, especially under harsh operational conditions [ 80 ].

These stability difficulties require continual research and development to improve nanomaterial robustness and compatibility with EV dynamics. In EV nanotechnology integration, environmental concerns are essential [ 81 ]. Nanomaterial manufacturing, use and disposal must meet environmental goals to address health and ecological problems throughout the life cycle of nanotechnology in EVs. Research and collaboration between the scientific and manufacturing sectors aim to overcome these limitations and advance nanotechnology’s use in EVs. Scalability, cost-effectiveness, long-term stability and environmental impact can be addressed to realize nanotechnology’s transformative promise in EVs, enabling sustainable, efficient and widely accessible electric mobility [ 82–84 ].

4.2. Commercial potential of nanotechnology in EV batteries

The discourse on the commercial viability of nanotechnology-enabled EV batteries involves a comprehensive examination of the promising breakthroughs and the obstacles and limitations that hinder their widespread use. Nanotechnology has become a robust and influential factor in energy storage, specifically EV batteries [ 85 ]. These technological developments are crucial for achieving higher performance metrics, including increased energy storage capacity, enhanced conductivity, efficient charge and discharge cycles and enhanced safety and stability. When considering the economic potential of nanotechnology in EV batteries, it is crucial to carefully examine the obstacles that could impede its smooth absorption into the market [ 86 ].

An essential component of this topic revolves around comprehending the market’s preparedness for EV batteries that incorporate nanotechnology. Although the potential advantages are significant, the market must be sufficiently prepared to adopt these technical advancements. Market preparation covers various elements, including establishing infrastructure for manufacturing and distribution, creating regulatory frameworks that can handle emerging technology and promoting consumer awareness and acceptance [ 87 ]. Evaluating the present level of preparedness offers valuable information regarding the timeframe and viability of implementing nanotechnology-enhanced EV batteries on a broader scope. Simultaneously, it is crucial to tackle the obstacles posed by incorporating nanotechnology in EV batteries. The issues encompass a wide spectrum, from intricate technological obstacles to financial constraints. Nanomaterials, including nanotubes, graphene and metal oxides, provide remarkable advancements at the molecular scale. However, their ability to be scaled up and their cost-effectiveness in industrial applications must be thoroughly evaluated. The complexities of production processes, potential environmental consequences and the requirement for specialized knowledge provide problems that require careful and thorough consideration [ 88 ].

Moreover, the conversation should include the possible obstacles to the adoption that could hinder the universal approval of nanotechnology-enabled EV batteries. Barriers can appear in different ways, such as economic, technological and societal issues. Economic factors encompass the aggregate expenses of production, prospective price increases for consumers and the economic viability for producers [ 89 ]. Technological impediments encompass challenges such as the requirement for standardized testing protocols, concerns regarding reliability and compatibility issues with current infrastructure. The success of nanotechnology-enabled EV batteries is heavily influenced by societal variables, including how the public perceives and accepts new technologies. The economic feasibility of EV batteries enhanced by nanotechnology also depends on effectively addressing safety issues and environmental consequences [ 90 ]. Although nanomaterials provide advancements in battery safety and stability, it is crucial to conduct a comprehensive assessment of the potential hazards related to the manufacturing and disposal of nanomaterials. Establishing or modifying regulatory frameworks is necessary to guarantee the secure and conscientious incorporation of nanotechnology into the electric car industry [ 91 ].

Strong competition in the EV sector complicates commercializing nanotechnology-enabled batteries. Given the ongoing advancements in traditional battery technologies and other energy storage solutions, it is crucial to consider the placement of nanotech-enabled batteries in this changing landscape [ 92 ]. An essential aspect of evaluating the commercial success of nanotechnology-enabled EV batteries is comprehending the competitive dynamics, developing distinctive selling factors and distinguishing them from existing technologies. Moreover, the influence of government regulations and incentives in defining the commercial path of nanotechnology-enabled EV batteries should not be underestimated [ 93 ]. Implementing supportive regulations, financial incentives and research funding can greatly expedite the implementation and acceptance of these advanced energy storage options. On the other hand, if there are no well-defined norms or obstacles in regulations, it could impede innovation and discourage investment in the emerging market of nanotechnology-enabled EV batteries [ 94 ].

4.3. Nanoscale breakthroughs boost battery performance

One of the most important factors in improving the overall performance of EV batteries is the adoption of nanoscale enhancements, which are advances on a smaller scale. The essential components of the battery, such as electrodes and catalysts, are subjected to transformations due to using materials such as nanotubes, graphene, metal oxides and nanoscale catalysts [ 95 ]. The nanoscale dimensions of these materials contribute to an increase in surface area, an improvement in conductivity and an enhancement in electrochemical characteristics. Due to these enhancements, the battery’s cycle life is extended, its charge and discharge rates are accelerated and its energy density is increased [ 96 ]. Furthermore, the improvements in nanotechnology also address safety concerns by reducing the dangers of overheating and limiting the creation of dendrites. As a result, the cumulative effect of these small-scale advancements dramatically raises the overall efficiency, reliability and sustainability of EV batteries, paving the way for a more optimistic future in electric transportation [ 86 ].

4.4. Future perspectives of nanotechnology

A comprehensive evaluation of the environmental consequences of incorporating nanomaterials and nanotechnology processes into EVs is imperative. This underscores the criticality of assessing health and environmental risks to ascertain nanotechnology’s feasibility and ecological sustainability in this domain. When considering the future, nanotechnology can fundamentally transform numerous facets of EV technology [ 97 ]. Nanomaterials possessing enhanced energy storage properties such as higher energy densities, faster charging capabilities and longer cycle lifetimes hold promise for substantially augmenting EV batteries’ range, efficiency and overall performance. Furthermore, continuous investigations into nanomaterials have the potential to yield sophisticated lightweight materials that integrate composites and nanoscale structures. Such materials would augment EV construction materials’ strength while decreasing their weight. Consequently, this could enhance maneuverability, range and energy efficiency [ 98 , 99 ].

Nanotechnology can create smart, functional surfaces with self-cleaning, anti-fogging and anti-icing qualities that improve EV visibility and efficiency while reducing maintenance. Nanosensors in EVs enable real-time battery health, temperature and performance monitoring, improving charging, safety and maintenance [ 100 ]. As research advances, sustainable nanomaterials that use abundant, non-toxic resources to make the EV sector more environmentally friendly become a priority. As EV nanomaterial recycling reduces resource depletion and waste throughout the lifecycle of EVs, it supports a circular economy. These advances demonstrate the continuous commitment to leverage nanotechnology’s transformative potential in EVs, balancing innovation with environmental responsibility for a sustainable electric mobility future [ 101 , 102 ].

Readers interested in delving more into nanotechnology in batteries will find that internet resources that clarify complicated topics are an excellent place to begin their exploration [ 103 ]. Websites like Nano.gov provide explanations of nanotechnology and its uses that are suitable for beginners. These explanations include the role that nanotechnology plays in battery technology. In addition, educational sites such as Khan Academy offer illuminating video lessons on nanoscience and its applications in the real world, which makes the process of learning more interesting and accessible [ 104 ]. Exploring articles on respected science websites such as Scientific American or National Geographic can provide in-depth yet understandable insights into how nanomaterials contribute to breakthroughs in battery efficiency. This is especially helpful for individuals who prefer a more hands-on approach [ 105 ]. The last point is that books such as Nanotechnology for Dummies by Earl Boysen and Nancy Boysen offer a straightforward guide to comprehending nanotechnology. This makes it an invaluable resource for readers anxious to dispel the mystery surrounding this fascinating field [ 106 ].

This study conducted a systematic literature analysis to analyze bibliometric data from various sources to gain insights into the changing research landscape of nanomaterials in EVs. The study primarily examined author affiliations, nation collaboration maps, citation patterns, keywords and the frequency of author keywords. The research publication selection process is depicted in Figure 2 , highlighting the meticulousness and thoroughness of the literature evaluation. Between 1991 and July 2023, the research yielded 2361 articles about EVs, nanotechnology, nanomaterials and sustainable development. By applying the exclusion criteria, the selection was narrowed down to 1540 articles that were deemed relevant, resulting in a strong dataset for bibliometric analysis. Prominent journals such as Energy and Environmental Science , Nano Energy , ACS Applied Materials and Interfaces and the Journal of Materials Chemistry emphasized the importance of nanoparticles in the context of EVs. The significance of bibliometric analysis in comprehending the impact, influence and interconnections among academic journals was underscored. Citation patterns were employed to assess the scientific value of articles, monitor research trends and evaluate collaboration networks. The study demonstrated the ever-changing nature of how scientific knowledge is spread and highlighted the importance of bibliometric research in guiding politicians, institutions and researchers when making decisions. Figure 3 depicts how the Scopus article research selections were reduced in the first step.

Section 4 included an in-depth examination of nanomaterials in EVs, including a study of publishing trends, country production, thematic evaluation of keywords, collaboration maps and field plots. Figure 4 depicts the yearly dissemination of publications from 1991 to 2023. It shows a notable increase in research efforts beginning in 2005, with an estimated annual growth rate of around 12.79%. The collaboration map depicted in Figure 5 prominently showcased the significant contributions made by prominent universities, with Nanyang Technological University and Beijing University taking the lead in research endeavors. Figure 6 provides an overview of the worldwide contributions to nanotechnology in EVs, with China, India and the United States as the primary countries leading in this field. The analysis of keywords ( Figure 7 ) revealed the consistent presence of terms such as self-powered sensors, batteries, triboelectric nanogenerator, nanotechnology, renewable energy, lithium-ion batteries, electrode material and electrochemical performance. These terms indicate the main themes of the research. Figures 8 and 9 depict the cooperative endeavors of several nations, with particular emphasis on China, India, the United States, South Korea, Germany, Japan, the United Kingdom, the Philippines, Singapore, France, Australia, Canada, Italy, Spain and Malaysia, which are recognized as significant contributors in this sector. Figures 10 and 11 visually depict keyword analysis, revealing the most common study topics and the network of writers who collaborated. A list of the 50 most significant keywords was compiled, which included terms such as ‘nanotechnology,’ ‘lithium,’ ‘electrodes,’ ‘electric batteries,’ ‘nanostructured materials,’ and ‘energy efficiency.’ The treemap structure ( Figure 10c ) displayed the distribution of keywords in publications by different authors, providing a detailed comprehension of the research landscape. To summarize, this SLR thoroughly examines nanomaterials in EVs, utilizing bibliometric methodologies to uncover patterns in research, collaborations and important theme areas. The results emphasize the increasing significance of nanotechnology in the progression of EV technologies. Research gaps may exist in comprehending the precise applications of nanomaterials, tackling issues related to scalability and cost-efficiency, and investigating potential environmental consequences. Potential areas for future research could prioritize interdisciplinary investigations, technological advancements and sustainable methodologies to facilitate the incorporation of nanomaterials into EV technologies. The findings obtained from this work provide valuable contributions to the wider discussion on the convergence of nanotechnology and electric cars, offering guidance for future research efforts in this rapidly changing and developing subject.

In conclusion, the bibliometric analysis conducted on nanotechnology in EVs provides valuable insights into this field’s research landscape and trends. The study highlights the growing interest and research output dedicated to nanotechnology’s applications in electric cars, showcasing its significance and potential impact. By examining publication trends, influential articles, collaborating institutions and key contributors, the analysis helps identify the key areas of research focus and the leading contributors in the field. This information can guide future research directions, collaboration opportunities and resource allocation. The analysis also highlights the challenges and opportunities associated with nanotechnology in EVs. It reveals the need to address scalability, cost-effectiveness, long-term stability and environmental impact to realize the potential of nanotechnology in this domain fully.

Moreover, the bibliometric analysis is a foundation for further exploration and understanding of nanotechnology in EVs. It provides a basis for researchers, policymakers and industry stakeholders to assess the progress made, identify knowledge gaps and develop strategies to accelerate the development and adoption of nanotechnology in EVs. Overall, the bibliometric analysis of nanotechnology in EVs is a valuable tool that enhances our understanding of the research landscape, highlights emerging trends and offers insights into the future perspectives of this exciting field. It paves the way for further research and collaboration, ultimately contributing to advancing and implementing nanotechnology in EVs for a greener and more sustainable transportation future.

The authors declare that there is no conflict of interest in this work.

The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University, Saudi Arabia, for funding this work through the Research Group Program under grant no. RGP 2/88/44.

Pulkit Kumar: Conceptualization, Methodology, Software, Writing – Original Draft, Data Curation, Writing – Review & Editing; Harpreet Kaur Channi: Conceptualization, Methodology, Supervision, Writing – Review & Editing, Formal analysis; Atul Babbar: Investigation, Supervision, Resources, Formal analysis; Raman Kumar: Investigation, Supervision, Conceptualization, Methodology, Formal analysis, Writing – Review & Editing; Javed Khan Bhutto: Formal analysis, Investigation, Supervision T.M. Yunus Khan: Investigation, Visualization; Abhijit Bhowmik: Formal analysis, Investigation, Supervision; Abdul Razak: Formal analysis, Investigation, Supervision; Anteneh Wogasso Wodajo: Formal analysis, Visualization, Supervision

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Royal Society of Chemistry

Nanotechnology from lab to industry – a look at current trends

ORCID logo

First published on 1st August 2022

Nanotechnology holds great promise and is hyped by many as the next industrial evolution. Medicine, food and cosmetics, agriculture and environmental health, and technology industries already profit from nanotechnology innovations and their influence is expected to increase drastically in the near future. However, there are also many challenges that need to be overcome to bring a nanotechnological product or business to the market. In this article we discuss current examples of nanotechnology that have been successfully introduced in the market and their relevance and geographical spread. We then discuss different partners for scientists and their role in the commercialization process. Finally, we review the different steps it takes to bring a nanotechnology to the market, highlight the many difficulties related to these steps, and provide a roadmap for the journey from lab to industry which can be beneficial to researchers.

1. Introduction

2. nanotechnology developments.

All inhabited continents are represented among the top countries involved in scientific publishing; however, only Europe (14 countries), Asia (8 countries), North America (2 countries), and Oceania (1 country) are included among the top 25 countries involved in the patenting of nanotechnology developments. Seventeen countries were common factors among both publishing and patenting discoveries. It is also noteworthy that the two countries that had the highest investments in scientific research (China and The USA) produced highest numbers of publications and patents, respectively. Patents can be used as technological indicators as they provide an insight into the research and development activities that are intended for commercial gain. 12 The transfer of these nanotechnology advancements to commercialized end products is however a major challenge that the scientific community faces. However, it has to be noted here that there are also quite some differences in culture when it comes to patenting. There are differences between countries in how buerocratic the patent process is. Additionally, there are differences in how much is patented at all. In some cultures, it might be more common to keep innovation a secret than to patent. There are also differences in how patents are made. In some places there is a high number of smaller patents while in others there are a few more elaborate ones.

2.1. Nanotechnology industries worldwide

3. the business of ‘lab-to-industry’, 3.1. ideation.

These two approaches show how innovation relies on technology seeds and market needs. One might ponder which of the two approaches is better. There are both merits and challenges associated with each approach. While each can lead to innovation, a pairing of the two is recommended. When closely integrated, the potential impact of the innovation increases. This synchronization of the ‘seed’ and ‘need’ approaches is called accelerated innovation. It enables the restructuring of research and development, and innovation processes to make new product development dramatically faster and less costly. 15 Furthermore, it also facilitates functional thinking and exaptation where the latter refers to the discovery of unintended functions for technologies. Altogether creating the ideal conditions for researchers to make radical innovations and bridge the gap between academia and industry.

3.2. Business model

Breakthrough technologies, especially those incorporating the use of nanotechnology, are intended to create value. Value is created via this technology when there is meaningful performance improvement or when the cost of solving problems is significantly reduced. There is however a major challenge for nanotechnology innovations in terms of a business model, and that is, the challenge of taking the product to customers. Several factors can influence this (for example, having limited resources) and for this reason, a go-to-market strategy is critical.

A joint-development partnership is an agreement between two organizations to develop a new product or service. It is a strategic alliance that serves to leverage the assets of each company to create a new offering for commercialization that would be difficult to achieve individually. This type of partnership is commonly used for product development or beta testing. Typically, these agreements are not binding and one party can quit at any time. Profits, access, expenses, and losses are usually shared between the companies. With this type of business partnership, it is important to have a close business relationship with the company before engaging in this agreement. As is the case with licensing arrangements, the most ideal joint-development partnership can be determined with the assistance of an attorney. Matters relating to the ownership and access to intellectual property, responsibilities, disengagement, and termination are some of the issues to be discussed with a suitable attorney before engaging a potential partner.

In partnerships, securing intellectual property early remains crucial. In an innovative nanotechnology business, the science underpinning the technology is critical and must be protected. This can be achieved by engaging an intellectual property counsel. The services of a corporate counsel should also be acquired early to ensure the start-up is properly incorporated. These parties should be appointed at the early stages as they help with structuring the company. The technology transfer process which is discussed in Section 3.3 helps to get these counsels on board.

There are some key players that are needed to guarantee a good business model and these are outlined in Fig. 4 . To assure a diversity of skills that are necessary for success, an often overlooked group of individuals is needed. This is a company board. This can include a board of advisors and a board of directors. The functions of these two bodies bear some similarities and differences. The board of advisors is composed of business professionals who fill skill and expertise gaps and can offer guidance to the management team. This can include matters concerning business performance, market trends, long-term goals of the company, and financing to name a few. While the additional skill set required in a science-based industry might be in business management, it is not unusual for additional technical expertise to be warranted. This can include the skills of fellow scientists who have had prior success in transitioning science to the marketplace. These scientists, when recruited, could form a scientific or technical advisory board. Regardless of the composition of the advisory board, their core function is to provide non-binding strategic advice. Their role is not fiduciary. This means that the team of experts and community leaders has no legal responsibility to the company. Their role however remains critical as they can compensate for some of the weaknesses within the management team and bring different opinions, perspectives, and experiences to the table. The board of advisors is particularly helpful for start-ups. A board of directors, on the other hand, is essentially a panel of people elected or appointed to represent shareholders. They oversee the activities of the company and have a fiduciary responsibility to represent and protect the members' or investors' interests in the company. The management team however reports to the board of directors. Larger companies that will require significant funding need a board of directors. Both the boards of advisors and directors can assist with strategic planning, the development of new ideas, improvement of management structure, improving company image and reputation, reassuring stakeholders and investors, and overall, help to ensure the success of the company.

The management team and the company board can together decide on the most suitable business model for the company. In making this decision, special focus should be placed on the model that will create and deliver great value to customers while simultaneously delivering great margins. The model should also hedge against customer dissatisfaction or dissonance and issues securing adequate funding. While the team is now multifaceted, additional support to make the right decisions that will position the company for success can be sought. This can be achieved using accelerators and incubators (which might be available within the university or municipality), government agencies such as the local chamber of commerce, and small business and technology development centers. Start-ups are generally encouraged to not employ at the early stages and to instead contract personnel for specific functions if necessary.

3.3. Technology transfer process

The efficiency of the transfer of nanotechnology innovations from the lab to the industry is dependent on the efficacy of the technology transfer process. Countries that invest in improving nanotechnology transfer policies and practices have greater nanotechnology outputs. This is evident in the United States where the National Nanotechnology Initiative (NNI) was developed. It is a collaboration of federal departments and agencies with interests in nanotechnology research, development, and commercialization. 17 Within the NNI are agencies such as the Nano manufacturing and Small Business Innovation Research (SBIR) programs, and the NNI's National Nanotechnology Coordination Office (NNCO) that are concerned with the transfer of newly developed nanotechnologies into products for commercial use. In Asia, there has been an increase in expenditure towards nanotechnology research and deliberate efforts to transfer research findings to industries. While the production of nanotechnology publications in China is higher than in other countries ( Fig. 2a ), the transfer of these technologies to industries is not equivalent. 18 The National Steering Committee for Nanoscience and Nanotechnology (NSCNN) was established to oversee and coordinate nanotechnology policies and programs in China. Some key members of this group include the Chinese Academy of Sciences (CAS), the National Natural Science Foundation of China (NSFC), the National Development and Reform Commission (NDRC), and the Chinese Academy of Engineering. These agencies are expected to impact the technology transfer process within the country.

The success of the transfer of technology in The United States reveals that more favorable environments for nanotechnology transfer need to be created globally. This will create a stronger ecosystem for nanotechnology research and innovation, and in turn, result in greater success in the use of intellectual property to facilitate the creation of start-ups formed from the ground up or through partnerships. Some nanotechnology and nano-engineering associations across the world that can be modelled in other countries to positively impact the transfer of technology are outlined in Table 2 . These associations were selected from the Nanotechnology 2020 Market Analysis. 9

3.4. Readiness for commercialization

Technology readiness evaluates the technology itself and seeks to determine if the technology will maintain itself in the market. This is usually determined by performing a technology readiness assessment (TRA). It is recommended that this TRA is done at several points during the ‘life cycle’ of the new technology or system. Possible components of this assessment include an evaluation of the conceptual design, a clear protocol to facilitate a decision from among several competing design options, and similarly, a defined approach to decide when to begin full-scale development. These decisions might be made by the research team or they can be more complex and warrant an external, independent peer-review process. 20 Market readiness assesses how marketable the technology is; that is, how well the technology will be accepted by the target market. This is generally done by examining whether the technology offers meaningful identifiable and quantifiable benefits, has distinct advantages over competing products, has access to a market of a suitable size that is defined and is growing (demand-based), has immediate market uses, and has feasible manufacturing requirements. 21

The commercialization readiness assessment also evaluates the readiness of the technology's business model. This is done to verify the stability and readiness of the foundation upon which the technology will be delivered. Within this component, parameters for assessment include determining whether prospective licensees are identified, if industry contacts are available, and if further development or patenting is possible based on the availability of financial support for the licensee. Additionally, anticipated future royalty revenue of the license, access to venture capital, a profitable investment, and availability of government support for additional development for innovations resulting from universities are also crucial. 22 The last key area is management readiness which assesses the readiness of the management team that is responsible for the technology. It addresses matters such as the ability of the inventor to champion the innovation as a team player, whether the inventor's expectations for success are realistic, if the inventor is recognized and reputable in the field, if commercialization skills such as sales and marketing skills are available, whether management capabilities are available, and also whether the inventor is the patent holder for innovations resulting from government labs. 23

A method of quantifying the judgments made for each criterion of the four areas of the Cloverleaf framework to determine the degree to which each condition is met was suggested. 19 If all components of the criteria list for the four ‘leaves’ assessing readiness are satisfied, then the technology is ready for commercialization. If a partnership agreement is being utilized, some components should be completed before engaging a partner and others should be finalized with the partner. Regardless of the business model, if any area is found lacking, additional preparation is warranted to ensure the success of the venture when it enters the market.

Alternative to the Cloverleaf framework is the Technology Readiness Levels (TRL) model. This was developed by NASA and is a type of measurement system that is used to permit more effective assessment and communication regarding the maturity of new technologies. 20 The different levels of the framework are outlined in Fig. 6 . There are nine technology readiness levels. A project is evaluated against the parameters for each technology level and is then assigned a TRL rating based on its progress. TRL 1 is the lowest level and indicates that a technology requires further research and development, and testing. TRL 9 is the highest level and signifies a mature technology that is proven to work and may be put into use and commercialized.

3.5. Financials

Another type of capital provider is venture capitalists. These private investors provide funds to early-stage companies that are pursuing big opportunities with high growth potential. Venture capital firms exchange capital for equity ownership and can also provide strategic assistance, and an invaluable network. To capture the interest of a venture capitalist, a start-up should have a good “elevator pitch” and a strong investor pitch deck for their innovative product. This should therefore include the strength of the management team and clearly outline the large potential market for the nanotechnology innovation, and a unique product or service with a strong competitive advantage. Another entity that can provide financing and has a similar structure to a venture capital firm is a family office. This is a special investment firm that manages the wealth owned by individuals and families with a high net worth. 26 Family offices make optimal investors and are increasingly entering venture investment as a relatively new capital provider. They are comprised of qualified professionals with extensive experience and tend to offer more patient capital and expect lower returns than traditional investors.

4. The challenge of moving technology from lab to industry

Biological or environmental challenges are other factors that can impede the transfer of nanotechnology from the lab to the industry. Biological challenges include insufficient knowledge involving the interaction of nanomaterials in vitro and in vivo , inadequate information on their bioaccumulation in target organs, tissues, and cells, and also limited information on their biocompatibility. 30,31 Physical properties such as particle size, composition, surface area, surface charge, surface chemistry, and agglomeration state all influence the biocompatibility of nanomaterials and so more information is needed on their safety in vivo . 31 Environmental challenges include nanomaterials entering the environment either directly or indirectly (for example, via landfills). Nanomaterials can have potentially adverse effects on natural systems and can enter the environment at different stages of their life cycle. Three emission scenarios that are generally of relevance are (i) release during the production of various nanotechnology products or nano-enabled products; (ii) release during use; and (iii) release after disposal. 32 While present in the environment, nanomaterials can then undergo many transformations. These include chemical transformations (for example, photo-degradation), physical transformations (such as aggregation), biologically-mediated transformations (for instance, redox reactions in biological systems), and interactions with macromolecules (for example, flocculation). 30 The interplay between these transformations and the transport of the nanomaterial within the ecosystem ultimately determine their fate and ecotoxicity.

Possible biological and environmental impacts of nanotechnology innovations should be determined with in vitro and in vivo models, as well as within aquatic and terrestrial ecosystems. The production process from which the nanomaterial results should also be considered so that any such material emitted during this time or released from nano-enabled devices during their fabrication, use, recycling or disposal can be studied and minimized. Biological and environmental challenges can also be mitigated by providing employers and the extended workforce with information on the potential toxicity of nanomaterials at different stages of their life cycle. With the help of modelling, recent developments have been geared towards predicting the fate, behavior, and concentration of nanomaterials in the environment. 33 While these simulations can be helpful, more efficient and reliable analytical instruments and methods must be developed so that nanomaterials can be satisfactorily characterized and quantified, and the necessary tools developed to detect, monitor and track them in biological media and complex environmental matrixes.

The nanotechnology industry plays a major role in economic development; however, several economic challenges can hinder the transfer of innovations from the lab to the industry. Generally, these include limited investment in relevant research and development activities and a lack of appropriate mechanisms to secure these investments, lack of laboratory equipment and appropriate infrastructure to facilitate research and its commercialization, and insufficient funding opportunities to engage in research that has the potential for commercialization. Constraints imposed on the activities needed to commercialize nanotechnology outputs are also impacted by the socio-economic dynamics of innovation. While many believe the rapid growth in nanotechnology will have significant economic benefits, some advocate to reduce or halt its development. The backlash against nanotechnology by this group is based on the belief that it will exacerbate problems concerning existing socio-economic inequity and power imbalance caused by inequality. This, they suggest, will cause a nano-divide which refers to differing access to nanotechnology between low-, middle-, and high-income countries. 34,35 The ethical criticism is mainly concerned with inequity based on where knowledge is developed and retained and a country's capacity to engage in these processes. 35 An attempt to combat these challenges is outlined in the European Union's Framework Programs through the Responsible Research and Innovation (RRI) approach. This approach ‘anticipates and assesses potential implications and societal expectations concerning research and innovation, intending to foster the design of inclusive and sustainable research and innovation’ (https://ec.europa.eu). These measures which are intended to facilitate broader access to nano-technology and its innovations globally are critical in addressing a nano-divide.

The final category of challenges that can significantly impact the transfer of nanotechnology from the lab to the industry is regulatory challenges. These are concerned with a lack of clear regulatory guidelines for nanotechnology and nanotechnology-enabled products. Some regulatory challenges include inadequate policies to foster the development and operation of nanotechnology businesses or insufficient strategies implemented by governments to attract nanotechnology business initiatives. Additionally, a lack of technology transfer protocols, or requisites for regulatory approvals to facilitate the movement of innovation from the lab to commercial products are problematic. 36 The multidisciplinary nature of nanotechnology also presents regulatory challenges. With its cross-industry applications, policing and enforcement nanotechnology patents have proven to be prohibitively expensive (WIPO, 2011). New intellectual property practices and protocols are therefore required to simplify the pathway from lab to industry thereby reducing time and expense.

The technical, biological, environmental, economic, and regulatory challenges of nanotechnology need to be addressed urgently. Policies governing all aspects of nanotechnology research and subsequent commercialization must balance its potential benefits with its current challenges. Combatting these challenges will require considerable efforts to prevent any possible harmful effects of nanotechnology while also facilitating the awareness of its benefits to society. 37 The involvement of scientific, governmental, industry, and labor force representatives is therefore critical in decision making so the challenges associated with the commercialization of nanotechnology can be controlled, minimized or mitigated.

5. Conclusions

The necessary risk assessment to understand the potentially harmful effects of products resulting from nanotechnology have however not kept pace with their proliferation; and researchers are racing to address this knowledge gap. 38 Companies resulting from the transfer of nanotechnology innovations from the lab to the marketplace must therefore have rigorous risk management protocols where risks are identified, control measures are planned and implemented, and risks communication. 37 Identified regulatory impediments should also be addressed and technology transfer policies and practices implemented. Entrepreneurial education and training, and the establishment of business incubators should also be supported within the necessary departments or research institutes. Improvement in the understanding of nanotechnology within society would also help commercialization efforts. Overall, societal actors such as researchers, policymakers, investors, citizens etc. must work together during the research and commercialization stages so that the many benefits of nanotechnology outputs can be aligned with the needs and expectations of society.

Conflicts of interest

Notes and references.

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Mini review article, nanoremediation: nanomaterials and nanotechnologies for environmental cleanup.


  • 1 Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Campus Ciudad de México, Ciudad de México, Mexico
  • 2 Departamento de Física, Facultad de Ciencias, Universidad Nacional Autónoma de México, Ciudad de México, Mexico
  • 3 Laboratorio de Medicina Genómica, Departamento de Genómica, Instituto Nacional de Rehabilitación Luis Guillermo Ibarra Ibarra, Ciudad de México, Mexico
  • 4 Departamento de Farmacia, Facultad de Química, Universidad Nacional Autónoma de México, Ciudad de México, Mexico

Different global events such as industrial development and the population increment have triggered the presence and persistence of several organic and inorganic contaminants, representing a risk for the environment and human health. Consequently, the search and application of novel technologies for alleviating the challenge of environmental pollution are urgent. Nanotechnology is an emerging science that could be employed in different fields. In particular, Nanoremediation is a promising strategy defined as the engineered materials employed to clean up the environment, is an effective, rapid, and efficient technology to deal with persistent compounds such as pesticides, chlorinated solvents, halogenated chemicals, or heavy metals. Furthermore, nanoremediation is a sustainable alternative to eliminate emerging pollutants such as pharmaceutics or personal care products. Due to the variety of nanomaterials and their versatility, they could be employed in water, soil, or air media. This review provides an overview of the application of nanomaterials for media remediation. It analyzes the state of the art of different nanomaterials such as metal, carbon, polymer, and silica employed for water, soil, and air remediation.


Contaminated water, soil, and air represent a critical world problem involving extreme environmental and human health risks. Several developed techniques for remediation include conventional methods such as thermal treatment, pump-and-treat, chemical oxidation, and emerging technologies such as “nanoremediation” ( Ganie et al., 2021 ; Mukhopadhyay et al., 2021 ). Nanoremediation uses engineered nanomaterials to clean up polluted media, and this technique is less costly and more effective than most typical methods.

In addition to its cost-effectiveness, the interest in applying nanomaterials for environmental remediation relies on the nanostructure’s characteristics. Nanoparticles (NPs) present sensitivity, high surface-area to mass ratio, exceptional electronic properties, and catalytic behavior ( Corsi et al., 2018 ). Catalysis and chemical reduction can be regarded as the primary mechanisms for remediation by NPs. Moreover, NPs have been employed in the removal process based on adsorption because NPs present a random distribution of active sites in their high surface area and a wide possibility of coating modifications ( Guerra et al., 2018 ). In addition, NPs can diffuse in small spaces, enhancing their application in soil and water remediation. Also, membranes based on nanomaterials have been used in water nanofiltration (NF) since the membrane pores potentially retain big components in water effluents. Moreover, the interaction with the membrane selectively separates the more minor compounds. Nanomaterials employed for water, soil, and air remediation include metal oxides, carbon nanotubes, quantum dots, and biopolymers.

This review aims to discuss the applications of different types of nanomaterials in the context of water, soil, and air treatment, presenting current studies and approaches related to nanotechnology application for environmental remediation.

Nanoremediation of Water

Over the last decade, the study of nanomaterials for application in water and wastewater treatment has been widely spread ( Figure 1 ). As clean water is fundamental for living organisms to sustain life, contaminated groundwater is a problem that concerns environmental researchers due to the extreme risks that it represents to different ecosystems ( Schweitzer and Noblet 2018 ). Water sources are susceptible to pollution by many ions, heavy metals, petroleum hydrocarbons, pesticides, radioactive materials, as well as emerging pollutants such as pharmaceutics and personal care products ( Jadhav et al., 2015 ; Zamora-Ledezma et al., 2021 ).


FIGURE 1 . Different types of nanomaterials are employed for nanoremediation.

In this context, research and development of efficient methods for water remediation are imperative. In recent years, different technologies based on nanomaterials have been employed in the remediation of water due to their properties, including the selectivity to certain pollutants and their absorption capacity ( Table 1 ). The predominant nanomaterials employed in water remediation are metallic nanoparticles, biopolymeric membranes, and carbon-derived materials ( Saikia et al., 2019 ).


TABLE 1 . Advantages of Nanomaterials employed in media remediation.

Metal and Metal-Based Nanomaterials

Several types of metal oxide nanoparticles such as iron oxide (Fe 2 O 3 /Fe 3 O 4 ), zinc oxide (ZnO), and titanium dioxide (TiO 2 ) are utilized for water purification due to their high reactivity, photolytic characteristics as well as adsorbent properties derived from their massive surface area and affinity to different chemical groups ( Aragaw et al., 2021 ). For instance, iron nanoparticles have been employed to treat dyes in wastewater from textile, paint, and paper industries due to their stability in suspension medium and high adsorption capacity. In recent years, these NPs have been highly efficient in the adsorption of dyes such as methyl orange and methylene blue, two of the most utilized dyes in industry, which present the most inharmonious effects on the environment and human health ( Mashkoor and Nasar 2020 ). In this context, the methyl orange and phenol removal efficiency of magnetic iron oxide NPs in combination with carbon has been examined, revealing that the nanocomposites present stronger interactions with the dye, being the carbon concentration a decisive parameter in the NPs adsorbent behavior ( Istratie et al., 2019 ). Besides dyes, heavy metals like chromium (VI) are another critical type of pollutants in water. Current researches suggested that the environmental risk by chromium (VI) could be lessened by the presence of iron oxide or zero-valent iron NPs and organic acids (such as citric acid) ( Yang et al., 2017 ; Zhou et al., 2018 ). Titanium dioxide NPs are widely employed as photocatalyst for micropollutants removal in water, and it is an effective alternative for emerging contaminants such as pharmaceutics ( Mahmoud et al., 2017 ).

Carbon-Based Nanomaterials

Nanoporous carbon-based materials such as activated carbons, carbon nanotubes (CNTs), including multi-walled nanotubes (MWCNTs) and single-walled nanotubes (SWCNTs), and graphene and its oxide, present physicochemical characteristics that make them suitable for water treatment operations to remove contaminants like heavy metals, fluorides, textile dyes or pharmaceutical products. For instance, a study evaluated the adsorption of hexavalent chromium by MWCNTs in contaminated groundwater ( Mpouras et al., 2021 ). The authors analyzed the adsorption efficiency effect of parameters such as pH and adsorbent concentration. Their results suggested that at pH values higher than 7, the adsorption decreased. MWCNTs have also been applied in water gasoline removal projects ( Lico et al., 2019 ). Due to the great environmental concern that represents fluoride, different alternatives based on carbon have been employed to achieve deflouridation of wastewater. In this context, there are reports of the fluoride removal capacity of chemical and bio-reduced graphene oxide, exposing that the first one presented an 87% of reduction; meanwhile, the bio-reduced presented 94% of capacity ( Roy et al., 2017 ). Similarly, activated carbon has been widely explored in removing pharmaceutical products due to their low cost, large pore size, and high porosity. For instance, the comparison of carbamazepine and sildenafil citrate adsorption onto powdered activated carbon and granular activated carbon was reported in 2019 ( Delgado et al., 2019 ). The results revealed that approximately 90% of the compounds were removed in 10 h using powdered activated carbon, whereas the granular activated carbon achieved just 40% of removal after 70 h, which is related to the greater surface area of the powdered. Likewise, the evaluation of caffeine, ibuprofen, and triclosan adsorption employing powdered activated carbon was reported, observing an important effect of pH ( Kaur et al., 2018 ).

Polymer-Based Nanomaterials

Different alternatives based on polymer nanotechnology could be employed in water treatment, such as nanoparticles, nanocomposites, or NF membranes ( Abdelbasir and Shalan 2019 ; Bassyouni et al., 2019 ). Particularly, polymeric nanomembranes are employed to eliminate unwanted nanoparticles in the aqueous phase by detouring particles in the membrane pores and by the chemical interaction between the pollutants and the membranes, provoking the pollutant’s immobilization. In this context, chitosan is a widely employed polymer for NF membranes elaboration based on facile manufacturing techniques such as solvent casting. These membranes are a strategy to clean textile wastewater ( Long et al., 2020 ), revealing a lower rejection to electroneutral and negatively charged dyes than the positively charged. However, the dyes’ physical size also plays a key role in NF efficiency ( Weng et al., 2017 ). The stability and effectiveness of these nanofiltration membranes could be enhanced using the membranes as matrix or support to other types of materials, constituting a composite. Recently, synthetic and natural polymers such as polyamide, cellulose, and chitosan have been employed as membrane matrices and modified by different components such as triethanolamine, metal oxide nanoparticles, and carbon nanotubes ( Yan et al., 2016 ; Lakhotia et al., 2018 ). For example, it has been reported that by employing carboxylated MWCNTs in polyamide membranes, an increment in salt rejection rate can be observed, which is very useful to remove the industrial salts from textile effluents ( Al-Hobaib et al., 2017 ). In addition, polyethersulfone membranes functionalized with MWCNTs, graphene, or other polymers exhibited excellent heavy metals and dyes rejection in aqueous media ( Vatsha et al., 2014 ; Ma et al., 2017 ; Peydayesh et al., 2020 ).

Nanoremediation of Soil

The settlement of Homo sapiens during the transition from hunter-gatherer to farmer resulted in an irreversible impact on nature. The dominance of the wheat business, first as a form of subsistence, later as a style of economic exchange, had consequences in the disappearance of animal species, plants, diversion of river courses, and soil erosion and contamination. Subsequently, the appearance and increase of industrialization and excessive urbanization have accelerated the deterioration and contamination of soil ( Kumar et al., 2021 ). Recently, the use of nanomaterials for the remediation of soil has been attractive due to its high reactivity, high surface-to-volume ratio, surface functionalization, and modification of physical properties such as size, morphology, porosity, and chemical composition. The set of these properties allows the selectivity and efficiency in the capture of pollutants. The intercalation of nanoparticles in the soil allows the cleaning of extensive areas and reduces costs and time due to the application in situ . Nanoremediation for soil contamination has predominated with metallic and magnetic nanoparticles, carbon nanotubes, and nanoscale zero-valent iron ( Mukhopadhyay et al., 2021 ).

Nanoscale zero-valent iron (nZVI) is an electron donor with a negative reduction potential. The use of nZVI is one of the most frequent in pilot trials ( Cheng et al., 2021 ) because it allows the removal of chlorinated organic solvents, polychlorinated biphenyls, and organochlorine pesticides through oxidation-reduction transformation strategies sequestration ( Stefaniuk et al., 2016 ). nZVI has also been shown to be effective in the remediation of trichloroethene, hexavalent chromium, nitrate, lead, cadmium, and DDT with high cleaning percentages ( Guerra et al., 2018 ). There are different nZVI synthesis methods such as carbothermal reduction, ultrasound-assisted, electrochemical, and green synthesis. Although nZVI possesses reactivity as a reducing agent, it lacks agglomeration dispersion stability, difficulty separating it from the remediated soil, and limited mobility. Modifications to the surface are a technological option to preserve its function, and the most frequent strategies include mixing with other noble metals in the form of an alloy such as Pd, Pt, Ag, Cu, and Ni. Other strategies include coating the surface with biopolymers like starch, carboxymethyl cellulose, guar gum, or synthetic polymers like poly (ethylene glycol). While the incorporation of nZVI on the surface of supports such as silica, activated carbon, zeolites, or polymer membranes facilitates the separation of the nanomaterial from the purified soil. Additionally, nZVI can be immobilized utilizing a “trapping” strategy in emulsions or dispersions of particles in biopolymers such as calcium alginate, chitosan, and gum arabic. Other metal-based nanomaterials include applying SiO 2 , Al 2 O 3 , TiO 2 , iron phosphate, goethite, and magnetic nanoparticles ( Stefaniuk et al., 2016 ).

Carbonaceous nanomaterials exhibit unique characteristics such as large surface area, high microporosity, excellent sorption capacities, and eco-friendly nature. Some architectures embrace fullerene C 60 , fullerene C 540 , SWCNTs, MWCNTs, graphene, and activated carbon nanoparticles ( Matos et al., 2017 ; Marcon et al., 2021 ). Moreover, activation or functionalization of carbon-based nanomaterials represents additional advantages as in other environmental remediation applications. Recently, there has been a greater preference for CNTs because they offer greater adsorption capacity than graphene, graphene oxides, biochar, and granular activated carbon. The adsorption is determined by the exposure area and functional groups on the surface, such as -COOH and -OH. The adsorption capacity can be increased by coupling functional groups such as -NH 2 , -SH, oxidation processes, nonmagnetic metal oxide coating, and grafting of magnetic iron oxides. The increase in surface area, high surface-to-volume ratio, and therefore its high reactivity favor flocculation and decrease its properties for nanoremediation. The use of the surfactant poloxamer 407 has allowed an adequate stabilization of multi-walls carbon nanotubes ( Matos et al., 2017 ). CNTs can remove heavy metal ions such as Pb 2+ , Cu 2+ , Ni 2+ , and ZN 2+ ; however, the immobilization of heavy metals depends on pH, organic matter content, and the presence of silt and clay particles. CNTs can also remediate the soil of total petroleum hydrocarbons, crude oil, Cr (VI), Cd, DDT, hexachlorocyclohexane, increasing the microbial population and plant growth ( Shan et al., 2015 ). CNTs application techniques comprise their incorporation into membrane filtration, separation columns, and an aqueous dispersion.

Nanoremediation of Gas Phase

Air pollution is one of the most significant problems that the world is facing this century since it impacts climate change and public health. The six most common and harmful outdoor air pollutants include particle matter (PM10 and PM2.5), nitrogen oxides (NOx), sulfur dioxide (SO 2 ), carbon monoxide (CO), lead, and ground-level ozone, which is formed by chemical reactions between NOx and volatile organic compounds (VOCs) ( Manisalidis et al., 2020 ). NOx, SOx, VOCs, and ammonia (NH 3 ) are considered secondary particulate matter precursors. Carbon dioxide (CO 2 ) is not a pollutant; however, it is the most important greenhouse gas emitted through human activities. In order to overcome this problem, several options have been investigated, including the use of graphene oxides (GOs), graphite oxides and CNTs with highly reactive surface sites, and mesoporous silica materials with ordered and tunable porous structure, high surface area, large pore volume and thermal stability ( Guerra et al., 2018 ).

Carbon-Based Materials

The benefits of nanotechnology in air pollution control are remediation and treatment, pollution prevention, and detection and sensing. The surface of graphite oxide is rich in oxygen-containing functional groups, which can be controlled by changing the reaction temperature with the addition of water ( Luo et al., 2018 ). This material has been used for ammonia gas sensors operating at different temperatures ( Bannov et al., 2017 ; Luo et al., 2018 ). Carbon-based nanomaterials also offer the possibility of combining other types of nanomaterials to form nanocomposites, merging different properties in a single new material ( Scida et al., 2011 ). GO, and zirconium hydroxide/graphene composites ( Seredych and Bandosz 2010 ; Babu et al., 2016 ) have been applied as an environmental remediation tool through the adsorption of SO 2 . GO was also partially reduced via photoreduction under ultraviolet light irradiation and used as a photocatalyst to degrade VOCs ( Tai et al., 2019 ). Furthermore, a GO membrane with a large specific surface area and a continuous pore structure was used to capture PM2.5 ( Jung et al., 2018 ; Zou et al., 2019 ). There have been numerous studies on CNTs in order to enhance their adsorption properties. CNTs typically must be modified or coated with other reactive materials having appropriate functional groups or charges ( Guerra et al., 2018 ). Modified MWCNTs or SWCNTs have been utilized to detect H 2 S and SO 2 ( Zhang et al., 2012 ), CO and NH 3 ( Dong et al., 2013 ), NO 2 and NH 3 ( Kim et al., 2016 ), NO 2 ( Park et al., 2019 ), VOCs ( Amade et al., 2014 ), NO x and CO 2 ( Su et al., 2009 ).

Silica-Based Materials

Silica-based nanomaterials exhibit high versatility because of their numerous advantageous properties, including wide surface area, adjustable pore size, and easily adaptable surfaces ( Shukla et al., 2020 ). Furthermore, the ability of these nanomaterials for catalysis and adsorption has led to a growing interest in recent years for the remediation of polluted air and the elimination of contaminants in the gas phase ( Guerra et al., 2018 ). The superficial modification of silica nanomaterials may enhance their physicochemical properties. For example, incorporating hydroxyl groups on the surface of the silica nanomaterials may facilitate some surface phenomena, including gas adsorption and wetting. This approach is effective in designing novel catalysts and adsorbents. One of the first studies analyzing the adsorbent capability of modified mesoporous silica demonstrated that the existence of amine groups on its surface promotes the effective capture of H 2 S and CO 2 from natural gas ( Huang et al., 2003 ). According to the authors, the material quickly removed up to 80% of the total H 2 S (35 min) and CO 2 (30 min); thus, that material is highly efficient in removing those gases. Similarly, another report revealed that aminosilicates have the potential to eliminate CO 2 from ambient air, which suggests that these materials may help mitigate climate changes ( Choi et al., 2011 ). In addition to CO 2 , these amine-modified silicates also effectively eliminate other organic contaminants such as aldehydes and ketones ( Nomura and Jones 2013 , 2014 ). Thus, they could be applicable for removing pollutants in an industrial environment. On the other hand, atmospheric contamination by lead (Pb) is an emerging environmental and health problem worldwide, and eliminating Pb from the air represents a challenging question. Concerning this, Yang et al. ( Yang et al., 2013 ) developed silica nanoparticles to tackle this environmental problem. The results demonstrated that their silica nanoparticles could remove atmospheric Pb in polluted air. Therefore, silica-based nanoparticles might represent attractive environmental agents against industrial pollution by Pb and other heavy metals.

The high surface-to-volume ratio is the basic strategy offered by nanomaterials to adsorb contaminants. However, the increase in surface area is one of the main disadvantages of nanomaterials, and therefore, the appearance of the flocculation phenomenon and possible particle coalescence. Therefore, a challenge is to find the balance between physical stability and adequate surface activity that favors interaction with pollutants. While stabilization with non-ionic surfactants allows a decrease in flocculation, possibly the addition of functional groups to increase the removal of pollutants such as -COOH and -NH 2 can prevent the agglomeration of nanoparticles under specific pH conditions through a simultaneous mechanism of repulsion of electrical charges. With this proposal, the use of non-ionic surfactants would not be necessary. In addition, another challenge is the complexity of the different media. The formation of a corona can overshadow sophisticated nanomaterials on the nanoparticle’s surface with ligands from the contaminated medium; therefore, nanoremediation may be favored with previous cleaning steps.

Author Contributions

Conceptualization, GL-G, JM and MDP-A; investigation, LE-G, JR-G, IGK, and GL-G; writing—original draft preparation, LE-G, JR-G, MDP-A, and GL-G; writing—review and editing, IGK, MDP-A, and GL-G; visualization, GL-G; supervision, MDP-A, and GL-G; project administration, MDP-A, JM and GL-G.

This research was funded by CONACYT A1-S-15759 to Gerardo Leyva-Gómez and Fundación Miguel Alemán Valdés grant to JM.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: nanoremediation, nanomaterials, bioremediation, nanotechnology, environmental ecotoxicity

Citation: Del Prado-Audelo ML, García Kerdan I, Escutia-Guadarrama L, Reyna-González JM, Magaña JJ and Leyva-Gómez G (2021) Nanoremediation: Nanomaterials and Nanotechnologies for Environmental Cleanup. Front. Environ. Sci. 9:793765. doi: 10.3389/fenvs.2021.793765

Received: 12 October 2021; Accepted: 30 November 2021; Published: 24 December 2021.

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Copyright © 2021 Del Prado-Audelo, García Kerdan, Escutia-Guadarrama, Reyna-González, Magaña and Leyva-Gómez. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: G. Leyva-Gómez, [email protected] ; J. J. Magaña, [email protected]

A bibliometric analysis of the role of nanotechnology in dark fermentative biohydrogen production

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  • Published: 26 March 2024

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  • Fakiha Tul Jannat 1 ,
  • Kiran Aftab   ORCID: orcid.org/0000-0003-4180-8623 1 ,
  • Umme Kalsoom 2 &
  • Muhammad Ali Baig 3  

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Recently, nanoparticles have drawn a lot of interest as catalysts to enhance the effectiveness and output of biohydrogen generation processes. This review article provides a comprehensive bibliometric analysis of the significance of nanotechnology in dark fermentative biohydrogen production. The study examines the scientific literature from the database of The Web of Science© while the bibliometric investigation utilized VOSviewer© and Bibliometrix software tools to conduct the analysis. The findings revealed that a total of 232 articles focused on studying dark fermentation for hydrogen production throughout the entire duration. The extracted data was used to analyze publication trends, authorship patterns, and geographic distribution along with types and effects of nanoparticles on the microbial community responsible for dark fermentative biohydrogen production. The findings of this bibliometric analysis provide valuable insights into the advancements and achievements in the utilization of nanoparticles in the dark fermentation process used to produce biohydrogen.

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Jannat, F.T., Aftab, K., Kalsoom, U. et al. A bibliometric analysis of the role of nanotechnology in dark fermentative biohydrogen production. Environ Sci Pollut Res (2024). https://doi.org/10.1007/s11356-024-33005-6

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Computer Science > Computer Vision and Pattern Recognition

Title: sv3d: novel multi-view synthesis and 3d generation from a single image using latent video diffusion.

Abstract: We present Stable Video 3D (SV3D) -- a latent video diffusion model for high-resolution, image-to-multi-view generation of orbital videos around a 3D object. Recent work on 3D generation propose techniques to adapt 2D generative models for novel view synthesis (NVS) and 3D optimization. However, these methods have several disadvantages due to either limited views or inconsistent NVS, thereby affecting the performance of 3D object generation. In this work, we propose SV3D that adapts image-to-video diffusion model for novel multi-view synthesis and 3D generation, thereby leveraging the generalization and multi-view consistency of the video models, while further adding explicit camera control for NVS. We also propose improved 3D optimization techniques to use SV3D and its NVS outputs for image-to-3D generation. Extensive experimental results on multiple datasets with 2D and 3D metrics as well as user study demonstrate SV3D's state-of-the-art performance on NVS as well as 3D reconstruction compared to prior works.

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  • Quantum Research

Landmark IBM error correction paper published on the cover of Nature

Ibm has created a quantum error-correcting code about 10 times more efficient than prior methods — a milestone in quantum computing research..

Landmark IBM error correction paper published on the cover of Nature

27 Mar 2024

Rafi Letzter

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Today, the paper detailing those results was published as the cover story of the scientific journal Nature. 1

Last year, we demonstrated that quantum computers had entered the era of utility , where they are now capable of running quantum circuits better than classical computers can. Over the next few years, we expect to find speedups over classical computing and extract business value from these systems. But there are also algorithms with mathematically proven speedups over leading classical methods that require tuning quantum circuits with hundreds of millions, to billions, of gates. Expanding our quantum computing toolkit to include those algorithms requires us to find a way to compute that corrects the errors inherent to quantum systems — what we call quantum error correction.

Read how a paper from IBM and UC Berkeley shows a path toward useful quantum computing

Quantum error correction requires that we encode quantum information into more qubits than we would otherwise need. However, achieving quantum error correction in a scalable and fault-tolerant way has, to this point, been out of reach without considering scales of one million or more physical qubits. Our new result published today greatly reduces that overhead, and shows that error correction is within reach.

While quantum error correction theory dates back three decades, theoretical error correction techniques capable of running valuable quantum circuits on real hardware have been too impractical to deploy on quantum system. In our new paper, we introduce a new code, which we call the gross code , that overcomes that limitation.

This code is part of our broader strategy to bring useful quantum computing to the world.

While error correction is not a solved problem, this new code makes clear the path toward running quantum circuits with a billion gates or more on our superconducting transmon qubit hardware.

What is error correction?

Quantum information is fragile and susceptible to noise — environmental noise, noise from the control electronics, hardware imperfections, state preparation and measurement errors, and more. In order to run quantum circuits with millions to billions of gates, quantum error correction will be required.

Error correction works by building redundancy into quantum circuits. Many qubits work together to protect a piece of quantum information that a single qubit might lose to errors and noise.

On classical computers, the concept of redundancy is pretty straightforward. Classical error correction involves storing the same piece of information across multiple bits. Instead of storing a 1 as a 1 or a 0 as a 0, the computer might record 11111 or 00000. That way, if an error flips a minority of bits, the computer can treat 11001 as 1, or 10001 as 0. It’s fairly easy to build in more redundancy as needed to introduce finer error correction.

Things are more complicated on quantum computers. Quantum information cannot be copied and pasted like classical information, and the information stored in quantum bits is more complicated than classical data. And of course, qubits can decohere quickly, forgetting their stored information.

Research has shown that quantum fault tolerance is possible, and there are many error correcting schemes on the books. The most popular one is called the “surface code,” where qubits are arranged on a two-dimensional lattice and units of information are encoded into sub-units of the lattice.

But these schemes have problems.

First, they only work if the hardware’s error rates are better than some threshold determined by the specific scheme and the properties of the noise itself — and beating those thresholds can be a challenge.

Second, many of those schemes scale inefficiently — as you build larger quantum computers, the number of extra qubits needed for error correction far outpaces the number of qubits the code can store.

At practical code sizes where many errors can be corrected, the surface code uses hundreds of physical qubits per encoded qubit worth of quantum information, or more. So, while the surface code is useful for benchmarking and learning about error correction, it’s probably not the end of the story for fault-tolerant quantum computers.

Exploring “good” codes

The field of error correction buzzed with excitement in 2022 when Pavel Panteleev and Gleb Kalachev at Moscow State University published a landmark paper proving that there exist asymptotically good codes — codes where the number of extra qubits needed levels off as the quality of the code increases.

This has spurred a lot of new work in error correction, especially in the same family of codes that the surface code hails from, called quantum low-density parity check, or qLDPC codes. These qLDPC codes are quantum error correcting codes where the operations responsible for checking whether or not an error has occurred only have to act on a few qubits, and each qubit only has to participate in a few checks.

But this work was highly theoretical, focused on proving the possibility of this kind of error correction. It didn’t take into account the real constraints of building quantum computers. Most importantly, some qLDPC codes would require many qubits in a system to be physically linked to high numbers of other qubits. In practice, that would require quantum processors folded in on themselves in psychedelic hyper-dimensional origami, or entombed in wildly complex rats’ nests of wires.

In our paper, we looked for fault-tolerant quantum memory with a low qubit overhead, high error threshold, and a large code distance.

High-threshold and low-overhead fault-tolerant quantum memory

Bravyi, S., Cross, A., Gambetta, J., et al. High-threshold and low-overhead fault-tolerant quantum memory. Nature (2024). https://doi.org/10.1038/s41586-024-07107-7

In our Nature paper, we specifically looked for fault-tolerant quantum memory with a low qubit overhead, high error threshold, and a large code distance.

Let’s break that down:

Fault-tolerant: The circuits used to detect errors won't spread those errors around too badly in the process, and they can be corrected faster than they occur

Quantum memory: In this paper, we are only encoding and storing quantum information. We are not yet doing calculations on the encoded quantum information.

High error threshold: The higher the threshold, the higher amount of hardware errors the code will allow while still being fault tolerant. We were looking for a code that allowed us to operate the memory reliably at physical error rates as high as 0.001, so we wanted a threshold close to 1 percent.

Large code distance: Distance is the measure of how robust the code is — how many errors it takes to completely flip the value from 0 to 1 and vice versa. In the case of 00000 and 11111, the distance is 5. We wanted one with a large code distance that corrects more than just a couple errors. Large-distance codes can suppress noise by orders of magnitude even if the hardware quality is only marginally better than the code threshold. In contrast, codes with a small distance become useful only if the hardware quality is significantly better than the code threshold.

Low qubit overhead: Overhead is the number of extra qubits required for correcting errors. We want the number of qubits required to do error correction to be far less than we need for a surface code of the same quality, or distance.

We’re excited to report that our team’s mathematical analysis found concrete examples of qLDPC codes that met all of these required conditions. These fall into a family of codes called “Bivariate Bicycle (BB)” codes. And they are going to shape not only our research going forward, but how we architect physical quantum systems.

The gross code

While many qLDPC code families show great promise for advancing error correction theory, most aren’t necessarily pragmatic for real-world application. Our new codes lend themselves better to practical implementation because each qubit needs only to connect to six others, and the connections can be routed on just two layers.

To get an idea of how the qubits are connected, imagine they are put onto a square grid, like a piece of graph paper. Curl up this piece of graph paper so that it forms a tube, and connect the ends of the tube to make a donut. On this donut, each qubit is connected to its four neighbors and two qubits that are farther away on the surface of the donut. No more connections needed.

The good news is we don’t actually have to embed our qubits onto a donut to make these codes work — we can accomplish this by folding the surface differently and adding a few other long-range connectors to satisfy mathematical requirements of the code. It’s an engineering challenge, but much more feasible than a hyper-dimensional shape.

We explored some codes that have this architecture and focused on a particular [[144,12,12]] code. We call this code the gross code because 144 is a gross (or a dozen dozen). It requires 144 qubits to store data — but in our specific implementation, it also uses another 144 qubits to check for errors, so this instance of the code uses 288 qubits. It stores 12 logical qubits well enough that fewer than 12 errors can be detected. Thus: [[144,12,12]].

Using the gross code, you can protect 12 logical qubits for roughly a million cycles of error checks using 288 qubits. Doing roughly the same task with the surface code would require nearly 3,000 qubits.

This is a milestone. We are still looking for qLDPC codes with even more efficient architectures, and our research on performing error-corrected calculations using these codes is ongoing. But with this publication, the future of error correction looks bright.

fig1-Tanner Graphs of Surface and Bivariate Bicycle Codes.png

Fig. 1 | Tanner graphs of surface and BB codes.

Fig. 1 | Tanner graphs of surface and BB codes. a, Tanner graph of a surface code, for comparison. b, Tanner graph of a BB code with parameters [[144, 12, 12]] embedded into a torus. Any edge of the Tanner graph connects a data and a check vertex. Data qubits associated with the registers q(L) and q(R) are shown by blue and orange circles. Each vertex has six incident edges including four short-range edges (pointing north, south, east and west) and two long-range edges. We only show a few long-range edges to avoid clutter. Dashed and solid edges indicate two planar subgraphs spanning the Tanner graph, see the Methods. c, Sketch of a Tanner graph extension for measuring Z ˉ \={Z} and X ˉ \={X} following ref. 50, attaching to a surface code. The ancilla corresponding to the X ˉ \={X} measurement can be connected to a surface code, enabling load-store operations for all logical qubits by means of quantum teleportation and some logical unitaries. This extended Tanner graph also has an implementation in a thickness-2 architecture through the A and B edges (Methods).

Syndrome measurement circuit

Fig. 2 | Syndrome measurement circuit.

Fig. 2 | Syndrome measurement circuit. Full cycle of syndrome measurements relying on seven layers of CNOTs. We provide a local view of the circuit that only includes one data qubit from each register q(L) and q(R) . The circuit is symmetric under horizontal and vertical shifts of the Tanner graph. Each data qubit is coupled by CNOTs with three X-check and three Z-check qubits: see the Methods for more details.

Why error correction matters

Today, our users benefit from novel error mitigation techniques — methods for reducing or eliminating the effect of noise when calculating observables, alongside our work suppressing errors at the hardware level. This work brought us into the era of quantum utility. IBM researchers and partners all over the world are exploring practical applications of quantum computing today with existing quantum systems. Error mitigation lets users begin looking for quantum advantage on real quantum hardware.

But error mitigation comes with its own overhead, requiring running the same executions repeatedly so that classical computers can use statistical methods to extract an accurate result. This limits the scale of the programs you can run, and increasing that scale requires tools beyond error mitigation — like error correction.

Last year, we debuted a new roadmap laying out our plan to continuously improve quantum computers over the next decade. This new paper is an important example of how we plan to continuously increasing the complexity (number of gates) of the quantum circuits that can be run on our hardware. It will allow us to transition from running circuits with 15,000 gates to 100 million, or even 1 billion gates.

Bravyi, S., Cross, A.W., Gambetta, J.M. et al. High-threshold and low-overhead fault-tolerant quantum memory. Nature 627, 778–782 (2024). https://doi.org/10.1038/s41586-024-07107-7

Start using our 100+ qubit systems

Keep exploring, computing with error-corrected quantum computers.

Logical gates with magic state distillation

Logical gates with magic state distillation

Error correcting codes for near-term quantum computers

Error correcting codes for near-term quantum computers

research papers of nanotechnology

A new paper from IBM and UC Berkeley shows a path toward useful quantum computing

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  • v.14(5); 2021 May

Nanotechnology and its use in imaging and drug delivery (Review)

School of Health Sciences, Division of Applied Biomedical Sciences and Biotechnology, International Medical University, Kuala Lumpur 57000, Malaysia

Nyet Kui Wong

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Nanotechnology is the exploitation of the unique properties of materials at the nanoscale. Nanotechnology has gained popularity in several industries, as it offers better built and smarter products. The application of nanotechnology in medicine and healthcare is referred to as nanomedicine, and it has been used to combat some of the most common diseases, including cardiovascular diseases and cancer. The present review provides an overview of the recent advances of nanotechnology in the aspects of imaging and drug delivery.

1. Introduction

Nanoscience is the study of the unique properties of materials between 1-100 nm, and nanotechnology is the application of such research to create or modify novel objects. The ability to manipulate structures at the atomic scale allows for the creation of nanomaterials ( 1-3 ). Nanomaterials have unique optical, electrical and/or magnetic properties at the nanoscale, and these can be used in the fields of electronics and medicine, amongst other scenarios. Nanomaterials are unique as they provide a large surface area to volume ratio. Unlike other large-scaled engineered objects and systems, nanomaterials are governed by the laws of quantum mechanics instead of the classical laws of physics and chemistry. In short, nanotechnology is the engineering of useful objects and functional systems at the molecular or atomic scale ( 4 ).

Nanotechnologies have had a significant impact in almost all industries and areas of society as it offers i) better built, ii) safer and cleaner, iii) longer-lasting and iv) smarter products for medicine, communications, everyday life, agriculture and other industries ( 5 ). The use of nanomaterials in everyday products can be generally divided into two types. First, nanomaterials can be merged or added to a pre-existing product and improve the composite objects' overall performance by lending some of its unique properties. Otherwise, nanomaterials such as nanocrystals and nanoparticles can be used directly to create advanced and powerful devices attributed to their distinctive properties. The benefits of nanomaterials could potentially affect the future of nearly all industrial sectors ( 6 ).

The beneficial use of nanomaterials can be found in sunscreens, cosmetics, sporting goods, tyres, electronics and several other everyday items ( 6 ). Additionally, nanotechnologies have revolutionized advances in medicine, specifically in diagnostic methods, imaging and drug delivery. Table I illustrates the areas where nanotechnologies have had a significant impact.

Potential areas where nanotechnologies may have a significant impact.

Nanomaterials allow mass-creation of products with enhanced functionality, significantly lower costs, and greener and cleaner manufacturing processes, to improve healthcare and reduce the impact of manufacturing on the environment ( 7 ).

2. Nanotechnology in medicine and healthcare

Nanomedicine is the term used to refer to the applications of nanotechnologies in medicine and healthcare. Specifically, nanomedicine uses technologies at the nanoscale and nano-enabled techniques to prevent, diagnose, monitor and treat diseases ( 8 ). Nanotechnologies exhibit significant potential in the field of medicine, including in imaging techniques and diagnostic tools, drug delivery systems, tissue-engineered constructs, implants and pharmaceutical therapeutics ( 9 ), and has advanced treatments of several diseases, including cardiovascular diseases, cancer, musculoskeletal conditions, psychiatric and neurodegenerative diseases, bacterial and viral infections, and diabetes ( 10 ).

3. Types of nanoparticles

To date, several nanoparticles and nanomaterials have been investigated and approved for clinical use. Some common types of nanoparticles are discussed below.

Micelles are amphiphilic surfactant molecules that consist of lipids and amphiphilic molecules. Micelles spontaneously aggregate and self-assemble into spherical vesicles under aqueous conditions with a hydrophilic outer monolayer and a hydrophobic core, and thus can be used to incorporate hydrophobic therapeutic agents. The unique properties of micelles allow for the enhancement of the solubility of hydrophobic drugs, thus improving bioavailability. The diameter of micelles ranges from 10-100 nm. Micelles have various applications, such as drug delivery agents, imaging agents, contrast agents and therapeutic agents ( 11 ).

Liposomes are spherical vesicles with particle sizes ranging from 30 nm to several microns, that consist of lipid bilayers. Liposomes can be used to incorporate hydrophilic therapeutic agents inside the aqueous phase and hydrophobic agents in the liposomal membrane layer. Liposomes are versatile; their surface characteristics can be modified with polymers, antibodies and/or proteins, enabling macromolecular drugs, including nucleic acids and crystalline metals, to be integrated into liposomes ( 10 , 11 ). Poly(ethylene glycol) (PEG)ylated liposomal doxorubicin (Doxil ® ) is the first FDA-approved nanomedicine, which has been used for treatment of breast cancer, and it enhances the effective drug concentration in malignant effusions without the need to increase the overall dose ( 10 , 11 ).

Dendrimers are macromolecules with branched repeating units expanding from a central core and consists of exterior functional groups ( 10-12 ). These functional groups can be anionic, neutral or cationic terminals, and they can be used to modify the entire structure, and/or the chemical and physical properties. Therapeutic agents can be encapsulated within the interior space of dendrimers, or attached to the surface groups, making dendrimers highly bioavailable and biodegradable. Conjugates of dendrimers with saccharides or peptides have been shown to exhibit enhanced antimicrobial, antiprion and antiviral properties with improved solubility and stability upon absorption of therapeutic drugs ( 13 ). Polyamidoamine dendrimer-DNA complexes (called dendriplexes) have been investigated as gene delivery vectors and hold promise in facilitating successive gene expression, targeted drug delivery and improve drug efficacy ( 14 , 15 ). dendrimers are promising particulate systems for biomedical applications, such as in imaging and drug delivery ( 16 , 17 ), due to their transformable properties.

Carbon nanotubes

Carbon nanotubes are cylindrical molecules that consist of rolled-up sheets of a single-layer of carbon atoms (graphene). They can be single-walled or multi-walled, or composed of several concentrically interlinked nanotubes ( 17 ). Due to their high external surface area, carbon nanotubes can achieve considerably high loading capacities as drug carriers. Additionally, their unique optical, mechanical and electronic properties have made carbon tubes appealing as imaging contrast agents ( 18 , 19 ) and biological sensors ( 20 ).

Metallic nanoparticles

Metallic nanoparticles include iron oxide and gold nanoparticles. Iron oxide nanoparticles consist of a magnetic core (4-5 nm) and hydrophilic polymers, such as dextran or PEG ( 17-20 ). Conversely, gold nanoparticles are composed of a gold atom core surrounded by negative reactive groups on the surface that can be functionalized by adding a monolayer of surface moieties as ligands for active targeting ( 17-20 ). Metallic nanoparticles have been used as imaging contrast agents ( 21 ), in laser-based treatment ( 12 ), as optical biosensors ( 12 ) and drug delivery vehicles ( 22 ).

Quantum dots

Quantum dots (QDs) are fluorescent semiconductor nanocrystals (1-100 nm) and have shown potential use for several biomedical applications, such as drug delivery and cellular imaging ( 17 , 23 , 24 ). Quantum dots possess a shell-core structure, in which the core structure is typically composed of II-VI or III-V group elements of the periodic table. Due to their distinctive optical properties and size, with high brightness and stability, quantum dots have been employed in the field of medical imaging ( 10 , 23 ).

4. Nanotechnology in imaging and diagnosis

Diagnosis of a disease is one of the most crucial steps in the healthcare process. All diagnoses are desired to be quick, accurate and specific to prevent ‘false negative’ cases. In vivo imaging is a non-invasive technique that identifies signs or symptoms within a patient's live tissues, without the need to undergo surgery ( 24 ). A previous improvement in diagnostic imaging techniques is the use of biological markers that can detect changes in the tissues at the cellular level. The aim of using a biological marker is to detect illnesses or symptoms, thereby serving as an early detection tool ( 25 ). Notably, some of these high precision molecular imaging agents have been developed through the use of nanotechnologies. In addition to diagnosis, imaging is also vital for detecting potential toxic reactions, in controlled drug release research, evaluating drug distribution within the body and closely monitoring the progress of a therapy. Potential drug toxicity can be reduced with the possibility of monitoring the distribution of drugs around the body and by releasing the drug as required ( 26 ).

Diagnostic imaging

Imaging techniques such as X-ray, ultrasound, computed tomography, nuclear medicine and magnetic resonance imaging are well established, and are widely used in biochemical and medical research. However, these techniques can only examine changes on the tissue surface relatively late in disease progression, although they can be improved through the use of contrast and targeting agents based on nanotechnologies, to improve resolution and specificity, by indicating the diseased site at the tissue level ( 27 ). Currently used medical imaging contrast agents are primarily small molecules that exhibit fast metabolism and a non-specific distribution, and can thus potentially result in undesirable toxic side effects ( 10 ). This particular area is where nanotechnologies make their most significant contribution in the field of medicine, by developing more powerful contrast agents for almost all imaging techniques, as nanomaterials exhibit lower toxicity, and enhanced permeability and retention effects in tissues. The size of the nanoparticles significantly influences its biodistribution, blood circulation half-life, cellular uptake, tissue penetration and targeting ( 17 , 28 ). Table II summarises some examples of nanoparticles used as contrast agents in molecular imaging.

Examples of nanoparticles used as contrast agents in the biomedical field.

CT, computed tomography; MRI, magnetic resonance imaging.

The use of nanoparticles in X-rays has some limitations. In order to enhance the contrast, a number of heavy atoms must be delivered into the target site without causing any toxic reactions. This can be achieved using stable and inert surface atoms, such as gold and silver. Hence, gold nanoshells have garnered significant attention, due to its low toxicity. Gold nanoshells are heavy metal nanoparticles (dielectric core) encapsulated in gold shells and have been proposed to be one of the most promising materials in optical imaging of cancers ( 29 , 30 ). Gold nanoshells are cost-effective, safe due to its non-invasive property and may provide high resolution imaging. Gold nanoshells have similar physical characteristics to gold colloids, as they both possess a unified electronic response of the metal to light resulting in active optical absorption ( 29-32 ). Gold nanoshells are widely employed by researchers as contrast agents in the Optical Coherence Tomography of cancer cells, as the optical resonance of gold nanoshells can be adjusted accurately over a wide range, including near-infrared, where tissue transmissivity is higher ( 31 ). Table III shows the various types of nanomaterials used as contrast agents in pre-clinical investigations and in clinical use. Significantly more research and pre-clinical studies are required to understand and predict the effects of these nanomaterials in biological systems.

Types of nanomaterials used as contrast agents.

In situ diagnostic devices

In situ diagnostic devices, such as capsule endoscopy cameras, have been shown to be successful in the clinical stage. These devices can locate and image the bleeding site and other internal problems via oral ingestion. It is hypothesized that in the future, these devices will incorporate nano-scaled sensors for chemicals, virus, bacteria and pH to broaden their utility and applications. Moreover, these devices are also being developed as an alternative safe and precise means of drug-loaded capsules in drug delivery systems ( 33 , 34 ).

Nanotechnology in drug delivery

Therapy typically involves delivering drugs to a specific target site. If an internal route for drug delivery is not available, external therapeutic methods, such as radiotherapy and surgical procedures are employed. These methods are often used interchangeably or in combination to combat diseases. The goal of therapy is to always selectively remove the tumours or the source of illness in a long-lasting manner ( 35 ). Nanotechnologies are making a compelling contribution in this area through the development of novel modes for drug delivery, and some of these methods have proven effective in a clinical setting and are clinically used ( 36 ). For example, doxorubicin a drug which exhibits high toxicity, can be delivered directly to tumour cells using liposomes (Doxil ® ) without affecting the heart or kidneys. Additionally, paclitaxel incorporated with polymeric mPEG-PLA micelles (Genexol-PM ® ) are used in chemotherapeutic treatment of metastatic breast cancers ( 10 , 11 , 36 ). The success of nanotechnologies in drug delivery can be attributed to the improved in vivo distribution, evasion of the reticuloendothelial system and the favourable pharmacokinetics ( 36 ).

A perfect drug delivery system encompasses two elements: Control over drug release and the targeting ability. Side effects can be reduced significantly, and drug efficiency can be ensured by specifically targeting and killing harmful or cancerous cells. Additionally, controlled drug release can also reduce the side effects of drugs ( 37 ). Benefits of nanoparticle drug delivery systems include minimised irritant reactions and improved penetration within the body due to their small size, allowing for intravenous and other delivery routes. The specificity of nanoparticle drug delivery systems is made possible by attaching nano-scaled radioactive antibodies that are complementary to antigens on the cancer cells with drugs, and these approaches have produced desirable results ( 38 ), exhibiting improved i) drug bioavailability, ii) delivery of drugs specifically to the target site, and iii) uptake of low solubility drugs ( 39 ). Table IV summarises the advantages of nanoparticles over conventional fine particles ( 39 , 40 ).

Comparison of nanoparticles and fine particles in drug delivery systems.

5. Nanotechnology and cancer treatment

Staggering numbers of individuals suffer from cancer worldwide, highlighting the need for an accurate detection method and novel drug delivery system that is more specific, efficient and exhibits minimal side effects ( 41 ). Anticancer treatments are often regarded as superior if the therapeutic agent can reach the specific target site without resulting in any side effects. Chemical modifications of the surface of nanoparticle carriers may improve this required targeted delivery. One of the best examples of modifications at the surface of nanoparticles is the incorporation of PEG or polyethylene oxide. These modifications enhance not only the specificity of drug uptake, but also the tumour-targeting ability. Incorporating PEG avoids the detection of nanoparticles as foreign objects by the body's immune system, thus allowing them to circulate in the bloodstream until they reach the tumour. Additionally, the application of hydrogel in breast cancer is a prime example of this innovative technology. Herceptin is a type of monoclonal antibody used in breast cancer treatment by targeting human epidermal growth factor receptor 2 (HER2) on cancer cells. A vitamin E-based hydrogel has thus been developed that can deliver Herceptin to the target site for several weeks with just a single dose. Due to the improved retention of Herceptin within the tumour, the hydrogel-based drug delivery is more efficient than conventional subcutaneous and intravenous delivery modes, thus making it a better anti-tumour agent ( 42-44 ). Nanoparticles can be modified in several ways to prolong circulation, enhance drug localisation, increase drug efficacy and potentially decrease the development of multidrug resistance through the use of nanotechnologies.

There are several studies using FDA-approved nano drugs, such as Abraxane ® , Doxil ® or Genexol-PM ® as adjuvants in combinatory cancer treatment. Abraxane ® , a paclitaxel albumin-stabilised nanoparticle formulation (nab-paclitaxel) has been approved for the treatment of metastatic breast cancer ( 45 ). There are >900 ongoing clinical trials involving nab-paclitaxel as an anticancer agent, based on Clinicaltrials.gov as of August 2020. Moreover, nab-paclitaxel, in combination with 5-chloro-2.4-dihydrooxypyridine, tegafur and oteracil potassium exhibited promising results when used for the treatment of HER2-negative breast cancer patients ( 46 ). Doxorubicin, daunorubicin, paclitaxel and vincristine are among the most extensively investigated anticancer agents in liposome-based drug formulations ( 10 , 11 ). Table V provides examples of FDA approved nanomedicines ( 10 ).

Examples of FDA approved nanomedicines.

6. Nanotechnologies for the treatment of cardiovascular diseases

Cardiovascular diseases are another field where the properties of nanoparticles may be leveraged. Cardiovascular diseases are the leading cause of death globally, and the rates are increasing alarmingly, due to an increase in sedentary lifestyles ( 47 ). Common examples of cardiovascular diseases that affect several individuals includes stroke, hypertension and restriction or blockage of blood circulation in a specific area. These diseases are the most common causes of prolonged disability and death ( 47 ). Nanotechnologies offer novel avenues for therapeutic and diagnostic strategies for management of cardiovascular diseases.

Most cardiovascular risk factors (for example, for hypertension, smoking, hypercholesterolemia, homocystinuria and diabetes mellitus) are associated with impaired nitric oxide (NO) endothelial production. Impaired endothelial function is established to be the first step in atherosclerosis. Gold and silica nanoparticles have been developed to improve NO supply for possible application in cardiovascular diseases, where low NO bioavailability occurs ( 48 ). Systemic administration of the 17-βE loaded CREKA-peptide-modified-nanoemulsion system has been shown to reduce the levels of pathological contributors to early atherosclerosis by reducing lesion size, lowering the levels of circulating plasma lipids and decreasing the gene expression of inflammatory markers associated with the disease ( 49 ). Moreover, novel formulations of block copolymer micelles constructed using PEG and poly(propylene sulphide) have been demonstrated to suppress the levels of pro-inflammatory cytokines ( 50 ), and exhibited excellent potential for management of atherosclerosis ( 50 ).

Drug delivery via liposomes has been proven to be effective for prevention of platelet aggregation, atherosclerosis and thrombosis. Prostaglandin E-1 (PGE-1) exhibits a wide range of pharmacological properties, including vasodilation, inhibition of platelet aggregation, leukocyte adhesion, as well as exhibiting an anti-inflammatory effect. Liposomal drug delivery of PGE-1 (Liprostin™), is currently undergoing phase III clinical trials for the treatment of various cardiovascular diseases, such as restenosis following angioplasty ( 51 ). Additionally, the use of liposomes carrying the thrombolytic drug urokinase has also been assessed; cyclic arginyl-glycyl-aspartic acid (cRGD) peptide liposomes encapsulated with urokinase can selectively bind to the GPIIb/IIIa receptors, and this improves the thrombolytic efficacy of urokinase by almost 4-fold over free urokinase ( 51 ).

Efficacy and effectiveness of the conventional thrombolytic drugs can also be advanced via novel nano-therapeutic approaches. Drugs can be selectively targeted to vascular blockage sites through mechanical activation within blood vessels based on the high-fluid shear strains present within them. In vivo and in vitro studies have been encouraging, thus validating this approach for use in lysis of blood clots, using a significantly lower amount of thrombolytic drug ( 48-53 ). One example of this technology is the use of dendrimers. Dendrimers have been used in several diseases as a means of delivering therapeutic agents. Plasminogen activator (rtPA) has been successfully attached to dendrimers producing an alternative drug delivery system, allowing for refinement of the rtPA-dendrimer complex concentration throughout the duration of treatment using different dilution proportions of each part of the complex ( 53 ). Another potential role of nanoparticles is to decrease haemorrhaging, which is a severe side effect of thrombolytic agents. Targeted thrombolysis via rtPA bound to polyacrylic acid coated nanoparticles minimises the intracerebral haemorrhage, and enhances retention at the target site ( 11 ).

Incorporation of nanotechnologies has assisted in reducing the side effects of drugs, whilst requiring lower doses of the drug to treat cardiovascular diseases. Table VI summarises some of the applications of nanoscale pharmaceuticals in drug delivery.

Applications of nanoscale pharmaceuticals in drug delivery.

The current progress in nanotechnology research for drug delivery systems, particularly with regard to their water-insoluble properties, has enabled drugs to be delivered to target sites with higher carrier capacity, specificity and stability. The constant advancements in nanoparticle drug delivery systems have allowed researchers to develop formulations that can increase the efficiency of drugs, whilst reducing the cost ( 54 ).

7. Potential risks of nanotechnologies

Although the emerging field of nanotechnology has piqued the public's interest at large, nanotechnologies have also resulted in extensive discussions regarding their safety and any health risks associated with their use. New challenges arise with the use of nanomaterials, specifically in predicting, understanding and governing the potential health risks. Research has demonstrated that low-solubility nanoparticles are more hazardous and toxic on a mass by mass basis than larger particles ( 55 ). Other potential risks posed by nanoparticles include explosions and catalytic effects. It is important to note that only specific nanomaterials are considered risky, particularly those with high reactivity and mobility. Until more thorough studies can confirm the hazardous effects of nanomaterials, the mere presence of them in a laboratory setting will not in itself impose a threat to humanity and the environment ( 56 ). Potential risks of nanotechnology can be broadly grouped into three areas: Health, environment and society, as shown in Table VII .

Potential risks of nanotechnologies.

8. Conclusion

There is no doubt that nanotechnologies have helped to improve the quality of life of patients by providing a platform for advances in biotechnological, medicinal and pharmaceutical industries. They have also facilitated healthcare procedures, from diagnosis to therapeutic interventions and follow-up monitoring. There is a constant push to create and develop novel nanomaterials to improve diagnosis and cures for diseases in a targeted, accurate, potent and long-lasting manner, with the ultimate aim of making medical practices more personalised, cheaper and safer ( 57 , 58 ). The prospect of nanotechnology lies within using the right nanomaterials and reducing any possible harmful effects. It is important to note that, risk evaluations are required before new nano-based products are approved for clinical and commercial use, as with any other product, to minimise any potential hazards to human health and the environment. A full life cycle evaluation is required to more accurately ascertain the sustainability and safety of their use long term ( 59 ).


Funding statement.

Funding: No funding was received.

Availability of data and materials

Authors' contributions.

SS and NKW both wrote and revised the manuscript. Both authors have read and approved the final manuscript.

Ethics approval and consent to participate

Patient consent for publication, competing interests.

The authors declare that they have no competing interests.

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  • Published: 26 March 2024

Predicting and improving complex beer flavor through machine learning

  • Michiel Schreurs   ORCID: orcid.org/0000-0002-9449-5619 1 , 2 , 3   na1 ,
  • Supinya Piampongsant 1 , 2 , 3   na1 ,
  • Miguel Roncoroni   ORCID: orcid.org/0000-0001-7461-1427 1 , 2 , 3   na1 ,
  • Lloyd Cool   ORCID: orcid.org/0000-0001-9936-3124 1 , 2 , 3 , 4 ,
  • Beatriz Herrera-Malaver   ORCID: orcid.org/0000-0002-5096-9974 1 , 2 , 3 ,
  • Christophe Vanderaa   ORCID: orcid.org/0000-0001-7443-5427 4 ,
  • Florian A. Theßeling 1 , 2 , 3 ,
  • Łukasz Kreft   ORCID: orcid.org/0000-0001-7620-4657 5 ,
  • Alexander Botzki   ORCID: orcid.org/0000-0001-6691-4233 5 ,
  • Philippe Malcorps 6 ,
  • Luk Daenen 6 ,
  • Tom Wenseleers   ORCID: orcid.org/0000-0002-1434-861X 4 &
  • Kevin J. Verstrepen   ORCID: orcid.org/0000-0002-3077-6219 1 , 2 , 3  

Nature Communications volume  15 , Article number:  2368 ( 2024 ) Cite this article

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  • Chemical engineering
  • Gas chromatography
  • Machine learning
  • Metabolomics
  • Taste receptors

The perception and appreciation of food flavor depends on many interacting chemical compounds and external factors, and therefore proves challenging to understand and predict. Here, we combine extensive chemical and sensory analyses of 250 different beers to train machine learning models that allow predicting flavor and consumer appreciation. For each beer, we measure over 200 chemical properties, perform quantitative descriptive sensory analysis with a trained tasting panel and map data from over 180,000 consumer reviews to train 10 different machine learning models. The best-performing algorithm, Gradient Boosting, yields models that significantly outperform predictions based on conventional statistics and accurately predict complex food features and consumer appreciation from chemical profiles. Model dissection allows identifying specific and unexpected compounds as drivers of beer flavor and appreciation. Adding these compounds results in variants of commercial alcoholic and non-alcoholic beers with improved consumer appreciation. Together, our study reveals how big data and machine learning uncover complex links between food chemistry, flavor and consumer perception, and lays the foundation to develop novel, tailored foods with superior flavors.

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Predicting and understanding food perception and appreciation is one of the major challenges in food science. Accurate modeling of food flavor and appreciation could yield important opportunities for both producers and consumers, including quality control, product fingerprinting, counterfeit detection, spoilage detection, and the development of new products and product combinations (food pairing) 1 , 2 , 3 , 4 , 5 , 6 . Accurate models for flavor and consumer appreciation would contribute greatly to our scientific understanding of how humans perceive and appreciate flavor. Moreover, accurate predictive models would also facilitate and standardize existing food assessment methods and could supplement or replace assessments by trained and consumer tasting panels, which are variable, expensive and time-consuming 7 , 8 , 9 . Lastly, apart from providing objective, quantitative, accurate and contextual information that can help producers, models can also guide consumers in understanding their personal preferences 10 .

Despite the myriad of applications, predicting food flavor and appreciation from its chemical properties remains a largely elusive goal in sensory science, especially for complex food and beverages 11 , 12 . A key obstacle is the immense number of flavor-active chemicals underlying food flavor. Flavor compounds can vary widely in chemical structure and concentration, making them technically challenging and labor-intensive to quantify, even in the face of innovations in metabolomics, such as non-targeted metabolic fingerprinting 13 , 14 . Moreover, sensory analysis is perhaps even more complicated. Flavor perception is highly complex, resulting from hundreds of different molecules interacting at the physiochemical and sensorial level. Sensory perception is often non-linear, characterized by complex and concentration-dependent synergistic and antagonistic effects 15 , 16 , 17 , 18 , 19 , 20 , 21 that are further convoluted by the genetics, environment, culture and psychology of consumers 22 , 23 , 24 . Perceived flavor is therefore difficult to measure, with problems of sensitivity, accuracy, and reproducibility that can only be resolved by gathering sufficiently large datasets 25 . Trained tasting panels are considered the prime source of quality sensory data, but require meticulous training, are low throughput and high cost. Public databases containing consumer reviews of food products could provide a valuable alternative, especially for studying appreciation scores, which do not require formal training 25 . Public databases offer the advantage of amassing large amounts of data, increasing the statistical power to identify potential drivers of appreciation. However, public datasets suffer from biases, including a bias in the volunteers that contribute to the database, as well as confounding factors such as price, cult status and psychological conformity towards previous ratings of the product.

Classical multivariate statistics and machine learning methods have been used to predict flavor of specific compounds by, for example, linking structural properties of a compound to its potential biological activities or linking concentrations of specific compounds to sensory profiles 1 , 26 . Importantly, most previous studies focused on predicting organoleptic properties of single compounds (often based on their chemical structure) 27 , 28 , 29 , 30 , 31 , 32 , 33 , thus ignoring the fact that these compounds are present in a complex matrix in food or beverages and excluding complex interactions between compounds. Moreover, the classical statistics commonly used in sensory science 34 , 35 , 36 , 37 , 38 , 39 require a large sample size and sufficient variance amongst predictors to create accurate models. They are not fit for studying an extensive set of hundreds of interacting flavor compounds, since they are sensitive to outliers, have a high tendency to overfit and are less suited for non-linear and discontinuous relationships 40 .

In this study, we combine extensive chemical analyses and sensory data of a set of different commercial beers with machine learning approaches to develop models that predict taste, smell, mouthfeel and appreciation from compound concentrations. Beer is particularly suited to model the relationship between chemistry, flavor and appreciation. First, beer is a complex product, consisting of thousands of flavor compounds that partake in complex sensory interactions 41 , 42 , 43 . This chemical diversity arises from the raw materials (malt, yeast, hops, water and spices) and biochemical conversions during the brewing process (kilning, mashing, boiling, fermentation, maturation and aging) 44 , 45 . Second, the advent of the internet saw beer consumers embrace online review platforms, such as RateBeer (ZX Ventures, Anheuser-Busch InBev SA/NV) and BeerAdvocate (Next Glass, inc.). In this way, the beer community provides massive data sets of beer flavor and appreciation scores, creating extraordinarily large sensory databases to complement the analyses of our professional sensory panel. Specifically, we characterize over 200 chemical properties of 250 commercial beers, spread across 22 beer styles, and link these to the descriptive sensory profiling data of a 16-person in-house trained tasting panel and data acquired from over 180,000 public consumer reviews. These unique and extensive datasets enable us to train a suite of machine learning models to predict flavor and appreciation from a beer’s chemical profile. Dissection of the best-performing models allows us to pinpoint specific compounds as potential drivers of beer flavor and appreciation. Follow-up experiments confirm the importance of these compounds and ultimately allow us to significantly improve the flavor and appreciation of selected commercial beers. Together, our study represents a significant step towards understanding complex flavors and reinforces the value of machine learning to develop and refine complex foods. In this way, it represents a stepping stone for further computer-aided food engineering applications 46 .

To generate a comprehensive dataset on beer flavor, we selected 250 commercial Belgian beers across 22 different beer styles (Supplementary Fig.  S1 ). Beers with ≤ 4.2% alcohol by volume (ABV) were classified as non-alcoholic and low-alcoholic. Blonds and Tripels constitute a significant portion of the dataset (12.4% and 11.2%, respectively) reflecting their presence on the Belgian beer market and the heterogeneity of beers within these styles. By contrast, lager beers are less diverse and dominated by a handful of brands. Rare styles such as Brut or Faro make up only a small fraction of the dataset (2% and 1%, respectively) because fewer of these beers are produced and because they are dominated by distinct characteristics in terms of flavor and chemical composition.

Extensive analysis identifies relationships between chemical compounds in beer

For each beer, we measured 226 different chemical properties, including common brewing parameters such as alcohol content, iso-alpha acids, pH, sugar concentration 47 , and over 200 flavor compounds (Methods, Supplementary Table  S1 ). A large portion (37.2%) are terpenoids arising from hopping, responsible for herbal and fruity flavors 16 , 48 . A second major category are yeast metabolites, such as esters and alcohols, that result in fruity and solvent notes 48 , 49 , 50 . Other measured compounds are primarily derived from malt, or other microbes such as non- Saccharomyces yeasts and bacteria (‘wild flora’). Compounds that arise from spices or staling are labeled under ‘Others’. Five attributes (caloric value, total acids and total ester, hop aroma and sulfur compounds) are calculated from multiple individually measured compounds.

As a first step in identifying relationships between chemical properties, we determined correlations between the concentrations of the compounds (Fig.  1 , upper panel, Supplementary Data  1 and 2 , and Supplementary Fig.  S2 . For the sake of clarity, only a subset of the measured compounds is shown in Fig.  1 ). Compounds of the same origin typically show a positive correlation, while absence of correlation hints at parameters varying independently. For example, the hop aroma compounds citronellol, and alpha-terpineol show moderate correlations with each other (Spearman’s rho=0.39 and 0.57), but not with the bittering hop component iso-alpha acids (Spearman’s rho=0.16 and −0.07). This illustrates how brewers can independently modify hop aroma and bitterness by selecting hop varieties and dosage time. If hops are added early in the boiling phase, chemical conversions increase bitterness while aromas evaporate, conversely, late addition of hops preserves aroma but limits bitterness 51 . Similarly, hop-derived iso-alpha acids show a strong anti-correlation with lactic acid and acetic acid, likely reflecting growth inhibition of lactic acid and acetic acid bacteria, or the consequent use of fewer hops in sour beer styles, such as West Flanders ales and Fruit beers, that rely on these bacteria for their distinct flavors 52 . Finally, yeast-derived esters (ethyl acetate, ethyl decanoate, ethyl hexanoate, ethyl octanoate) and alcohols (ethanol, isoamyl alcohol, isobutanol, and glycerol), correlate with Spearman coefficients above 0.5, suggesting that these secondary metabolites are correlated with the yeast genetic background and/or fermentation parameters and may be difficult to influence individually, although the choice of yeast strain may offer some control 53 .

figure 1

Spearman rank correlations are shown. Descriptors are grouped according to their origin (malt (blue), hops (green), yeast (red), wild flora (yellow), Others (black)), and sensory aspect (aroma, taste, palate, and overall appreciation). Please note that for the chemical compounds, for the sake of clarity, only a subset of the total number of measured compounds is shown, with an emphasis on the key compounds for each source. For more details, see the main text and Methods section. Chemical data can be found in Supplementary Data  1 , correlations between all chemical compounds are depicted in Supplementary Fig.  S2 and correlation values can be found in Supplementary Data  2 . See Supplementary Data  4 for sensory panel assessments and Supplementary Data  5 for correlation values between all sensory descriptors.

Interestingly, different beer styles show distinct patterns for some flavor compounds (Supplementary Fig.  S3 ). These observations agree with expectations for key beer styles, and serve as a control for our measurements. For instance, Stouts generally show high values for color (darker), while hoppy beers contain elevated levels of iso-alpha acids, compounds associated with bitter hop taste. Acetic and lactic acid are not prevalent in most beers, with notable exceptions such as Kriek, Lambic, Faro, West Flanders ales and Flanders Old Brown, which use acid-producing bacteria ( Lactobacillus and Pediococcus ) or unconventional yeast ( Brettanomyces ) 54 , 55 . Glycerol, ethanol and esters show similar distributions across all beer styles, reflecting their common origin as products of yeast metabolism during fermentation 45 , 53 . Finally, low/no-alcohol beers contain low concentrations of glycerol and esters. This is in line with the production process for most of the low/no-alcohol beers in our dataset, which are produced through limiting fermentation or by stripping away alcohol via evaporation or dialysis, with both methods having the unintended side-effect of reducing the amount of flavor compounds in the final beer 56 , 57 .

Besides expected associations, our data also reveals less trivial associations between beer styles and specific parameters. For example, geraniol and citronellol, two monoterpenoids responsible for citrus, floral and rose flavors and characteristic of Citra hops, are found in relatively high amounts in Christmas, Saison, and Brett/co-fermented beers, where they may originate from terpenoid-rich spices such as coriander seeds instead of hops 58 .

Tasting panel assessments reveal sensorial relationships in beer

To assess the sensory profile of each beer, a trained tasting panel evaluated each of the 250 beers for 50 sensory attributes, including different hop, malt and yeast flavors, off-flavors and spices. Panelists used a tasting sheet (Supplementary Data  3 ) to score the different attributes. Panel consistency was evaluated by repeating 12 samples across different sessions and performing ANOVA. In 95% of cases no significant difference was found across sessions ( p  > 0.05), indicating good panel consistency (Supplementary Table  S2 ).

Aroma and taste perception reported by the trained panel are often linked (Fig.  1 , bottom left panel and Supplementary Data  4 and 5 ), with high correlations between hops aroma and taste (Spearman’s rho=0.83). Bitter taste was found to correlate with hop aroma and taste in general (Spearman’s rho=0.80 and 0.69), and particularly with “grassy” noble hops (Spearman’s rho=0.75). Barnyard flavor, most often associated with sour beers, is identified together with stale hops (Spearman’s rho=0.97) that are used in these beers. Lactic and acetic acid, which often co-occur, are correlated (Spearman’s rho=0.66). Interestingly, sweetness and bitterness are anti-correlated (Spearman’s rho = −0.48), confirming the hypothesis that they mask each other 59 , 60 . Beer body is highly correlated with alcohol (Spearman’s rho = 0.79), and overall appreciation is found to correlate with multiple aspects that describe beer mouthfeel (alcohol, carbonation; Spearman’s rho= 0.32, 0.39), as well as with hop and ester aroma intensity (Spearman’s rho=0.39 and 0.35).

Similar to the chemical analyses, sensorial analyses confirmed typical features of specific beer styles (Supplementary Fig.  S4 ). For example, sour beers (Faro, Flanders Old Brown, Fruit beer, Kriek, Lambic, West Flanders ale) were rated acidic, with flavors of both acetic and lactic acid. Hoppy beers were found to be bitter and showed hop-associated aromas like citrus and tropical fruit. Malt taste is most detected among scotch, stout/porters, and strong ales, while low/no-alcohol beers, which often have a reputation for being ‘worty’ (reminiscent of unfermented, sweet malt extract) appear in the middle. Unsurprisingly, hop aromas are most strongly detected among hoppy beers. Like its chemical counterpart (Supplementary Fig.  S3 ), acidity shows a right-skewed distribution, with the most acidic beers being Krieks, Lambics, and West Flanders ales.

Tasting panel assessments of specific flavors correlate with chemical composition

We find that the concentrations of several chemical compounds strongly correlate with specific aroma or taste, as evaluated by the tasting panel (Fig.  2 , Supplementary Fig.  S5 , Supplementary Data  6 ). In some cases, these correlations confirm expectations and serve as a useful control for data quality. For example, iso-alpha acids, the bittering compounds in hops, strongly correlate with bitterness (Spearman’s rho=0.68), while ethanol and glycerol correlate with tasters’ perceptions of alcohol and body, the mouthfeel sensation of fullness (Spearman’s rho=0.82/0.62 and 0.72/0.57 respectively) and darker color from roasted malts is a good indication of malt perception (Spearman’s rho=0.54).

figure 2

Heatmap colors indicate Spearman’s Rho. Axes are organized according to sensory categories (aroma, taste, mouthfeel, overall), chemical categories and chemical sources in beer (malt (blue), hops (green), yeast (red), wild flora (yellow), Others (black)). See Supplementary Data  6 for all correlation values.

Interestingly, for some relationships between chemical compounds and perceived flavor, correlations are weaker than expected. For example, the rose-smelling phenethyl acetate only weakly correlates with floral aroma. This hints at more complex relationships and interactions between compounds and suggests a need for a more complex model than simple correlations. Lastly, we uncovered unexpected correlations. For instance, the esters ethyl decanoate and ethyl octanoate appear to correlate slightly with hop perception and bitterness, possibly due to their fruity flavor. Iron is anti-correlated with hop aromas and bitterness, most likely because it is also anti-correlated with iso-alpha acids. This could be a sign of metal chelation of hop acids 61 , given that our analyses measure unbound hop acids and total iron content, or could result from the higher iron content in dark and Fruit beers, which typically have less hoppy and bitter flavors 62 .

Public consumer reviews complement expert panel data

To complement and expand the sensory data of our trained tasting panel, we collected 180,000 reviews of our 250 beers from the online consumer review platform RateBeer. This provided numerical scores for beer appearance, aroma, taste, palate, overall quality as well as the average overall score.

Public datasets are known to suffer from biases, such as price, cult status and psychological conformity towards previous ratings of a product. For example, prices correlate with appreciation scores for these online consumer reviews (rho=0.49, Supplementary Fig.  S6 ), but not for our trained tasting panel (rho=0.19). This suggests that prices affect consumer appreciation, which has been reported in wine 63 , while blind tastings are unaffected. Moreover, we observe that some beer styles, like lagers and non-alcoholic beers, generally receive lower scores, reflecting that online reviewers are mostly beer aficionados with a preference for specialty beers over lager beers. In general, we find a modest correlation between our trained panel’s overall appreciation score and the online consumer appreciation scores (Fig.  3 , rho=0.29). Apart from the aforementioned biases in the online datasets, serving temperature, sample freshness and surroundings, which are all tightly controlled during the tasting panel sessions, can vary tremendously across online consumers and can further contribute to (among others, appreciation) differences between the two categories of tasters. Importantly, in contrast to the overall appreciation scores, for many sensory aspects the results from the professional panel correlated well with results obtained from RateBeer reviews. Correlations were highest for features that are relatively easy to recognize even for untrained tasters, like bitterness, sweetness, alcohol and malt aroma (Fig.  3 and below).

figure 3

RateBeer text mining results can be found in Supplementary Data  7 . Rho values shown are Spearman correlation values, with asterisks indicating significant correlations ( p  < 0.05, two-sided). All p values were smaller than 0.001, except for Esters aroma (0.0553), Esters taste (0.3275), Esters aroma—banana (0.0019), Coriander (0.0508) and Diacetyl (0.0134).

Besides collecting consumer appreciation from these online reviews, we developed automated text analysis tools to gather additional data from review texts (Supplementary Data  7 ). Processing review texts on the RateBeer database yielded comparable results to the scores given by the trained panel for many common sensory aspects, including acidity, bitterness, sweetness, alcohol, malt, and hop tastes (Fig.  3 ). This is in line with what would be expected, since these attributes require less training for accurate assessment and are less influenced by environmental factors such as temperature, serving glass and odors in the environment. Consumer reviews also correlate well with our trained panel for 4-vinyl guaiacol, a compound associated with a very characteristic aroma. By contrast, correlations for more specific aromas like ester, coriander or diacetyl are underrepresented in the online reviews, underscoring the importance of using a trained tasting panel and standardized tasting sheets with explicit factors to be scored for evaluating specific aspects of a beer. Taken together, our results suggest that public reviews are trustworthy for some, but not all, flavor features and can complement or substitute taste panel data for these sensory aspects.

Models can predict beer sensory profiles from chemical data

The rich datasets of chemical analyses, tasting panel assessments and public reviews gathered in the first part of this study provided us with a unique opportunity to develop predictive models that link chemical data to sensorial features. Given the complexity of beer flavor, basic statistical tools such as correlations or linear regression may not always be the most suitable for making accurate predictions. Instead, we applied different machine learning models that can model both simple linear and complex interactive relationships. Specifically, we constructed a set of regression models to predict (a) trained panel scores for beer flavor and quality and (b) public reviews’ appreciation scores from beer chemical profiles. We trained and tested 10 different models (Methods), 3 linear regression-based models (simple linear regression with first-order interactions (LR), lasso regression with first-order interactions (Lasso), partial least squares regressor (PLSR)), 5 decision tree models (AdaBoost regressor (ABR), extra trees (ET), gradient boosting regressor (GBR), random forest (RF) and XGBoost regressor (XGBR)), 1 support vector regression (SVR), and 1 artificial neural network (ANN) model.

To compare the performance of our machine learning models, the dataset was randomly split into a training and test set, stratified by beer style. After a model was trained on data in the training set, its performance was evaluated on its ability to predict the test dataset obtained from multi-output models (based on the coefficient of determination, see Methods). Additionally, individual-attribute models were ranked per descriptor and the average rank was calculated, as proposed by Korneva et al. 64 . Importantly, both ways of evaluating the models’ performance agreed in general. Performance of the different models varied (Table  1 ). It should be noted that all models perform better at predicting RateBeer results than results from our trained tasting panel. One reason could be that sensory data is inherently variable, and this variability is averaged out with the large number of public reviews from RateBeer. Additionally, all tree-based models perform better at predicting taste than aroma. Linear models (LR) performed particularly poorly, with negative R 2 values, due to severe overfitting (training set R 2  = 1). Overfitting is a common issue in linear models with many parameters and limited samples, especially with interaction terms further amplifying the number of parameters. L1 regularization (Lasso) successfully overcomes this overfitting, out-competing multiple tree-based models on the RateBeer dataset. Similarly, the dimensionality reduction of PLSR avoids overfitting and improves performance, to some extent. Still, tree-based models (ABR, ET, GBR, RF and XGBR) show the best performance, out-competing the linear models (LR, Lasso, PLSR) commonly used in sensory science 65 .

GBR models showed the best overall performance in predicting sensory responses from chemical information, with R 2 values up to 0.75 depending on the predicted sensory feature (Supplementary Table  S4 ). The GBR models predict consumer appreciation (RateBeer) better than our trained panel’s appreciation (R 2 value of 0.67 compared to R 2 value of 0.09) (Supplementary Table  S3 and Supplementary Table  S4 ). ANN models showed intermediate performance, likely because neural networks typically perform best with larger datasets 66 . The SVR shows intermediate performance, mostly due to the weak predictions of specific attributes that lower the overall performance (Supplementary Table  S4 ).

Model dissection identifies specific, unexpected compounds as drivers of consumer appreciation

Next, we leveraged our models to infer important contributors to sensory perception and consumer appreciation. Consumer preference is a crucial sensory aspects, because a product that shows low consumer appreciation scores often does not succeed commercially 25 . Additionally, the requirement for a large number of representative evaluators makes consumer trials one of the more costly and time-consuming aspects of product development. Hence, a model for predicting chemical drivers of overall appreciation would be a welcome addition to the available toolbox for food development and optimization.

Since GBR models on our RateBeer dataset showed the best overall performance, we focused on these models. Specifically, we used two approaches to identify important contributors. First, rankings of the most important predictors for each sensorial trait in the GBR models were obtained based on impurity-based feature importance (mean decrease in impurity). High-ranked parameters were hypothesized to be either the true causal chemical properties underlying the trait, to correlate with the actual causal properties, or to take part in sensory interactions affecting the trait 67 (Fig.  4A ). In a second approach, we used SHAP 68 to determine which parameters contributed most to the model for making predictions of consumer appreciation (Fig.  4B ). SHAP calculates parameter contributions to model predictions on a per-sample basis, which can be aggregated into an importance score.

figure 4

A The impurity-based feature importance (mean deviance in impurity, MDI) calculated from the Gradient Boosting Regression (GBR) model predicting RateBeer appreciation scores. The top 15 highest ranked chemical properties are shown. B SHAP summary plot for the top 15 parameters contributing to our GBR model. Each point on the graph represents a sample from our dataset. The color represents the concentration of that parameter, with bluer colors representing low values and redder colors representing higher values. Greater absolute values on the horizontal axis indicate a higher impact of the parameter on the prediction of the model. C Spearman correlations between the 15 most important chemical properties and consumer overall appreciation. Numbers indicate the Spearman Rho correlation coefficient, and the rank of this correlation compared to all other correlations. The top 15 important compounds were determined using SHAP (panel B).

Both approaches identified ethyl acetate as the most predictive parameter for beer appreciation (Fig.  4 ). Ethyl acetate is the most abundant ester in beer with a typical ‘fruity’, ‘solvent’ and ‘alcoholic’ flavor, but is often considered less important than other esters like isoamyl acetate. The second most important parameter identified by SHAP is ethanol, the most abundant beer compound after water. Apart from directly contributing to beer flavor and mouthfeel, ethanol drastically influences the physical properties of beer, dictating how easily volatile compounds escape the beer matrix to contribute to beer aroma 69 . Importantly, it should also be noted that the importance of ethanol for appreciation is likely inflated by the very low appreciation scores of non-alcoholic beers (Supplementary Fig.  S4 ). Despite not often being considered a driver of beer appreciation, protein level also ranks highly in both approaches, possibly due to its effect on mouthfeel and body 70 . Lactic acid, which contributes to the tart taste of sour beers, is the fourth most important parameter identified by SHAP, possibly due to the generally high appreciation of sour beers in our dataset.

Interestingly, some of the most important predictive parameters for our model are not well-established as beer flavors or are even commonly regarded as being negative for beer quality. For example, our models identify methanethiol and ethyl phenyl acetate, an ester commonly linked to beer staling 71 , as a key factor contributing to beer appreciation. Although there is no doubt that high concentrations of these compounds are considered unpleasant, the positive effects of modest concentrations are not yet known 72 , 73 .

To compare our approach to conventional statistics, we evaluated how well the 15 most important SHAP-derived parameters correlate with consumer appreciation (Fig.  4C ). Interestingly, only 6 of the properties derived by SHAP rank amongst the top 15 most correlated parameters. For some chemical compounds, the correlations are so low that they would have likely been considered unimportant. For example, lactic acid, the fourth most important parameter, shows a bimodal distribution for appreciation, with sour beers forming a separate cluster, that is missed entirely by the Spearman correlation. Additionally, the correlation plots reveal outliers, emphasizing the need for robust analysis tools. Together, this highlights the need for alternative models, like the Gradient Boosting model, that better grasp the complexity of (beer) flavor.

Finally, to observe the relationships between these chemical properties and their predicted targets, partial dependence plots were constructed for the six most important predictors of consumer appreciation 74 , 75 , 76 (Supplementary Fig.  S7 ). One-way partial dependence plots show how a change in concentration affects the predicted appreciation. These plots reveal an important limitation of our models: appreciation predictions remain constant at ever-increasing concentrations. This implies that once a threshold concentration is reached, further increasing the concentration does not affect appreciation. This is false, as it is well-documented that certain compounds become unpleasant at high concentrations, including ethyl acetate (‘nail polish’) 77 and methanethiol (‘sulfury’ and ‘rotten cabbage’) 78 . The inability of our models to grasp that flavor compounds have optimal levels, above which they become negative, is a consequence of working with commercial beer brands where (off-)flavors are rarely too high to negatively impact the product. The two-way partial dependence plots show how changing the concentration of two compounds influences predicted appreciation, visualizing their interactions (Supplementary Fig.  S7 ). In our case, the top 5 parameters are dominated by additive or synergistic interactions, with high concentrations for both compounds resulting in the highest predicted appreciation.

To assess the robustness of our best-performing models and model predictions, we performed 100 iterations of the GBR, RF and ET models. In general, all iterations of the models yielded similar performance (Supplementary Fig.  S8 ). Moreover, the main predictors (including the top predictors ethanol and ethyl acetate) remained virtually the same, especially for GBR and RF. For the iterations of the ET model, we did observe more variation in the top predictors, which is likely a consequence of the model’s inherent random architecture in combination with co-correlations between certain predictors. However, even in this case, several of the top predictors (ethanol and ethyl acetate) remain unchanged, although their rank in importance changes (Supplementary Fig.  S8 ).

Next, we investigated if a combination of RateBeer and trained panel data into one consolidated dataset would lead to stronger models, under the hypothesis that such a model would suffer less from bias in the datasets. A GBR model was trained to predict appreciation on the combined dataset. This model underperformed compared to the RateBeer model, both in the native case and when including a dataset identifier (R 2  = 0.67, 0.26 and 0.42 respectively). For the latter, the dataset identifier is the most important feature (Supplementary Fig.  S9 ), while most of the feature importance remains unchanged, with ethyl acetate and ethanol ranking highest, like in the original model trained only on RateBeer data. It seems that the large variation in the panel dataset introduces noise, weakening the models’ performances and reliability. In addition, it seems reasonable to assume that both datasets are fundamentally different, with the panel dataset obtained by blind tastings by a trained professional panel.

Lastly, we evaluated whether beer style identifiers would further enhance the model’s performance. A GBR model was trained with parameters that explicitly encoded the styles of the samples. This did not improve model performance (R2 = 0.66 with style information vs R2 = 0.67). The most important chemical features are consistent with the model trained without style information (eg. ethanol and ethyl acetate), and with the exception of the most preferred (strong ale) and least preferred (low/no-alcohol) styles, none of the styles were among the most important features (Supplementary Fig.  S9 , Supplementary Table  S5 and S6 ). This is likely due to a combination of style-specific chemical signatures, such as iso-alpha acids and lactic acid, that implicitly convey style information to the original models, as well as the low number of samples belonging to some styles, making it difficult for the model to learn style-specific patterns. Moreover, beer styles are not rigorously defined, with some styles overlapping in features and some beers being misattributed to a specific style, all of which leads to more noise in models that use style parameters.

Model validation

To test if our predictive models give insight into beer appreciation, we set up experiments aimed at improving existing commercial beers. We specifically selected overall appreciation as the trait to be examined because of its complexity and commercial relevance. Beer flavor comprises a complex bouquet rather than single aromas and tastes 53 . Hence, adding a single compound to the extent that a difference is noticeable may lead to an unbalanced, artificial flavor. Therefore, we evaluated the effect of combinations of compounds. Because Blond beers represent the most extensive style in our dataset, we selected a beer from this style as the starting material for these experiments (Beer 64 in Supplementary Data  1 ).

In the first set of experiments, we adjusted the concentrations of compounds that made up the most important predictors of overall appreciation (ethyl acetate, ethanol, lactic acid, ethyl phenyl acetate) together with correlated compounds (ethyl hexanoate, isoamyl acetate, glycerol), bringing them up to 95 th percentile ethanol-normalized concentrations (Methods) within the Blond group (‘Spiked’ concentration in Fig.  5A ). Compared to controls, the spiked beers were found to have significantly improved overall appreciation among trained panelists, with panelist noting increased intensity of ester flavors, sweetness, alcohol, and body fullness (Fig.  5B ). To disentangle the contribution of ethanol to these results, a second experiment was performed without the addition of ethanol. This resulted in a similar outcome, including increased perception of alcohol and overall appreciation.

figure 5

Adding the top chemical compounds, identified as best predictors of appreciation by our model, into poorly appreciated beers results in increased appreciation from our trained panel. Results of sensory tests between base beers and those spiked with compounds identified as the best predictors by the model. A Blond and Non/Low-alcohol (0.0% ABV) base beers were brought up to 95th-percentile ethanol-normalized concentrations within each style. B For each sensory attribute, tasters indicated the more intense sample and selected the sample they preferred. The numbers above the bars correspond to the p values that indicate significant changes in perceived flavor (two-sided binomial test: alpha 0.05, n  = 20 or 13).

In a last experiment, we tested whether using the model’s predictions can boost the appreciation of a non-alcoholic beer (beer 223 in Supplementary Data  1 ). Again, the addition of a mixture of predicted compounds (omitting ethanol, in this case) resulted in a significant increase in appreciation, body, ester flavor and sweetness.

Predicting flavor and consumer appreciation from chemical composition is one of the ultimate goals of sensory science. A reliable, systematic and unbiased way to link chemical profiles to flavor and food appreciation would be a significant asset to the food and beverage industry. Such tools would substantially aid in quality control and recipe development, offer an efficient and cost-effective alternative to pilot studies and consumer trials and would ultimately allow food manufacturers to produce superior, tailor-made products that better meet the demands of specific consumer groups more efficiently.

A limited set of studies have previously tried, to varying degrees of success, to predict beer flavor and beer popularity based on (a limited set of) chemical compounds and flavors 79 , 80 . Current sensitive, high-throughput technologies allow measuring an unprecedented number of chemical compounds and properties in a large set of samples, yielding a dataset that can train models that help close the gaps between chemistry and flavor, even for a complex natural product like beer. To our knowledge, no previous research gathered data at this scale (250 samples, 226 chemical parameters, 50 sensory attributes and 5 consumer scores) to disentangle and validate the chemical aspects driving beer preference using various machine-learning techniques. We find that modern machine learning models outperform conventional statistical tools, such as correlations and linear models, and can successfully predict flavor appreciation from chemical composition. This could be attributed to the natural incorporation of interactions and non-linear or discontinuous effects in machine learning models, which are not easily grasped by the linear model architecture. While linear models and partial least squares regression represent the most widespread statistical approaches in sensory science, in part because they allow interpretation 65 , 81 , 82 , modern machine learning methods allow for building better predictive models while preserving the possibility to dissect and exploit the underlying patterns. Of the 10 different models we trained, tree-based models, such as our best performing GBR, showed the best overall performance in predicting sensory responses from chemical information, outcompeting artificial neural networks. This agrees with previous reports for models trained on tabular data 83 . Our results are in line with the findings of Colantonio et al. who also identified the gradient boosting architecture as performing best at predicting appreciation and flavor (of tomatoes and blueberries, in their specific study) 26 . Importantly, besides our larger experimental scale, we were able to directly confirm our models’ predictions in vivo.

Our study confirms that flavor compound concentration does not always correlate with perception, suggesting complex interactions that are often missed by more conventional statistics and simple models. Specifically, we find that tree-based algorithms may perform best in developing models that link complex food chemistry with aroma. Furthermore, we show that massive datasets of untrained consumer reviews provide a valuable source of data, that can complement or even replace trained tasting panels, especially for appreciation and basic flavors, such as sweetness and bitterness. This holds despite biases that are known to occur in such datasets, such as price or conformity bias. Moreover, GBR models predict taste better than aroma. This is likely because taste (e.g. bitterness) often directly relates to the corresponding chemical measurements (e.g., iso-alpha acids), whereas such a link is less clear for aromas, which often result from the interplay between multiple volatile compounds. We also find that our models are best at predicting acidity and alcohol, likely because there is a direct relation between the measured chemical compounds (acids and ethanol) and the corresponding perceived sensorial attribute (acidity and alcohol), and because even untrained consumers are generally able to recognize these flavors and aromas.

The predictions of our final models, trained on review data, hold even for blind tastings with small groups of trained tasters, as demonstrated by our ability to validate specific compounds as drivers of beer flavor and appreciation. Since adding a single compound to the extent of a noticeable difference may result in an unbalanced flavor profile, we specifically tested our identified key drivers as a combination of compounds. While this approach does not allow us to validate if a particular single compound would affect flavor and/or appreciation, our experiments do show that this combination of compounds increases consumer appreciation.

It is important to stress that, while it represents an important step forward, our approach still has several major limitations. A key weakness of the GBR model architecture is that amongst co-correlating variables, the largest main effect is consistently preferred for model building. As a result, co-correlating variables often have artificially low importance scores, both for impurity and SHAP-based methods, like we observed in the comparison to the more randomized Extra Trees models. This implies that chemicals identified as key drivers of a specific sensory feature by GBR might not be the true causative compounds, but rather co-correlate with the actual causative chemical. For example, the high importance of ethyl acetate could be (partially) attributed to the total ester content, ethanol or ethyl hexanoate (rho=0.77, rho=0.72 and rho=0.68), while ethyl phenylacetate could hide the importance of prenyl isobutyrate and ethyl benzoate (rho=0.77 and rho=0.76). Expanding our GBR model to include beer style as a parameter did not yield additional power or insight. This is likely due to style-specific chemical signatures, such as iso-alpha acids and lactic acid, that implicitly convey style information to the original model, as well as the smaller sample size per style, limiting the power to uncover style-specific patterns. This can be partly attributed to the curse of dimensionality, where the high number of parameters results in the models mainly incorporating single parameter effects, rather than complex interactions such as style-dependent effects 67 . A larger number of samples may overcome some of these limitations and offer more insight into style-specific effects. On the other hand, beer style is not a rigid scientific classification, and beers within one style often differ a lot, which further complicates the analysis of style as a model factor.

Our study is limited to beers from Belgian breweries. Although these beers cover a large portion of the beer styles available globally, some beer styles and consumer patterns may be missing, while other features might be overrepresented. For example, many Belgian ales exhibit yeast-driven flavor profiles, which is reflected in the chemical drivers of appreciation discovered by this study. In future work, expanding the scope to include diverse markets and beer styles could lead to the identification of even more drivers of appreciation and better models for special niche products that were not present in our beer set.

In addition to inherent limitations of GBR models, there are also some limitations associated with studying food aroma. Even if our chemical analyses measured most of the known aroma compounds, the total number of flavor compounds in complex foods like beer is still larger than the subset we were able to measure in this study. For example, hop-derived thiols, that influence flavor at very low concentrations, are notoriously difficult to measure in a high-throughput experiment. Moreover, consumer perception remains subjective and prone to biases that are difficult to avoid. It is also important to stress that the models are still immature and that more extensive datasets will be crucial for developing more complete models in the future. Besides more samples and parameters, our dataset does not include any demographic information about the tasters. Including such data could lead to better models that grasp external factors like age and culture. Another limitation is that our set of beers consists of high-quality end-products and lacks beers that are unfit for sale, which limits the current model in accurately predicting products that are appreciated very badly. Finally, while models could be readily applied in quality control, their use in sensory science and product development is restrained by their inability to discern causal relationships. Given that the models cannot distinguish compounds that genuinely drive consumer perception from those that merely correlate, validation experiments are essential to identify true causative compounds.

Despite the inherent limitations, dissection of our models enabled us to pinpoint specific molecules as potential drivers of beer aroma and consumer appreciation, including compounds that were unexpected and would not have been identified using standard approaches. Important drivers of beer appreciation uncovered by our models include protein levels, ethyl acetate, ethyl phenyl acetate and lactic acid. Currently, many brewers already use lactic acid to acidify their brewing water and ensure optimal pH for enzymatic activity during the mashing process. Our results suggest that adding lactic acid can also improve beer appreciation, although its individual effect remains to be tested. Interestingly, ethanol appears to be unnecessary to improve beer appreciation, both for blond beer and alcohol-free beer. Given the growing consumer interest in alcohol-free beer, with a predicted annual market growth of >7% 84 , it is relevant for brewers to know what compounds can further increase consumer appreciation of these beers. Hence, our model may readily provide avenues to further improve the flavor and consumer appreciation of both alcoholic and non-alcoholic beers, which is generally considered one of the key challenges for future beer production.

Whereas we see a direct implementation of our results for the development of superior alcohol-free beverages and other food products, our study can also serve as a stepping stone for the development of novel alcohol-containing beverages. We want to echo the growing body of scientific evidence for the negative effects of alcohol consumption, both on the individual level by the mutagenic, teratogenic and carcinogenic effects of ethanol 85 , 86 , as well as the burden on society caused by alcohol abuse and addiction. We encourage the use of our results for the production of healthier, tastier products, including novel and improved beverages with lower alcohol contents. Furthermore, we strongly discourage the use of these technologies to improve the appreciation or addictive properties of harmful substances.

The present work demonstrates that despite some important remaining hurdles, combining the latest developments in chemical analyses, sensory analysis and modern machine learning methods offers exciting avenues for food chemistry and engineering. Soon, these tools may provide solutions in quality control and recipe development, as well as new approaches to sensory science and flavor research.

Beer selection

250 commercial Belgian beers were selected to cover the broad diversity of beer styles and corresponding diversity in chemical composition and aroma. See Supplementary Fig.  S1 .

Chemical dataset

Sample preparation.

Beers within their expiration date were purchased from commercial retailers. Samples were prepared in biological duplicates at room temperature, unless explicitly stated otherwise. Bottle pressure was measured with a manual pressure device (Steinfurth Mess-Systeme GmbH) and used to calculate CO 2 concentration. The beer was poured through two filter papers (Macherey-Nagel, 500713032 MN 713 ¼) to remove carbon dioxide and prevent spontaneous foaming. Samples were then prepared for measurements by targeted Headspace-Gas Chromatography-Flame Ionization Detector/Flame Photometric Detector (HS-GC-FID/FPD), Headspace-Solid Phase Microextraction-Gas Chromatography-Mass Spectrometry (HS-SPME-GC-MS), colorimetric analysis, enzymatic analysis, Near-Infrared (NIR) analysis, as described in the sections below. The mean values of biological duplicates are reported for each compound.


HS-GC-FID/FPD (Shimadzu GC 2010 Plus) was used to measure higher alcohols, acetaldehyde, esters, 4-vinyl guaicol, and sulfur compounds. Each measurement comprised 5 ml of sample pipetted into a 20 ml glass vial containing 1.75 g NaCl (VWR, 27810.295). 100 µl of 2-heptanol (Sigma-Aldrich, H3003) (internal standard) solution in ethanol (Fisher Chemical, E/0650DF/C17) was added for a final concentration of 2.44 mg/L. Samples were flushed with nitrogen for 10 s, sealed with a silicone septum, stored at −80 °C and analyzed in batches of 20.

The GC was equipped with a DB-WAXetr column (length, 30 m; internal diameter, 0.32 mm; layer thickness, 0.50 µm; Agilent Technologies, Santa Clara, CA, USA) to the FID and an HP-5 column (length, 30 m; internal diameter, 0.25 mm; layer thickness, 0.25 µm; Agilent Technologies, Santa Clara, CA, USA) to the FPD. N 2 was used as the carrier gas. Samples were incubated for 20 min at 70 °C in the headspace autosampler (Flow rate, 35 cm/s; Injection volume, 1000 µL; Injection mode, split; Combi PAL autosampler, CTC analytics, Switzerland). The injector, FID and FPD temperatures were kept at 250 °C. The GC oven temperature was first held at 50 °C for 5 min and then allowed to rise to 80 °C at a rate of 5 °C/min, followed by a second ramp of 4 °C/min until 200 °C kept for 3 min and a final ramp of (4 °C/min) until 230 °C for 1 min. Results were analyzed with the GCSolution software version 2.4 (Shimadzu, Kyoto, Japan). The GC was calibrated with a 5% EtOH solution (VWR International) containing the volatiles under study (Supplementary Table  S7 ).


HS-SPME-GC-MS (Shimadzu GCMS-QP-2010 Ultra) was used to measure additional volatile compounds, mainly comprising terpenoids and esters. Samples were analyzed by HS-SPME using a triphase DVB/Carboxen/PDMS 50/30 μm SPME fiber (Supelco Co., Bellefonte, PA, USA) followed by gas chromatography (Thermo Fisher Scientific Trace 1300 series, USA) coupled to a mass spectrometer (Thermo Fisher Scientific ISQ series MS) equipped with a TriPlus RSH autosampler. 5 ml of degassed beer sample was placed in 20 ml vials containing 1.75 g NaCl (VWR, 27810.295). 5 µl internal standard mix was added, containing 2-heptanol (1 g/L) (Sigma-Aldrich, H3003), 4-fluorobenzaldehyde (1 g/L) (Sigma-Aldrich, 128376), 2,3-hexanedione (1 g/L) (Sigma-Aldrich, 144169) and guaiacol (1 g/L) (Sigma-Aldrich, W253200) in ethanol (Fisher Chemical, E/0650DF/C17). Each sample was incubated at 60 °C in the autosampler oven with constant agitation. After 5 min equilibration, the SPME fiber was exposed to the sample headspace for 30 min. The compounds trapped on the fiber were thermally desorbed in the injection port of the chromatograph by heating the fiber for 15 min at 270 °C.

The GC-MS was equipped with a low polarity RXi-5Sil MS column (length, 20 m; internal diameter, 0.18 mm; layer thickness, 0.18 µm; Restek, Bellefonte, PA, USA). Injection was performed in splitless mode at 320 °C, a split flow of 9 ml/min, a purge flow of 5 ml/min and an open valve time of 3 min. To obtain a pulsed injection, a programmed gas flow was used whereby the helium gas flow was set at 2.7 mL/min for 0.1 min, followed by a decrease in flow of 20 ml/min to the normal 0.9 mL/min. The temperature was first held at 30 °C for 3 min and then allowed to rise to 80 °C at a rate of 7 °C/min, followed by a second ramp of 2 °C/min till 125 °C and a final ramp of 8 °C/min with a final temperature of 270 °C.

Mass acquisition range was 33 to 550 amu at a scan rate of 5 scans/s. Electron impact ionization energy was 70 eV. The interface and ion source were kept at 275 °C and 250 °C, respectively. A mix of linear n-alkanes (from C7 to C40, Supelco Co.) was injected into the GC-MS under identical conditions to serve as external retention index markers. Identification and quantification of the compounds were performed using an in-house developed R script as described in Goelen et al. and Reher et al. 87 , 88 (for package information, see Supplementary Table  S8 ). Briefly, chromatograms were analyzed using AMDIS (v2.71) 89 to separate overlapping peaks and obtain pure compound spectra. The NIST MS Search software (v2.0 g) in combination with the NIST2017, FFNSC3 and Adams4 libraries were used to manually identify the empirical spectra, taking into account the expected retention time. After background subtraction and correcting for retention time shifts between samples run on different days based on alkane ladders, compound elution profiles were extracted and integrated using a file with 284 target compounds of interest, which were either recovered in our identified AMDIS list of spectra or were known to occur in beer. Compound elution profiles were estimated for every peak in every chromatogram over a time-restricted window using weighted non-negative least square analysis after which peak areas were integrated 87 , 88 . Batch effect correction was performed by normalizing against the most stable internal standard compound, 4-fluorobenzaldehyde. Out of all 284 target compounds that were analyzed, 167 were visually judged to have reliable elution profiles and were used for final analysis.

Discrete photometric and enzymatic analysis

Discrete photometric and enzymatic analysis (Thermo Scientific TM Gallery TM Plus Beermaster Discrete Analyzer) was used to measure acetic acid, ammonia, beta-glucan, iso-alpha acids, color, sugars, glycerol, iron, pH, protein, and sulfite. 2 ml of sample volume was used for the analyses. Information regarding the reagents and standard solutions used for analyses and calibrations is included in Supplementary Table  S7 and Supplementary Table  S9 .

NIR analyses

NIR analysis (Anton Paar Alcolyzer Beer ME System) was used to measure ethanol. Measurements comprised 50 ml of sample, and a 10% EtOH solution was used for calibration.

Correlation calculations

Pairwise Spearman Rank correlations were calculated between all chemical properties.

Sensory dataset

Trained panel.

Our trained tasting panel consisted of volunteers who gave prior verbal informed consent. All compounds used for the validation experiment were of food-grade quality. The tasting sessions were approved by the Social and Societal Ethics Committee of the KU Leuven (G-2022-5677-R2(MAR)). All online reviewers agreed to the Terms and Conditions of the RateBeer website.

Sensory analysis was performed according to the American Society of Brewing Chemists (ASBC) Sensory Analysis Methods 90 . 30 volunteers were screened through a series of triangle tests. The sixteen most sensitive and consistent tasters were retained as taste panel members. The resulting panel was diverse in age [22–42, mean: 29], sex [56% male] and nationality [7 different countries]. The panel developed a consensus vocabulary to describe beer aroma, taste and mouthfeel. Panelists were trained to identify and score 50 different attributes, using a 7-point scale to rate attributes’ intensity. The scoring sheet is included as Supplementary Data  3 . Sensory assessments took place between 10–12 a.m. The beers were served in black-colored glasses. Per session, between 5 and 12 beers of the same style were tasted at 12 °C to 16 °C. Two reference beers were added to each set and indicated as ‘Reference 1 & 2’, allowing panel members to calibrate their ratings. Not all panelists were present at every tasting. Scores were scaled by standard deviation and mean-centered per taster. Values are represented as z-scores and clustered by Euclidean distance. Pairwise Spearman correlations were calculated between taste and aroma sensory attributes. Panel consistency was evaluated by repeating samples on different sessions and performing ANOVA to identify differences, using the ‘stats’ package (v4.2.2) in R (for package information, see Supplementary Table  S8 ).

Online reviews from a public database

The ‘scrapy’ package in Python (v3.6) (for package information, see Supplementary Table  S8 ). was used to collect 232,288 online reviews (mean=922, min=6, max=5343) from RateBeer, an online beer review database. Each review entry comprised 5 numerical scores (appearance, aroma, taste, palate and overall quality) and an optional review text. The total number of reviews per reviewer was collected separately. Numerical scores were scaled and centered per rater, and mean scores were calculated per beer.

For the review texts, the language was estimated using the packages ‘langdetect’ and ‘langid’ in Python. Reviews that were classified as English by both packages were kept. Reviewers with fewer than 100 entries overall were discarded. 181,025 reviews from >6000 reviewers from >40 countries remained. Text processing was done using the ‘nltk’ package in Python. Texts were corrected for slang and misspellings; proper nouns and rare words that are relevant to the beer context were specified and kept as-is (‘Chimay’,’Lambic’, etc.). A dictionary of semantically similar sensorial terms, for example ‘floral’ and ‘flower’, was created and collapsed together into one term. Words were stemmed and lemmatized to avoid identifying words such as ‘acid’ and ‘acidity’ as separate terms. Numbers and punctuation were removed.

Sentences from up to 50 randomly chosen reviews per beer were manually categorized according to the aspect of beer they describe (appearance, aroma, taste, palate, overall quality—not to be confused with the 5 numerical scores described above) or flagged as irrelevant if they contained no useful information. If a beer contained fewer than 50 reviews, all reviews were manually classified. This labeled data set was used to train a model that classified the rest of the sentences for all beers 91 . Sentences describing taste and aroma were extracted, and term frequency–inverse document frequency (TFIDF) was implemented to calculate enrichment scores for sensorial words per beer.

The sex of the tasting subject was not considered when building our sensory database. Instead, results from different panelists were averaged, both for our trained panel (56% male, 44% female) and the RateBeer reviews (70% male, 30% female for RateBeer as a whole).

Beer price collection and processing

Beer prices were collected from the following stores: Colruyt, Delhaize, Total Wine, BeerHawk, The Belgian Beer Shop, The Belgian Shop, and Beer of Belgium. Where applicable, prices were converted to Euros and normalized per liter. Spearman correlations were calculated between these prices and mean overall appreciation scores from RateBeer and the taste panel, respectively.

Pairwise Spearman Rank correlations were calculated between all sensory properties.

Machine learning models

Predictive modeling of sensory profiles from chemical data.

Regression models were constructed to predict (a) trained panel scores for beer flavors and quality from beer chemical profiles and (b) public reviews’ appreciation scores from beer chemical profiles. Z-scores were used to represent sensory attributes in both data sets. Chemical properties with log-normal distributions (Shapiro-Wilk test, p  <  0.05 ) were log-transformed. Missing chemical measurements (0.1% of all data) were replaced with mean values per attribute. Observations from 250 beers were randomly separated into a training set (70%, 175 beers) and a test set (30%, 75 beers), stratified per beer style. Chemical measurements (p = 231) were normalized based on the training set average and standard deviation. In total, three linear regression-based models: linear regression with first-order interaction terms (LR), lasso regression with first-order interaction terms (Lasso) and partial least squares regression (PLSR); five decision tree models, Adaboost regressor (ABR), Extra Trees (ET), Gradient Boosting regressor (GBR), Random Forest (RF) and XGBoost regressor (XGBR); one support vector machine model (SVR) and one artificial neural network model (ANN) were trained. The models were implemented using the ‘scikit-learn’ package (v1.2.2) and ‘xgboost’ package (v1.7.3) in Python (v3.9.16). Models were trained, and hyperparameters optimized, using five-fold cross-validated grid search with the coefficient of determination (R 2 ) as the evaluation metric. The ANN (scikit-learn’s MLPRegressor) was optimized using Bayesian Tree-Structured Parzen Estimator optimization with the ‘Optuna’ Python package (v3.2.0). Individual models were trained per attribute, and a multi-output model was trained on all attributes simultaneously.

Model dissection

GBR was found to outperform other methods, resulting in models with the highest average R 2 values in both trained panel and public review data sets. Impurity-based rankings of the most important predictors for each predicted sensorial trait were obtained using the ‘scikit-learn’ package. To observe the relationships between these chemical properties and their predicted targets, partial dependence plots (PDP) were constructed for the six most important predictors of consumer appreciation 74 , 75 .

The ‘SHAP’ package in Python (v0.41.0) was implemented to provide an alternative ranking of predictor importance and to visualize the predictors’ effects as a function of their concentration 68 .

Validation of causal chemical properties

To validate the effects of the most important model features on predicted sensory attributes, beers were spiked with the chemical compounds identified by the models and descriptive sensory analyses were carried out according to the American Society of Brewing Chemists (ASBC) protocol 90 .

Compound spiking was done 30 min before tasting. Compounds were spiked into fresh beer bottles, that were immediately resealed and inverted three times. Fresh bottles of beer were opened for the same duration, resealed, and inverted thrice, to serve as controls. Pairs of spiked samples and controls were served simultaneously, chilled and in dark glasses as outlined in the Trained panel section above. Tasters were instructed to select the glass with the higher flavor intensity for each attribute (directional difference test 92 ) and to select the glass they prefer.

The final concentration after spiking was equal to the within-style average, after normalizing by ethanol concentration. This was done to ensure balanced flavor profiles in the final spiked beer. The same methods were applied to improve a non-alcoholic beer. Compounds were the following: ethyl acetate (Merck KGaA, W241415), ethyl hexanoate (Merck KGaA, W243906), isoamyl acetate (Merck KGaA, W205508), phenethyl acetate (Merck KGaA, W285706), ethanol (96%, Colruyt), glycerol (Merck KGaA, W252506), lactic acid (Merck KGaA, 261106).

Significant differences in preference or perceived intensity were determined by performing the two-sided binomial test on each attribute.

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

The data that support the findings of this work are available in the Supplementary Data files and have been deposited to Zenodo under accession code 10653704 93 . The RateBeer scores data are under restricted access, they are not publicly available as they are property of RateBeer (ZX Ventures, USA). Access can be obtained from the authors upon reasonable request and with permission of RateBeer (ZX Ventures, USA).  Source data are provided with this paper.

Code availability

The code for training the machine learning models, analyzing the models, and generating the figures has been deposited to Zenodo under accession code 10653704 93 .

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We thank all lab members for their discussions and thank all tasting panel members for their contributions. Special thanks go out to Dr. Karin Voordeckers for her tremendous help in proofreading and improving the manuscript. M.S. was supported by a Baillet-Latour fellowship, L.C. acknowledges financial support from KU Leuven (C16/17/006), F.A.T. was supported by a PhD fellowship from FWO (1S08821N). Research in the lab of K.J.V. is supported by KU Leuven, FWO, VIB, VLAIO and the Brewing Science Serves Health Fund. Research in the lab of T.W. is supported by FWO (G.0A51.15) and KU Leuven (C16/17/006).

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These authors contributed equally: Michiel Schreurs, Supinya Piampongsant, Miguel Roncoroni.

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VIB—KU Leuven Center for Microbiology, Gaston Geenslaan 1, B-3001, Leuven, Belgium

Michiel Schreurs, Supinya Piampongsant, Miguel Roncoroni, Lloyd Cool, Beatriz Herrera-Malaver, Florian A. Theßeling & Kevin J. Verstrepen

CMPG Laboratory of Genetics and Genomics, KU Leuven, Gaston Geenslaan 1, B-3001, Leuven, Belgium

Leuven Institute for Beer Research (LIBR), Gaston Geenslaan 1, B-3001, Leuven, Belgium

Laboratory of Socioecology and Social Evolution, KU Leuven, Naamsestraat 59, B-3000, Leuven, Belgium

Lloyd Cool, Christophe Vanderaa & Tom Wenseleers

VIB Bioinformatics Core, VIB, Rijvisschestraat 120, B-9052, Ghent, Belgium

Łukasz Kreft & Alexander Botzki

AB InBev SA/NV, Brouwerijplein 1, B-3000, Leuven, Belgium

Philippe Malcorps & Luk Daenen

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S.P., M.S. and K.J.V. conceived the experiments. S.P., M.S. and K.J.V. designed the experiments. S.P., M.S., M.R., B.H. and F.A.T. performed the experiments. S.P., M.S., L.C., C.V., L.K., A.B., P.M., L.D., T.W. and K.J.V. contributed analysis ideas. S.P., M.S., L.C., C.V., T.W. and K.J.V. analyzed the data. All authors contributed to writing the manuscript.

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Schreurs, M., Piampongsant, S., Roncoroni, M. et al. Predicting and improving complex beer flavor through machine learning. Nat Commun 15 , 2368 (2024). https://doi.org/10.1038/s41467-024-46346-0

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Unlocking insights into marketing to mature consumers: A new research synthesis

by Queen Mary, University of London

old customer

Research from Queen Mary University of London academic Dr. Mina Tajvidi, delves into marketing communications targeted at mature consumers aged 50 and above, addressing definitional inconsistencies and reviewing research published since 1972.

In an era marked by the increasing significance of an aging population, understanding and effectively targeting mature consumers has become paramount for marketers worldwide. A research paper titled "What we do know and don't know about marketing communications on mature consumers" sheds light on this crucial demographic, resolving long-standing definitional inconsistencies and providing a roadmap for future research and practice in marketing communications.

Authored by a team of esteemed scholars, this paper offers a comprehensive synthesis of existing research on marketing to mature consumers, encompassing individuals aged 50 and above. Drawing from a meticulous analysis of 106 papers published in premier marketing journals since 1972, the study identifies key themes and unveils critical insights into this demographic.

The findings of the paper underscore three primary research themes: the segmentation of mature consumers, their attitudes and behaviors, and effective marketing strategies tailored to this demographic. Furthermore, the research outlines a series of compelling future research directions, urging scholars and practitioners to delve deeper into understanding the complexities of mature consumer behavior and preferences.

One of the standout contributions of this paper is its proposal for an expanded definition of mature consumers, transcending mere chronological age to encompass biological, psychological, and social dimensions, as well as life events and circumstances. This holistic approach promises to revolutionize how marketers conceptualize and engage with this diverse demographic.

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The findings are published in the European Journal of Marketing .

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  1. Nanotechnology: Research Examples and How to Get Into the Field

  2. Nanotechnology: A New Frontier

  3. What is nanotechnology?

  4. Nanotechnology Expert Explains One Concept in 5 Levels of Difficulty

  5. Introduction: What is Nanotechnology?

  6. Nanotechnology 2.0


  1. Nanotechnology: A Revolution in Modern Industry

    Abstract. Nanotechnology, contrary to its name, has massively revolutionized industries around the world. This paper predominantly deals with data regarding the applications of nanotechnology in the modernization of several industries. A comprehensive research strategy is adopted to incorporate the latest data driven from major science platforms.

  2. Nanotechnology for a Sustainable Future: Addressing Global Challenges

    Nanotechnology is one of the most promising key enabling technologies of the 21st century. The field of nanotechnology was foretold in Richard Feynman's famous 1959 lecture "There's Plenty of Room at the Bottom", and the term was formally defined in 1974 by Norio Taniguchi. Thus, the field is now approaching 50 years of research and application. It is a continuously expanding area of ...

  3. Research articles

    Read the latest Research articles from Nature Nanotechnology. ... Nature Nanotechnology (Nat. Nanotechnol.) ISSN 1748-3395 (online) ISSN 1748-3387 (print) nature.com sitemap ...

  4. Nanoscience and technology

    News & Views 28 Mar 2024 Nature Nanotechnology. P: 1-2. Controlling the STING pathway to improve immunotherapy. ... Research Open Access 30 Mar 2024 Nature Communications. Volume: 15, P: 2773.

  5. A review on nanotechnology: Properties, applications, and mechanistic

    Nanotechnology is a relatively new field of science and technology that studies tiny objects (0.1-100 nm). Due to various positive attributes displayed by the biogenic synthesis of nanoparticles (NPs) such as cost-effectiveness, none to negligible environmental hazards, and biological reduction served as an attractive alternative to its counterpart chemical methods.

  6. Nature Nanotechnology

    Nature Nanotechnology offers a unique mix of news and reviews alongside top-quality research papers. Published monthly, in print and online, the journal reflects the entire spectrum of ...

  7. Nanomaterials: a review of synthesis methods, properties, recent

    a Center of Research Excellence in Desalination & Water Treatment, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia ... This review discusses a brief history of nanomaterials and their use throughout history to trigger advances in nanotechnology development. In particular, we describe and define various terms relating ...

  8. systematic review of nanotechnology for electric vehicles battery

    From 1991 to July 2023, this analysis uncovered 2361 publications concerning EVs, nanotechnology, nanomaterials and sustainable development. We have 2322 articles remaining after eliminating concise surveys, notes, erratum editorials and letters. After publication status-based exclusions of research papers, 2309 remained.

  9. Journal of Nanotechnology

    05 Jan 2024. 29 Dec 2023. 23 Dec 2023. Journal of Nanotechnology publishes papers related to the science and technology of nanosized and nanostructured materials, with emphasis on their design, characterization, functionality, and preparation for implementation in systems and devices.

  10. Applications of nanotechnology in medical field: a brief review

    For carrying out this study, relevant papers on Nanotechnology in the medical field from Scopus, Google scholar, ResearchGate, and other research platforms are identified and studied. The study discusses different types of Nanoparticles used in the medical field. This paper discusses nanotechnology applications in the medical field.

  11. Nanoparticles: Properties, applications and toxicities

    Nanotechnology is a known field of research since last century. Since "nanotechnology" was presented by Nobel laureate Richard P. Feynman during his well famous 1959 lecture "There's Plenty of Room at the Bottom" (Feynman, 1960), there have been made various revolutionary developments in the field of nanotechnology.Nanotechnology produced materials of various types at nanoscale level.

  12. (PDF) Nanotechnology: A Review

    This review paper look into the present aspects of "Nanotechnology". It gives a brief description about Nanotechnology and its application in various fields viz. medicine, computing, Robotics ...

  13. (PDF) Future of Nanotechnology

    This paper introduces recent trends in nanotechnology and its future scope. It also addresses known barriers to future progress in nanotechnology and its applications. Discover the world's research

  14. Nanotechnology from lab to industry

    2. Nanotechnology developments The booming global nanotechnology market is projected to exceed US$ 125 billion by 2024. 8 The commercialization of research outcomes resulting from the synthesis and application of nanotechnology therefore not only bears significant potential for benefit to society through their various applications but is profitable. As a result, nanotechnology is attracting ...

  15. Clean Water through Nanotechnology: Needs, Gaps, and Fulfillment

    Sustainable nanotechnology has made substantial contributions in providing contaminant-free water to humanity. In this Review, we present the compelling need for providing access to clean water through nanotechnology-enabled solutions and the large disparities in ensuring their implementation. We also discuss the current nanotechnology frontiers in diverse areas of the clean water space with ...

  16. Nanotechnology: A Revolution in Modern Industry

    Nanotechnology, contrary to its name, has massively revolutionized industries around the world. This paper predominantly deals with data regarding the applications of nanotechnology in the modernization of several industries. A comprehensive research strategy is adopted to incorporate the latest data driven from major science platforms. Resultantly, a broad-spectrum overview is presented which ...

  17. (PDF) What is nanotechnology?

    The paper reviewed twenty-one manuscripts on the applications of nanotechnology in water treatment, its efficiency and major challenges including original research studies and reviews.

  18. Frontiers

    Different global events such as industrial development and the population increment have triggered the presence and persistence of several organic and inorganic contaminants, representing a risk for the environment and human health. Consequently, the search and application of novel technologies for alleviating the challenge of environmental pollution are urgent. Nanotechnology is an emerging ...

  19. An Introduction to Nanotechnology

    Research and development in nanotechnology has allowed us to put man-made nanoscale objects into living cells [24]. Also, this technology has provided the possibility to investigate the microstructure and macrostructure of matter using molecular self-assembly. ... [32], who in 1974 presented a paper entitled "On the basic concept of ...

  20. A bibliometric analysis of the role of nanotechnology in dark

    A review article on nano-structured carbon's use as an electrode material in microbial fuel cells is the fifth paper. This research discusses how the conductive and stable properties of nanocarbons increase the efficiency of microbial fuel cells. According to reports, these structures have greater catalytic activity than typical Pt on carbon.

  21. Novel multiplexer, latch, and shift register in QCA nanotechnology for

    The current research goal is set to provide efficient design and a practical arrangement of the multiplexer, D-latch, and shift register in the quantum-dot cellular automata (QCA) technology. In the proposed multiplexer, including 14 cells, an area of 0.01 μ m 2 , a 0.5 clock cycle delay, and an energy consumption of 17.31 meV, the number of cells and energy consumption rates are reduced by 6 ...

  22. SV3D: Novel Multi-view Synthesis and 3D Generation from a Single Image

    We present Stable Video 3D (SV3D) -- a latent video diffusion model for high-resolution, image-to-multi-view generation of orbital videos around a 3D object. Recent work on 3D generation propose techniques to adapt 2D generative models for novel view synthesis (NVS) and 3D optimization. However, these methods have several disadvantages due to either limited views or inconsistent NVS, thereby ...

  23. Landmark IBM error correction paper on Nature cover

    This new paper is an important example of how we plan to continuously increasing the complexity (number of gates) of the quantum circuits that can be run on our hardware. It will allow us to transition from running circuits with 15,000 gates to 100 million, or even 1 billion gates.

  24. Nanotechnology and its use in imaging and drug delivery (Review)

    Nanoscience is the study of the unique properties of materials between 1-100 nm, and nanotechnology is the application of such research to create or modify novel objects. The ability to manipulate structures at the atomic scale allows for the creation of nanomaterials . Nanomaterials have unique optical, electrical and/or magnetic properties at ...

  25. Predicting and improving complex beer flavor through machine ...

    The beer was poured through two filter papers (Macherey-Nagel, 500713032 MN 713 ¼) to remove carbon dioxide and prevent spontaneous foaming. ... F.A.T. was supported by a PhD fellowship from FWO ...

  26. Research Assistant I, Nanotechnology

    Apply for the Job in Research Assistant I, Nanotechnology at Dallas, TX. View the job description, responsibilities and qualifications for this position. Research salary, company info, career paths, and top skills for Research Assistant I, Nanotechnology ... * Records and evaluates data obtained from work assignments for use in scientific papers.

  27. Unlocking insights into marketing to mature consumers: A new research

    Authored by a team of esteemed scholars, this paper offers a comprehensive synthesis of existing research on marketing to mature consumers, encompassing individuals aged 50 and above.