Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures

Advanced Materials, Journal Year: 2024, Volume and Issue: 36(18)

Published: Jan. 19, 2024

Abstract Machine learning holds significant research potential in the field of nanotechnology, enabling nanomaterial structure and property predictions, facilitating materials design discovery, reducing need for time‐consuming labor‐intensive experiments simulations. In contrast to their achiral counterparts, application machine chiral nanomaterials is still its infancy, with a limited number publications date. This despite great advance development new sustainable high values optical activity, circularly polarized luminescence, enantioselectivity, as well analysis structural chirality by electron microscopy. this review, an methods used studying provided, subsequently offering guidance on adapting extending work nanomaterials. An overview within framework synthesis–structure–property–application relationships presented insights how leverage study these highly complex are provided. Some key recent reviewed discussed Finally, review captures achievements, ongoing challenges, prospective outlook very important field.

Language: Английский

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A Comparative Study of the Electronic Transport and Gas-Sensitive Properties of Graphene+, T-graphene, Net-graphene, and Biphenylene-Based Two-Dimensional Devices

ACS Sensors, Journal Year: 2023, Volume and Issue: 8(9), P. 3510 - 3519

Published: Aug. 10, 2023

The electronic transport properties of the four carbon isomers: graphene+, T-graphene, net-graphene, and biphenylene, as well gas-sensing to nitrogen-based gas molecules including NO2, NO, NH3 molecules, are systematically studied comparatively analyzed by combining density functional theory with nonequilibrium Green's function. isomers metallic, especially graphene+ being a Dirac metal due two cones present at Fermi energy level. two-dimensional devices based on these exhibit good conduction in order biphenylene > T-graphene net-graphene. More interestingly, net-graphene-based biphenylene-based demonstrate significant anisotropic properties. sensors above structures all have selectivity sensitivity NO2 molecule, among which T-graphene-based most prominent maximum ΔI value 39.98 μA, only three-fifths original. In addition, graphene+-based also sensitive NO molecule values 29.42 25.63 respectively. However, physically adsorbed for molecule. By adsorption energy, charge transfer, electron localization functions, molecular projection self-consistent Hamiltonian states, mechanisms behind can be clearly explained. This work shows potential detection toxic NO2.

Language: Английский

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Machine learning-assisted development of TMDs-type gas-sensitive materials for dissolved gases in oil-immersed transformer oils

Materials Today Chemistry, Journal Year: 2025, Volume and Issue: 44, P. 102583 - 102583

Published: Feb. 13, 2025

Language: Английский

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Cr2-modified g-CN interface: A novel gas sensitive material for decomposition products of green insulating gas CF3SO2F

Surfaces and Interfaces, Journal Year: 2024, Volume and Issue: 46, P. 104077 - 104077

Published: Feb. 17, 2024

Language: Английский

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General Model for Predicting Response of Gas-Sensitive Materials to Target Gas Based on Machine Learning

ACS Sensors, Journal Year: 2024, Volume and Issue: 9(5), P. 2509 - 2519

Published: April 20, 2024

Gas sensors play a crucial role in various industries and applications. In recent years, there has been an increasing demand for gas society. However, the current method screening gas-sensitive materials is time-, energy-, cost-consuming. Consequently, imperative exists to enhance efficiency. this study, we proposed collaborative strategy through integration of density functional theory machine learning. Taking zinc oxide (ZnO) as example, responsiveness ZnO target was determined quickly on basis changes electronic state structure before after adsorption. work, adsorption energy structural characteristics adsorbing 24 kinds gases were calculated. These computed features served training learning model. Subsequently, evaluation algorithms utilized train fast The importance feature values evaluated by AdaBoost, Random Forest, Extra Trees models. Specifically, charge transfer assigned 0.160, 0.127, 0.122, respectively, ranking highest among 11 features. Following closely d-band center, which presumed exert influence electrical conductivity and, consequently, properties. With 5-fold cross-validation using Tree accuracy, 24-sample data set achieved accuracy 88%. 72-sample 78% multilayer perceptron cross-validation, with both sets exhibiting low standard deviations. This verified reliability strategy, showcasing its potential rapidly material's gas.

Language: Английский

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Quantum-level investigation of air decomposed pollutants gas sensor (Pd-modified g-C3N4) influenced by micro-water content.

Chemosphere, Journal Year: 2024, Volume and Issue: 358, P. 142198 - 142198

Published: April 30, 2024

Language: Английский

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Pulsed Airstream‐Driven Hierarchical Micro‐Nano Pore Structured Triboelectric Nanogenerator for Wireless Self‐Powered Formaldehyde Sensing

Small, Journal Year: 2024, Volume and Issue: 20(47)

Published: Aug. 14, 2024

Abstract Formaldehyde (HCHO), as a common volatile organic compound, has serious impact on human health in the daily lives and industrial production scenarios. Given security issue of HCHO detection danger warning, ZIF‐8/copper foam based pulsed airstream‐driven triboelectric nanogenerator (ZCP‐TENG) is designed to develop self‐powered sensors. By combining contact electrification electrostatic induction, ZCP‐TENG can be utilized for airflow energy harvesting concentration detection. The short‐circuit current output power reach 2.0 µA 81 µW (20 ppm). With high surface area, abundant micro‐nano pores, excellent permeation flux, ZCP‐TENGs exhibit sensing response (61.3% at 100 ppm), low limit (≈2 rapid response/recovery time (14/15 s), which served highly sensitive selective sensor. connecting an intelligent wireless alarm, are construct warning system monitor remind exceedance situations. Moreover, by support vector machine model, difference concentrations quickly identified with average prediction accuracy 100%. This study illustrates that have broad application prospects provide guidance monitoring warnings.

Language: Английский

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Accelerated Screening of Highly Sensitive Gas Sensor Materials for Greenhouse Gases Based on DFT and Machine Learning Methods

ACS Sensors, Journal Year: 2025, Volume and Issue: 10(1), P. 563 - 572

Published: Jan. 6, 2025

Greenhouse gases (GHGs) have caused great harm to the ecological environment, so it is necessary screen gas sensor materials for detecting GHGs. In this study, we propose an ideal design strategy with high screening efficiency and low cost targeting four typical GHGs (CO2, CH4, N2O, SF6). This introduces machine learning (ML) methods based on density functional theory (DFT) achieve accurate rapid from a large number of candidate materials. Specifically, include 28 different transition metal-doped WSe2 monolayers (TM-WSe2), molecules their optimal adsorption structures TM-WSe2 are constructed. Ten fine-tuned ML models implemented train predict energy (Eads) distance (D) target TM-WSe2, thereby selecting model identifying these promising addition, gas-sensing properties verified by band structure, work function, recovery time. research provides reasonable low-cost new way help artificial intelligence proves its effectiveness through experiments.

Language: Английский

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Some of Our Favorite Papers from the First 10 Years of ACS Sensors

ACS Sensors, Journal Year: 2025, Volume and Issue: 10(1), P. 1 - 3

Published: Jan. 24, 2025

Language: Английский

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Navigating the Evolution of Carbon Nitride Research: Integrating Machine Learning into Conventional Approaches

Physical Chemistry Chemical Physics, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Carbon nitride research has reached a promising point in today's endeavours with diverse applications including photocatalysis, energy storage, and sensing due to their unique electronic structural properties. Recent advances machine learning (ML) have opened new avenues for exploring optimizing the potential of these materials. This study presents comprehensive review integration ML techniques carbon an introduction CN classifications recent advancements. We discuss methodologies employed, such as supervised learning, unsupervised reinforcement predicting material properties, synthesis conditions, enhancing performance metrics. Key findings indicate that algorithms can significantly reduce experimental trial-and-error, accelerate discovery processes, provide deeper insights into structure-property relationships nitride. The synergistic effect combining traditional approaches is highlighted, showcasing studies where driven models successfully predicted novel compositions enhanced functional Future directions this field are also proposed, emphasizing need high-quality datasets, advanced models, interdisciplinary collaborations fully realize materials next-generation technologies.

Language: Английский

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