High-Throughput Screening of Gas Sensor Materials for Decomposition Products of Eco-Friendly Insulation Medium by Machine Learning

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

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

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

In the electrical industry, there are many hazardous gases that pollute the environment and even jeopardize human health, so timely detection and effective control of these hazardous gases is of great significance. In this work, the gas-sensitive properties of Pd-modified g-C3N4 interface for each hazardous gas molecule were investigated from a microscopic viewpoint, taking the hazardous gases (CO, NOx) that may be generated in the power industry as the detection target. Then, the performance of Pd-modifiedg-C3N4 was evaluated for practical applications as a gas sensor material. Novelly, an unconventional means was designed to briefly predict the effect of humidity on the adsorption properties of this sensor material. The final results found that Pd-modified g-C3N4 is most suitable as a potential gas-sensitizing material for NO2 gas sensors, followed by CO. Interestingly, Pd-modified g-C3N4 is less suitable as a potential gas-sensitizing material for NO gas sensors, but has the potential to be used as a NO cleaner (adsorbent). Unconventional simulation explorations of humidity effects show that in practical applications Pd-modified g-C3N4 remains a promising material for gas sensing in specific humidity environments. This work reveals the origin of the excellent properties of Pd-modified g-C3N4 as a gas sensor material and provides new ideas for the detection and treatment of these three hazardous gases.

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

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

Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures

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

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

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