Machine-learning-aided identification of ethanol in humid air using zinc complex capped CsPbBr<sub>3</sub> resistive sensors

Wang, Yue; Wang, Xi; Zhang, Hui; Gao, Shasha; Xu, Wenjie; Zhao, Yulong; Jiao, Mingzhi; Huang, Sheng; Gu, Xiuquan

doi:10.1039/d3nj02357e

New J. Chem.

2023

DOI: 10.1039/d3nj02357e

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Machine-learning-aided identification of ethanol in humid air using zinc complex capped CsPbBr₃ resistive sensors

Yue Wang,

Xi Wang,

Hui Zhang

et al.

Abstract: In this work, a facile room-temperature (RT) solution-processing strategy was developed to synthesize perovskite CsPbBr3 quantum dots (QDs), which were then modified with Zn-based organic ligands for enhancing its ambient...

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2024

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Cited by 2 publications

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Machine Learning‐Assisted Research and Development of Chemiresistive Gas Sensors

Yuan,

Luo,

Meng

2024

Adv Eng Mater

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The traditional trial‐and‐error testing to develop high‐performance chemiresistive gas sensors is inefficient and fails to meet the high demand for sensors in various industries. Machine learning can address the limitations of trial‐and‐error testing and can be effectively utilized for enhancing, developing, and designing sensors. This review first discusses the prediction of critical mechanism parameters of gas‐sensitive materials by machine learning, including adsorption energy, band gap, thermal conductivity, and dielectric constant. Secondly, it proposes that machine learning can improve five performance indexes: selectivity, response/recovery time, stability, sensitivity, and accuracy. Machine learning also facilitates the development and structural design of gas‐sensitive new materials. In addition, the potential of machine learning to optimize the sensor arrays is investigated, including reducing the number of sensors, identifying the best array combination, and improving recognition and detection capabilities. Finally, this paper discusses the challenges and limitations of machine‐learning assisted chemiresistive gas sensors in practical applications and envisions their future development.This article is protected by copyright. All rights reserved.

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Machine Learning‐Assisted Research and Development of Chemiresistive Gas Sensors

Yuan,

Luo,

Meng

2024

Adv Eng Mater

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Rapid Discovery of Gas Response in Materials Via Density Functional Theory and Machine Learning

Gao,

Cheng,

Chen

et al. 2024

Energy & Environ Materials

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In this study, a framework for predicting the gas‐sensitive properties of gas‐sensitive materials by combining machine learning and density functional theory (DFT) has been proposed. The framework rapidly predicts the gas response of materials by establishing relationships between multisource physical parameters and gas‐sensitive properties. In order to prove its effectiveness, the perovskite Cs3Cu2I5 has been selected as the representative material. The physical parameters before and after the adsorption of various gases have been calculated using DFT, and then a machine learning model has been trained based on these parameters. Previous studies have shown that a single physical parameter alone is not enough to accurately predict the gas sensitivity of materials. Therefore, a variety of physical parameters have been selected for machine learning, and the final machine learning model achieved 92% accuracy in predicting gas sensitivity. It is important to note that although there have been no previous reports on the response of Cs3Cu2I5 to hydrogen sulfide, the resulting model predicts the gas response of H2S; it is subsequently confirmed experimentally. This method not only enhances the understanding of the gas sensing mechanism, but also has a universal nature, making it suitable for the development of various new gas‐sensitive materials.

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Machine-learning-aided identification of ethanol in humid air using zinc complex capped CsPbBr₃ resistive sensors

Abstract: In this work, a facile room-temperature (RT) solution-processing strategy was developed to synthesize perovskite CsPbBr3 quantum dots (QDs), which were then modified with Zn-based organic ligands for enhancing its ambient...

Cited by 2 publications

References 29 publications

Machine Learning‐Assisted Research and Development of Chemiresistive Gas Sensors

Machine Learning‐Assisted Research and Development of Chemiresistive Gas Sensors

Rapid Discovery of Gas Response in Materials Via Density Functional Theory and Machine Learning

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Machine-learning-aided identification of ethanol in humid air using zinc complex capped CsPbBr3 resistive sensors

Abstract: In this work, a facile room-temperature (RT) solution-processing strategy was developed to synthesize perovskite CsPbBr3 quantum dots (QDs), which were then modified with Zn-based organic ligands for enhancing its ambient...

Cited by 2 publications

References 29 publications

Machine Learning‐Assisted Research and Development of Chemiresistive Gas Sensors

Machine Learning‐Assisted Research and Development of Chemiresistive Gas Sensors

Rapid Discovery of Gas Response in Materials Via Density Functional Theory and Machine Learning

Contact Info

Product

Resources

About

Machine-learning-aided identification of ethanol in humid air using zinc complex capped CsPbBr₃ resistive sensors