General Model for Predicting Response of Gas-Sensitive Materials to Target Gas Based on Machine Learning-Reference-Cited by-同舟云学术

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

Published:2024-04-20 Issue:5 Volume:9 Page:2509-2519
ISSN:2379-3694
Container-title:ACS Sensors
language:en
Short-container-title:ACS Sens.

Author:

Yang Zijiang¹,Sun Yujiao¹,Gao Shasha¹,Yu Qiuchen¹,Zhao Yizhe²,Huo Yumeng²,Wan Zixin²,Huang Sheng¹³^ORCID,Wang Yanyan²,Gu Xiuquan¹

Affiliation:

1. School of Materials Science and Physics, China University of Mining and Technology, Xuzhou 221116, Jiangsu, China

2. National Narcotics Laboratory Beijing Regional Center, Beijing 100164, China

3. School of Safety Engineering, China University of Mining and Technology, Xuzhou 221116, Jiangsu, China

Funder

National Natural Science Foundation of China

Beijing Municipal Human Resources and Social Security Bureau

Natural Science Fund Project in Jiangsu Province

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acssensors.4c00186

Reference84 articles.

1. Function and applications of gas sensors

2. Advanced Micro- and Nano-Gas Sensor Technology: A Review

3. Semiconductor Gas Sensors: Materials, Technology, Design, and Application

4. Nanostructured Gas Sensors for Medical and Health Applications: Low to High Dimensional Materials

5. Developments in gas sensor technology for hydrogen safety

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