Type discrimination and concentration prediction towards ethanol using a machine learning–enhanced gas sensor array with different morphology-tuning characteristics-Reference-Cited by-同舟云学术

Type discrimination and concentration prediction towards ethanol using a machine learning–enhanced gas sensor array with different morphology-tuning characteristics

Published:2021 Issue:41 Volume:23 Page:23933-23944
ISSN:1463-9076
Container-title:Physical Chemistry Chemical Physics
language:en
Short-container-title:Phys. Chem. Chem. Phys.

Author:

Wang Tao¹,Ma Hongli¹,Jiang Wenkai¹,Zhang Hexin¹,Zeng Min¹,Yang Jianhua¹,Wang Xue²,Liu Ke²,Huang Renhua³,Yang Zhi¹^ORCID

Affiliation:

1. Key Laboratory of Thin Film and Microfabrication (Ministry of Education), Department of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, P. R. China

2. Department of Dermatology, Shanghai Ninth People's Hospital, Affiliated to Shanghai Jiao Tong University School of Medicine, Center for Specialty Strategy Research of Shanghai Jiao Tong University China Hospital Development Institute, Shanghai 200011, P. R. China

3. Department of Radiation, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200240, P. R. China

Abstract

Microwave-assisted method has been developed to synthesize ZnO gas sensing nanomaterials with controllable hierarchical structures. Machine learning algorithms such as PCA, SVM, ELM, and BP further improve the selectivity and quantitation.

Funder

National Natural Science Foundation of China

Shanghai Jiao Tong University

Publisher

Royal Society of Chemistry (RSC)

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Link

http://pubs.rsc.org/en/content/articlepdf/2021/CP/D1CP02394B

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