Quantitative prediction of ternary mixed gases based on an SnO<sub>2</sub> sensor array and an SSA-BP neural network model-Reference-Cited by-同舟云学术

Quantitative prediction of ternary mixed gases based on an SnO₂ sensor array and an SSA-BP neural network model

Published:2023 Issue:15 Volume:25 Page:10935-10945
ISSN:1463-9076
Container-title:Physical Chemistry Chemical Physics
language:en
Short-container-title:Phys. Chem. Chem. Phys.

Author:

Li Meihua¹²^ORCID,Gu Yunlong¹,Zhang Yunfan¹,Gao Xiaodong¹,Ge Shikun¹,Wei Guangfen¹²

Affiliation:

1. School of Information and Electronic Engineering, Shandong Technology and Business University, Yantai 264005, China

2. Key Laboratory of Sensing Technology and Control in Universities of Shandong, Shandong Technology and Business University, Yantai 264005, China

Abstract

Gas sensor arrays have been prepared using tin oxide gas sensing materials synthesized by biotemplate method and hydrothermal reaction, combined with neural network algorithms to predict the concentration of gas mixtures.

Funder

Natural Science Foundation of Shandong Province

National Natural Science Foundation of China

Publisher

Royal Society of Chemistry (RSC)

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Link

http://pubs.rsc.org/en/content/articlepdf/2023/CP/D2CP04396C

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5. MoS2-Templated Porous Hollow MoO3 Microspheres for Highly Selective Ammonia Sensing via a Lewis Acid-Base Interaction

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