Issue 25, 2023

High-performance metal-oxide gas sensors based on hierarchical core–shell ZnFe2O4 microspheres for detecting 2-chloroethyl ethyl sulfide

Abstract

Mustard gas, an erosive chemical agent, is primarily used as a chemical weapon, which seriously threatens human life and health. Therefore, detecting mustard gas and its simulant, 2-chloroethyl ethyl sulfide (2-CEES), is a very important task. As a binary metal oxide with a spinel structure, ZnFe2O4 is widely used for fabricating gas sensors because of its stable chemical structure and abundant oxygen vacancies. In this study, gas-sensing ZnFe2O4 microspheres with a hierarchical core–shell nanosheet structure were prepared via a simple one-step solvothermal method. Scanning electron microscopy, transmission electron microscopy, X-ray diffraction, X-ray photoelectron spectroscopy, and N2 adsorption analyses were performed to characterize the morphology, structure, and chemical composition of these microspheres. A gas sensor was fabricated from the as-synthesized material, and its gas sensing performance was evaluated, using 2-CEES as a target gas. The obtained ZnFe2O4-based sensor exhibited a high sensitivity of 9.07 to 1 ppm 2-CEES at the optimal working temperature of 250 °C. The sensor response and recovery times were 18 and 546 s, respectively, and its detection sensitivity of 2.87 achieved at a 2-CEES concentration of 0.01 ppm was within an acceptable range. Additionally, the sensor demonstrated sufficiently high 2-CEES selectivity, repeatability, and long-term stability.

Graphical abstract: High-performance metal-oxide gas sensors based on hierarchical core–shell ZnFe2O4 microspheres for detecting 2-chloroethyl ethyl sulfide

Article information

Article type
Paper
Submitted
24 Apr 2023
Accepted
25 May 2023
First published
25 May 2023

Anal. Methods, 2023,15, 3084-3091

High-performance metal-oxide gas sensors based on hierarchical core–shell ZnFe2O4 microspheres for detecting 2-chloroethyl ethyl sulfide

J. Yang, L. Yang, S. Cao, J. Yang, C. Yan, L. Zhang, Q. Huang and J. Zhao, Anal. Methods, 2023, 15, 3084 DOI: 10.1039/D3AY00627A

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