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© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

In recent years, two-dimensional layered material MXene has attracted extensive attention in the fields of sensors due to its large specific surface area and rich active sites. So, we employed multilayer Ti3C2TX and SnO2 microspheres to prepare SnO2/MXene composites for enhancing gas-sensing properties of pristine SnO2. The composite was brushed on a microelectromechanical system (MEMS) platform to make resistance-type gas sensors with low power consumption. The gas-sensing results show that the SnO2/MXene sensor with the best composite ratio (SnO2: MXene mass ratio is 5:1, named SM-5) greatly improves gas sensitivity of SnO2 sensor, among which the sensitivity to ethanol gas is the highest. At the same time, the composite also speeds up the response recovery speed of the sensor. When the SM-5 sensor worked at its optimal temperature 230 °C, its response value to 10 ppm ethanol reaches 5.0, which is twice that of the pristine SnO2 sensor. Its response and recovery time are only 14 s and 26 s, respectively. The sensing mechanism of the composite is discussed according to the classical the space charge or depletion layer model. It is concluded that the Schottky barrier of composites and the metal properties of Ti3C2Tx are responsible for improvement of the gas-sensing properties of the composite.

Details

Title
The SnO2/MXene Composite Ethanol Sensor Based on MEMS Platform
Author
Wang, Chen  VIAFID ORCID Logo  ; Li, Runlong; Feng, Lingyan  VIAFID ORCID Logo  ; Xu, Jiaqiang  VIAFID ORCID Logo 
First page
109
Publication year
2022
Publication date
2022
Publisher
MDPI AG
e-ISSN
22279040
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2642367725
Copyright
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.