Impact of gas flowrate on performance of chemiresistive NO2 sensors
Source abstract
Abstract
Research Highlights • Effect of gas flowrates on performance of resistive sensors for NO2 is studied. • ZnO (metal oxide), 2D Tetrazine (polymer), and WS2 (TMD) were chosen for the study. • Tests were carried out at 100, 500, and 1000 sccm total flowrates. • The response of all the materials increased with increasing total gas flowrates. • The response times and recovery times of all the materials changed with flowrate. This paper investigates in-depth the practical implications and the underlying mechanism of the effect of gas flowrate on the sensing performances of chemiresistive sensors. Three different types of 2D sensing layers were chosen for this study − metal oxide (ZnO, synthesized using hydrothermal method), organic polymer (tetrazine, synthesized using modified Pinner method), and transition metal dichalcogenide (WS2, synthesized using liquid exfoliation technique). The 2D morphologies of all the materials were confirmed using a field emission scanning electron microscope. These materials have different crystallographic structures, bandgaps, and functional groups. Yet, the response of all of them for 2–10 ppm NO2 was found to increase with increasing total flowrates when tested at 100, 500, and 1000 sccm. The response of ZnO varied from 6 to 46 %, 93 to 521 %, and 117 to 1416 %, while that of 2D tetrazine was found to vary from − 5.3 to − 13.9 %, −8.1 to − 26.5 %, and − 8.9 to − 34.2 %, and for WS2, the response ranged from 0 %, −0.8 to − 5.2 %, and − 2 to − 12.2 % for 2 – 10 ppm NO2 at 100, 500, and 1000 sccm flowrates, respectively. The response times and the recovery times of all the materials, too, exhibited the effect of gas flowrate.
Source: expertdata · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Gas Sensing Nanomaterials and Sensors · Advanced Chemical Sensor Technologies · Analytical Chemistry and Sensors
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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