Abstract

Developing a multi-analyte gas sensing system that simultaneously detects trace levels of CO and NO2 at low temperatures is necessary for the Internet of Things (IoT) based air quality monitoring applications. Nevertheless, gas sensors operating at low temperatures are nonspecific and rarely detect target gases at lower ppb levels in the air. Herein, an array of two SnS2 sensors with different bias voltages has been developed and characterized upon exposure to individual and binary mixtures of CO and NO2 gases at different concentrations. The developed gas sensors array achieved the lower detection limit of 45 ppb for NO2 and 150 ppb for CO. Further, co-adsorption-induced interaction analysis was carried out to predict the target gas concentration in the binary mixture using the mixed gas response. The mean absolute percentage error of 7.86% is observed in predicting the target gas concentrations in the binary mixture, which indicates the high prediction accuracy of proposed method. As a minimal resource intensive approach, the proposed method can be used in air quality monitoring applications that require low-power and low-cost sensors.

Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.