Simultaneous Detection of CO and NO2 Gases using Interaction Analysis of SnS2 Sensor Array Response

Srinivasulu Kanaparthi, S. Singh
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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.
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利用 SnS2 传感器阵列响应的交互分析同时检测 CO 和 NO2 气体
开发一种能在低温条件下同时检测一氧化碳和二氧化氮痕量水平的多分析气体传感系统,对于基于物联网(IoT)的空气质量监测应用来说十分必要。然而,在低温下工作的气体传感器是非特异性的,很少能检测到空气中低ppb级的目标气体。在此,我们开发了由两个具有不同偏置电压的 SnS2 传感器组成的阵列,并在暴露于不同浓度的一氧化碳和二氧化氮的单独和二元混合物时对其进行了表征。所开发的气体传感器阵列的二氧化氮检测下限为 45 ppb,一氧化碳检测下限为 150 ppb。此外,还进行了共吸附诱导相互作用分析,利用混合气体响应来预测二元混合物中的目标气体浓度。在预测二元混合物中的目标气体浓度时,观察到的平均绝对百分比误差为 7.86%,这表明所提议的方法具有很高的预测精度。作为一种最小资源密集型方法,所提出的方法可用于需要低功耗和低成本传感器的空气质量监测应用中。
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