Chemiresistive sensor array for quantitative prediction of CO and NO2 gas concentrations in their mixture using machine learning algorithms

IF 5.3 2区 化学 Q1 CHEMISTRY, ANALYTICAL
Venkata Ramesh Naganaboina, Soumya Jana, Shiv Govind Singh
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引用次数: 0

Abstract

Single sensors have been developed for specific gas detection in real-time environments, but their selectivity is limited by interference from other gases when considering mixtures of gases. Consequently, accurate detection of target gases in mixed gas environments is essential. Therefore, this study develops a sensor array approach to quantitatively estimate the concentration of carbon monoxide (CO) and nitrogen dioxide (NO2) gases in their binary mixture (CO and NO2). The sensor array consists of two different sensors, developed with zinc oxide and graphene-cobalt sulfide. The sensor array was tested in the presence of 29 different proportions of the binary mixture at room temperature. Subsequently, machine learning (ML) algorithms are applied on sensor signals to estimate the concentration of gases. The ML models unfortunately exhibited inaccurate prediction when all sensor signals were considered, therefore, to improve the prediction accuracy, the sensor signals were divided into three levels based on the mixed gas concentration regime. Interestingly, the classification and regression algorithms provided good classification accuracy (85.13 ± 3.2%) and reasonable predictions of target gas concentrations at three levels. The proposed computational framework can be extended to include additional gases in mixed gases and used in various applications, including automotive, industrial, and environmental monitoring.

Graphical abstract

利用机器学习算法定量预测一氧化碳和二氧化氮混合气体浓度的化学电阻传感器阵列
目前已开发出用于在实时环境中检测特定气体的单一传感器,但在检测混合气体时,它们的选择性会受到其他气体干扰的限制。因此,在混合气体环境中准确检测目标气体至关重要。因此,本研究开发了一种传感器阵列方法,用于定量估计二元混合物(一氧化碳和二氧化氮)中一氧化碳(CO)和二氧化氮(NO2)气体的浓度。传感器阵列由两种不同的传感器组成,分别采用氧化锌和石墨烯-硫化钴研制而成。在室温下,传感器阵列在 29 种不同比例的二元混合物中进行了测试。随后,在传感器信号上应用机器学习(ML)算法来估计气体浓度。遗憾的是,当考虑到所有传感器信号时,ML 模型显示出不准确的预测,因此,为了提高预测准确性,根据混合气体浓度体系将传感器信号分为三个等级。有趣的是,分类和回归算法提供了良好的分类精度(85.13 ± 3.2%),并对三个级别的目标气体浓度进行了合理预测。所提出的计算框架可扩展到混合气体中的其他气体,并可用于汽车、工业和环境监测等各种应用中。 图表摘要
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来源期刊
Microchimica Acta
Microchimica Acta 化学-分析化学
CiteScore
9.80
自引率
5.30%
发文量
410
审稿时长
2.7 months
期刊介绍: As a peer-reviewed journal for analytical sciences and technologies on the micro- and nanoscale, Microchimica Acta has established itself as a premier forum for truly novel approaches in chemical and biochemical analysis. Coverage includes methods and devices that provide expedient solutions to the most contemporary demands in this area. Examples are point-of-care technologies, wearable (bio)sensors, in-vivo-monitoring, micro/nanomotors and materials based on synthetic biology as well as biomedical imaging and targeting.
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