{"title":"Chemiresistive sensor array for quantitative prediction of CO and NO2 gas concentrations in their mixture using machine learning algorithms","authors":"Venkata Ramesh Naganaboina, Soumya Jana, Shiv Govind Singh","doi":"10.1007/s00604-024-06835-x","DOIUrl":null,"url":null,"abstract":"<div><p>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 (NO<sub>2</sub>) gases in their binary mixture (CO and NO<sub>2</sub>). 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.</p><h3>Graphical abstract</h3><div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":705,"journal":{"name":"Microchimica Acta","volume":"191 12","pages":""},"PeriodicalIF":5.3000,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Microchimica Acta","FirstCategoryId":"92","ListUrlMain":"https://link.springer.com/article/10.1007/s00604-024-06835-x","RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, ANALYTICAL","Score":null,"Total":0}
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.
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.