A real-time sharp selectivity with In2S3 gas sensor using a nonlinear dynamic response for VOCs. (November 2021)

• Record Type: Journal Article
• Authors: Bouricha, B.; Souissi, R.; Bouguila, N.; Labidi, A.
• Is Part Of: Measurement. Volume 185(2021)
• Publication Date: 2021-11
• Subjects: VOCs selectivity -- In2S3 sensing material -- Principal component analysis -- Hierarchical ascendant classification -- Factor loading; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2021.110070
• Languages: English
• ISSNs: 0263-2241

Abstract

Graphical abstract: PCA and HAC treatment. Highlights: Feature success of the current transient sensor in VOC discrimination. PCA and HCA have been deployed to represent variances in sample observations. The selection of characteristics in three subsets gave good results with the HAC. VOCs Tests show good numerical recognition sequence modeled for each vapor. Abstract: Selectivity continues to be one of the major challenges for gas sensors. This work reports on transient variations in the resistance of In2 S3 sensors following injection of different concentrations of ethanol, methanol, isopropanol, acetone and toluene at 350 °C operating temperature. Volatile Organic Compounds (VOCs) are recognized using the transient evolution of a single sensing element that offers different characteristics that can be direct or indirect signals detection of the target gas. Novel features for twenty individuals were processed by principal component analysis and hierarchical ascending classification, and four obvious clusters were observed. The output of the VOC detection sensor for the gaseous analyte is represented by the result of analyzes of three data matrices having 9, 7 and 8 dimensional vectors. Result is delivered with a sequence of three digits determined after global execution. Based on this approach, the distinction of one reducing gas from another is demonstrated.