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Discrimination of binary mixture of toxic gases using ZnO nanowires-based E-nose

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Abstract

We report the utilization of ZnO nanowires (NWs)-based e-nose towards successful discrimination of binary gaseous mixture comprising H2S and NO2 gases. In particular, analysis of individual components in the binary mixture of gases has been carried out using different pattern recognition algorithms (PRA) or models. Of these, principal component analysis (PCA) indicated a successful discrimination of the gases. The maximum variance of three principal components were found to be 95.89, 3.53, and 0.56%, respectively. To cross validate the results, hierarchical cluster analysis (HCA) and linear discriminant analysis (LDA) studies have also been performed. Herein, by estimating the probability of the classes, an accurate prediction of the gases with minimal misclassification was achieved. Thus, using sequential application of the three basic PRAs on the data repository, a successful discrimination of the individual component of the binary mixture of gases was accomplished.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

KRS would like thank Council of Scientific and Industrial Research-University Grants Commission (CSIR-UGC) for the award of Senior Research Fellowship. BKB thanks CSIR for the award of Research Associateship (CSIR-RA).

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The authors have not disclosed any funding.

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Authors and Affiliations

  1. Technical Physics Division, Bhabha Atomic Research Center, Mumbai, 400 085, India

    K. R. Sinju, B. K. Bhangare, A. K. Debnath & Niranjan S. Ramgir

  2. Homi Bhabha National Institute, Anushaktinagar, Mumbai, 400 094, India

    K. R. Sinju, A. K. Debnath & Niranjan S. Ramgir

Authors
  1. K. R. Sinju
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  2. B. K. Bhangare
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  3. A. K. Debnath
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  4. Niranjan S. Ramgir
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Contributions

KRS: Conceptualization; Data curation; Formal analysis, Original draft; Methodology; Software; BKB: Data curation, Formal analysis; AKD: Supervision; Resources; NSR: Conceptualization; Methodology; Formal Analysis, Writing—review & editing, Supervision.

Corresponding author

Correspondence to Niranjan S. Ramgir.

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Sinju, K.R., Bhangare, B.K., Debnath, A.K. et al. Discrimination of binary mixture of toxic gases using ZnO nanowires-based E-nose. J Mater Sci: Mater Electron 34, 1562 (2023). https://doi.org/10.1007/s10854-023-10956-z

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