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An electronic nose using conductometric gas sensors based on P3HT doped with triflates for gas detection using computational techniques (PCA, LDA, and kNN)

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Abstract

This study presents the development of an electronic nose comprising eight homemade sensors with pure P3HT and doped with different materials. The objective is to electronically identify the gases exposed on these sensors and evaluate the accuracy of target gas classification. The resistance variation for each sensor is measured over time and the collected data were processed by three different identification techniques as following: principal component analysis (PCA), linear discriminate analysis (LDA), and nearest neighbor analysis (kNN). The merit factor for the analysis is the relative modulation of the resistance is very important and computationally gives different results. In addition, the fact that we have sensors made with innovative materials where the reproducibility of the response for the same material can be a constraint in the recognition. In contrast, we have shown that despite the lack of reproducibility for the same material on two different sensors and despite the instability during the ten last sec, we have good recognition rates and we can even say which algorithm is better. It is noted that the LDA is the most reliable and efficient method for gas classification with a prediction accuracy equal to 100%, whereas it reach 93.52% and 73.14% for PCA and kNN, respectively, for other techniques for 40% of training dataset and 60% of testing dataset.

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Acknowledgements

This work was in collaboration between the laboratory of microelectronics and instrumentation of the faculty of sciences of Monastir, Tunisia and the central laboratory of IEMN in Lille, France. In addition, the gas sensor devices used in this work are already fabricated and characterized and published in our paper entitled “Mildly doped polythiophene with triflates for molecular recognition [36].”

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Authors and Affiliations

  1. Department of Physics, Faculty of Sciences, University of Monastir, 5050, Monastir, Tunisia

    Aicha Boujnah, Aimen Boubaker & Adel Kalboussi

  2. University Lille, CNRS, Centrale Lille, University Polytechnique Hauts-de-France, UMR 8520 - IEMN, 59000, Lille, France

    Sébastien Pecqueur & Kamal Lmimouni

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  1. Aicha Boujnah
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  2. Aimen Boubaker
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  3. Sébastien Pecqueur
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  4. Kamal Lmimouni
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Contributions

AB contributed to methodology, formal analysis, investigation, writing of the original draft, writing, reviewing, & editing of the manuscript, and visualization. AB contributed to writing, reviewing, & editing of the manuscript, project administration, and funding acquisition. SP contributed to validation, formal analysis, investigation, writing, reviewing, & editing of the manuscript, and supervision. KL contributed to writing, reviewing, & editing of the manuscript and project administration. AK contributed to writing, reviewing, & editing of the manuscript, project administration, and funding acquisition.

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Correspondence to Aicha Boujnah.

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Boujnah, A., Boubaker, A., Pecqueur, S. et al. An electronic nose using conductometric gas sensors based on P3HT doped with triflates for gas detection using computational techniques (PCA, LDA, and kNN). J Mater Sci: Mater Electron 33, 27132–27146 (2022). https://doi.org/10.1007/s10854-022-09376-2

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