A chemiresistive sensor array based on polyaniline nanocomposites and machine learning classification

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68378271%3A_____%2F22%3A00566244" target="_blank" >RIV/68378271:_____/22:00566244 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21230/22:00358000
Result on the web
<a href="https://hdl.handle.net/11104/0337633" target="_blank" >https://hdl.handle.net/11104/0337633</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3762/bjnano.13.34" target="_blank" >10.3762/bjnano.13.34</a>

Result language
angličtina
Original language name
A chemiresistive sensor array based on polyaniline nanocomposites and machine learning classification
Original language description
The selective detection of ammonia (NH3), nitrogen dioxide (NO2), carbon oxides (CO2 and CO), acetone ((CH3)2CO), and toluene (C6H5CH3) is investigated by means of a gas sensor array based on polyaniline nanocomposites. The array composed by seven different conductive sensors with composite sensing layers are measured and analyzed using machine learning. Statistical tools, such as principal component analysis and linear discriminant analysis, are used as dimensionality reduction methods. Five different classification methods, namely k-nearest neighbors algorithm, support vector machine, random forest, decision tree classifier, and Gaussian process classification (GPC) are compared to evaluate the accuracy of target gas determination. We found the Gaussian process classification model trained on features extracted from the data by principal component analysis to be a highly accurate method reach to 99% of the classification of six different gases.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10302 - Condensed matter physics (including formerly solid state physics, supercond.)

Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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