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

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/68407700:21230/22:00358000

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10302 - Condensed matter physics (including formerly solid state physics, supercond.)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Beilstein Journal of Nanotechnology

ISSN

2190-4286

e-ISSN

2190-4286

Svazek periodika

13

Číslo periodika v rámci svazku

April

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

13

Strana od-do

411-423

Kód UT WoS článku

000792480700001

EID výsledku v databázi Scopus

2-s2.0-85130803268