Modeling a multiple-chain emeraldine gas sensor for NH3 and NO2 detection.

This paper describes atomistic device models of a multiple-chain polyaniline (PANI) gas sensing component, utilizing the non-equilibrium Green's functions formalism. The numerical results are compared with experimental data of ammonia and nitrogen dioxide detection. Multiple molecules of PANI in the form of emeraldine salt were studied with more than one absorbed molecule of ammonia or nitrogen dioxide. From the I-V characteristics of the system with and without adsorbed gas molecules for gas concentrations of 3, 6, 9, and 12 ppm, the effective resistance changes, (R - R 0)/R 0, were obtained and compared with experimental results. A good agreement with the measured values was obtained. In summary, PANI as emeraldine salt was numerically modeled for several adsorbed gas concentrations, several gas configurations, and different PANI molecule positions, including carrier hopping between them. The results are comparable to the experiment and show good properties for the application as gas sensor device for NH3 detection and rather good properties for NO2 detection.

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

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.

Gas-sensing behaviour of ZnO/diamond nanostructures.

Microstructured single- and double-layered sensor devices based on p-type hydrogen-terminated nanocrystalline diamond (NCD) films and/or n-type ZnO nanorods (NRs) have been obtained via a facile microwave-plasma-enhanced chemical vapour deposition process or a hydrothermal growth procedure. The morphology and crystal structure of the synthesized materials was analysed with scanning electron microscopy, X-ray diffraction measurements and Raman spectroscopy. The gas sensing properties of the sensors based on i) NCD films, ii) ZnO nanorods, and iii) hybrid ZnO NRs/NCD structures were evaluated with respect to oxidizing (i.e., NO2, CO2) and reducing (i.e., NH3) gases at 150 °C. The hybrid ZnO NRs/NCD sensor showed a remarkably enhanced NO2 response compared to the ZnO NRs sensor. Further, inspired by this special hybrid structure, the simulation of interaction between the gas molecules (NO2 and CO2) and hybrid ZnO NRs/NCD sensor was studied using DFT calculations.