Nanowire-Based Sensor Array for Detection of Cross-Sensitive Gases Using PCA and Machine Learning Algorithms

• Published in: IEEE sensors journal Vol. 20; no. 11; pp. 6020 - 6028
• Main Authors: Khan, Md Ashfaque Hossain, Thomson, Brian, Debnath, Ratan, Motayed, Abhishek, Rao, Mulpuri V.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Classification; Computer simulation; Copper; cross-sensitivity; Datasets; Decision trees; Density functional theory; Ethanol; First principles; Gallium nitride; Gas detectors; gas sensor; Gas sensors; Gases; Industry standards; Light emitting diodes; Machine learning; Metal oxides; Model accuracy; Nanowires; Nitrogen dioxide; Platinum; Power consumption; Power management; Principal component analysis; principal component analysis (PCA); Principal components analysis; Room temperature; Sensor array; Sensor arrays; Sensors; Silver; Support vector machines; Ultraviolet radiation
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2020.2972542

In this work, a gas sensor array has been designed and developed comprising of Pt, Cu and Ag decorated TiO 2 and ZnO functionalized GaN nanowires using industry standard top-down fabrication approach. The receptor metal/metal-oxide combinations within the array have been determined from our prior molecular simulation results using first principle calculations based on density functional theory (DFT). The gas sensing data was collected for both singular and mixture of NO 2 , ethanol, SO 2 and H 2 in presence of H 2 O and O 2 gases under UV light at room temperature. Each gas produced a unique response pattern across the sensors within the array by which precise identification of cross-sensitive gases is possible. After pre-processing of raw data, unsupervised principal component analysis (PCA) technique was applied on the array response. It is found that, each analyte gas forms a separate cluster in the score plot for all the target gases and their mixtures, indicating a clear discrimination among them. Then, four supervised machine learning algorithms such as- Decision Tree, Support Vector Machine (SVM), Naive Bayes (kernel) and k-Nearest Neighbor (k-NN) were trained and optimized using their significant parameters with our array dataset for the classification of gas type. Results indicate that the optimized SVM and NB classifier models exhibited 100% classification accuracy on test dataset. Practical applicability of the considered algorithms has been discussed as well. Moreover, this array device works at room-temperature using very low power and low-cost UV light-emitting diode (LED) as compared to high power consuming commercially available metal-oxide sensors.