Selective denoising autoencoder for classification of noisy gas mixtures using 2D transition metal dichalcogenides

Electronic nose (E-nose) technology, which is composed of an array of chemical sensors and pattern recognition, has been widely utilized for the quantitative classification of gas mixtures. However, for the practical use of E-nose in real-industry, advanced algorithms are necessary to handle the noise in sensing data caused by various environmental variables. In order to achieve precise measurements even in real-world environments, it is necessary to denoise and classify noisy sensing data. To address these challenges, we have developed a novel deep learning approach, called selective denoising autoencoder (SDAE), which intelligently leverages both clean and noisy data gathered from real-world environments for mixed gas classification. Two-dimensional transition metal dichalcogenides (2D TMDCs) were used for the sensing channel. Raman spectroscopy, X-ray photoelectron spectroscopy, and scanning electron microscopy were used to characterize the TMDC sensing channel. Additionally, we conducted an analysis of the gas sensing properties towards NO₂, NH₃, and their mixtures (at ratios of 1:1, 1:2, and 2:1), and performed gas classification using our proposed SDAE model. The result achieved more than 95 % accuracy in all cases even in the noisy environment, which could be practically utilized in the industry.