Solid oxide fuel cells (SOFCs) have been recognized as one of the powerful next-generation energy conversion systems in association of the demanding green carbon technology. The electrochemical performance is crucially dependent on the intricate microstructures of porous electrodes, either cathodes or anodes. The composite electrodes should be analyzed in the sophisticated manner by characterizing the microstructural parameters. The current work combines electron microscopy with machine learning, more specifically semantic segmentation. The semantic segmentation was synergistically combined with high volume of electron micrographs enabled by FIB-SEM which has been used for microstructural analyses in SOFCs. The Ni/YSZ anode composites were selected as a model system, by incorporating the third components, i.e., pores featured by porous electrodes. The semantic segmentation-predicted image separation was connected with the conventional linear intercept approach, leading to the automated extraction of microstructural parameters. The work reports an exemplary analysis results based on the machine learning-assisted microstructure characterization in SOFC composite materials, implying that the machine learning-assisted approach becomes an essential tool in coping with high volume of electron microscopy-generated image data.

A high-performance machine learning-assisted gas sensor strategy based on the integration of supervised and unsupervised learning with a gas-sensitive semiconductor metal oxide (SMO) gas sensor array is introduced. A 4-SMO sensor array was chosen as a test sensor system for detecting carbon monoxide (CO) and ethyl alcohol (C2H5OH) mixtures using 15 different combinations. Gas sensing detection/classification was performed with different numbers of gas sensor and machine learning algorithms. K-Means clustering was successfully employed to rationally identify the similarity features of targeted gases among 4 different groups, i.e., matrix gas, two single-component gases, and one two-gas mixture, based on only unlabeled voltage-based gas sensing information. Detailed classification was performed through a multitude of supervised algorithms, i.e., 2-layer artificial neural networks (ANNs), 4-layer deep neural networks (DNNs), 1-dimensional convolutional neural networks (1D CNNs), and 2-dimensional CNNs (2D CNNs). The numerical-based DNNs and image-based CNNs are shown to be excellent approaches for gas detection and classification, as indicated by the highest accuracy and lowest loss indicators. Through the analysis of the influence of the number of sensors on the arrayed gas sensor system, the application of machine learning methodology to an arrayed gas sensor system demonstrates four unique features, i.e., a data augmentation methodology, machine learning approach of combining K-means clustering and neural networks, and a systematic approach to optimized sensor combinations, potentially leading to the practical sensor networks based on chemical sensors. Even two SMO sensor combinations are shown to be highly effective in gas discrimination against diverse gas environments assisted through numeric-based DNNs and image-based 1D CNNs, overcoming the simple clustering proposed through the unsupervised K-means clustering.

Zinc oxide (ZnO) is a transparent wide band gap semiconductor material with various possible applications in form of thin films. Most previous studies on atomic layer deposition (ALD) of ZnO thin films utilized a few well-known processes with diethylzinc (DEZ) and counter-reactants such as H2O and O3. However, O3 and H2O reactants have relatively strong reactivity, so that they are not suitable for substrates sensitive to oxidation. Therefore, development of milder non-aqueous alternative ALD process for ZnO is highly desired. In this study, we introduce ALD of ZnO using alcohols with theoretically optimized molecular structure. To discover suitable alcohol reactants for ZnO ALD, reaction pathways and reactivity between various types of alcohol reactants with surface-ethyl groups were evaluated through density functional theory calculations. It was found that unsaturated allylic alcohols would have the lowest activation energy for ZnO ALD via an allylic rearrangement mechanism. Experimental, novel ALD processes for ZnO using DEZ with alcohol as oxygen sources are set up. Ethanol as a typical simple alcohol is compared to 2-methyl-3-buten-2-ol (MBO) as an alcohol with optimal molecular structure setup. ALD ZnO films using MBO showed processes and material properties comparable to those of H2O-ALD ZnO. ZnO thin films as transparent conducting oxide could be obtained, and device performances of thin-film transistors based on alcohol-ALD processes are evaluated.