Zirconium oxides were deposited using plasma-enhanced atomic layer deposition (PEALD) involving (2-(N-methylamino)1-MethylEthyleneCyclopentadienyl)Bis(DiMethylAmino)Zr (abbreviated as CMEN-Zr) and oxygen plasma as zirconium and oxygen sources. The zirconium oxide thin films demonstrate temperature-independent growth rates per cycle of 0.94 A/cycle at 150–215 °C. The deposited ZrO₂ thin films were characterized using numerous analytical tools, i.e., X-ray photoelectron spectroscopy for chemical bonding state and composition, X-ray diffraction for crystallinity, atomic force microscopy for surface morphology, field-emission scanning electron microscopy for cross-sectional analysis, spectroscopic ellipsometry and UV–visible spectrophotometry for optical characterization, capacitance–voltage measurements for dielectric constants and atomic defects, and current–voltage characteristics for electrical information. The insulating features of the crystalline and stoichiometric ZrO₂ films were implemented in the anode composites to evaluate the influence of ALD-based nano-features on the electrochemical performance of solid oxide fuel cells, with the main emphasis on anode performance. The presence of nanomaterials on Ni/YSZ anode composites is analyzed to determine the negative effects on electrochemical performance and the degradation of cell performance of solid oxide fuel cells (SOFCs). The artificial design was proven to be effective in controlling the cell performance as long as proper material design was adopted in SOFC electrodes.

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.

In this work, loess-based materials were designed based on a multicomponent composite materials system for ecofriendly natural three-dimensional (3D) printing involving quick lime, gypsum, and water. The 3D printing process was monitored as a function of gypsum content; in terms of mechanical strength and electrical resistance, in the cube-shaped bulk form. After initial optimization, the 3D printing composition was refined to provide improved printability in a 3D printing system. The optimal 3D fabrication allowed for reproducible printing of rectangular columns and cubes. The development of 3D printing materials was scrutinized using a multitude of physicochemical probing tools, including X-ray diffraction for phase identification, impedance spectroscopy to monitor setting behaviors, and mercury intrusion porosimetry to extract the pore structure of loess-based composite materials. Additionally, the setting behavior in the loess-based composite materials was analyzed by investigating the formation of gypsum hydrates induced by chemical reaction between quick lime and water. This setting reaction provides reasonable mechanical strength that is sufficient to print loess-based pastes via 3D printing. Such mechanical strength allows utilization of robotic 3D printing applications that can be used to fabricate ecofriendly structures.