Rational Molecular Design for Non-aqueous Atomic Layer Deposition of Zinc Oxide

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

Machine Learning-Assisted Gas-Specific Fingerprint Detection/Classification Strategy Based on Mutually Interactive Features of Semiconductor Gas Sensor Arrays

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

Artificial Slanted Nanocilia Array as a Mechanotransducer for Controlling Cell Polarity

We present a method to induce cell directional behavior using slanted nanocilia arrays. NIH-3T3 fibroblasts demonstrated bidirectional polarization in a rectangular arrangement on vertical nanocilia arrays and exhibited a transition from a bidirectional to a unidirectional polarization pattern when the angle of the nanocilia was decreased from 90 degrees to 30 degrees. The slanted nanocilia guided and facilitated spreading by allowing the cells to contact the sidewalls of the nanocilia, and the directional migration of the cells opposed the direction of the slant due to the anisotropic bending stiffness of the slanted nanocilia. Although the cells recognized the underlying anisotropic geometry when the nanocilia were coated with fibronectin, collagen type I, and Matrigel, the cells lost their directionality when the nanocilia were coated with poly-D-lysine and poly-L-lysine. Furthermore, although the cells recognized geometrical anisotropy on fibronectin coatings, pharmacological perturbation of PI3K-Rac signaling hindered the directional elongation of the cells on both the slanted and vertical nanocilia. Furthermore, myosin light chain II was required for the cells to obtain polarized morphologies. These results indicated that the slanted nanocilia array provided anisotropic contact guidance cues to the interacting cells. The polarization of cells was controlled through two steps: the recognition of underlying geometrical anisotropy and the subsequent directional spreading according to the guidance cues.clos