Wafer-Scale Si-Based Metal−Insulator−Semiconductor Photoanodes for Water Oxidation Fabricated Using Thin Film Reactions and Multiple-layer Electrodeposited Catalysts

Solar-driven photoelectrochemical (PEC) water splitting offers a promising and environmentally friendly route for the conversion of renewable solar energy to hydrogen gas. A crystalline Si absorber is especially attractive due to its moderate bandgap, high charge mobility, long carrier diffusion length, costeffectiveness, and scalability in manufacturing. To improve the stability of Si-based PEC cells in operation, metal−insulator− semiconductor (MIS) structures have been widely employed. In this work, we employ simple and highly scalable processes to fabricate high-performance, extremely stable Si-based MIS photoanodes, and demonstrate their application to the fabrication of wafer-scale photoanodes. Localized conduction paths formed via an Al/SiO2 thin-film reaction enable low-resistance charge extraction even through thick insulating layers, yielding photoanodes with excellent stability. To improve the efficiency, we demonstrate a twostep Ni/NiFe electrodeposition process to create efficient oxygen evolution reaction catalysts. The Ni/NiFe catalyst allows for a high Schottky barrier between Si and Ni, lowering the photoanode onset potential, while the NiFe surface layer improves the catalytic performance. An unassisted solar-driven water splitting system incorporating a wafer-scale photoanode and monocrystalline Si solar cells is demonstrated and yields a solar-to-hydrogen conversion efficiency of 6.9% under simulated AM 1.5G sunlight illumination.This research was primarily supported by the National Science Foundation (grant CBET-2109842). The authors acknowledge the use of the facilities and instrumentation supported by Texas Materials Institute, and by the National Science Foundation through the Center for Dynamics and Control of Materials: an NSF MRSEC under Cooperative Agreement nos. DMR-1720595 and DMR-2308817. This work was performed in part at the University of Texas Microelectronics Research Center, a member of the National Nanotechnology Coordinated Infrastructure (NNCI), which is supported by the National Science Foundation (grant ECCS-2025227).Center for Dynamics and Control of Material

Mechanisms of Visible Light Photocatalysis in N-Doped Anatase TiO2 with Oxygen Vacancies from GGA+U Calculations

We have systematically studied the photocatalytic mechanisms of nitrogen doping in anatase TiO2 using first-principles calculations based on density functional theory, employing Hubbard U (8.47 eV) on-site correction. The impurity formation energy, charge density, and electronic structure properties of TiO2 supercells containing substitutional nitrogen, interstitial nitrogen, or oxygen vacancies were evaluated to clarify the mechanisms under visible light. According to the formation energy, a substitutional N atom is better formed than an interstitial N atom, and the formation of an oxygen vacancy in N-doped TiO2 is easier than that in pure TiO2. The calculated results have shown that a significant band gap narrowing may only occur in heavy nitrogen doping. With light nitrogen doping, the photocatalysis under visible light relies on N-isolated impurity states. Oxygen vacancies existence in N-doped TiO2 can improve the photocatalysis in visible light because of a band gap narrowing and n-type donor states. These findings provide a reasonable explanation of the mechanisms of visible light photocatalysis in N-doped TiO2

Constructivist Perspective on Developing a Multidimensional Blended Teaching Model Fostering Deep Learning

To promote high-quality development of higher education, it is imperative to facilitate students’ transition from surface learning to deep learning. Compared with surface learning that focuses on rote memorization, deep learning emphasizes meaningful learning based on understanding and transfer. It involves three progressively advanced cognitive stages of knowing: "learning for understanding," "learning for application," and "learning for innovation," which ultimately enable the internalization, transfer, and creative application of knowledge. How to foster deep learning in students has been an urgent issue of higher education. This study, grounded in constructivist learning theory, explores a multidimensional blended teaching model fostering deep learning. It also develops an evaluation system assessing learning outcomes from the perspectives of ideological, political and moral education, knowledge, and competencies. We conducted an empirical study to test the effectiveness of this multidimensional blended teaching model. Findings will provide theoretical and practical implications for teaching reforms of similar courses

Real value prediction of protein solvent accessibility using enhanced PSSM features

<p>Abstract</p> <p>Background</p> <p>Prediction of protein solvent accessibility, also called accessible surface area (ASA) prediction, is an important step for tertiary structure prediction directly from one-dimensional sequences. Traditionally, predicting solvent accessibility is regarded as either a two- (exposed or buried) or three-state (exposed, intermediate or buried) classification problem. However, the states of solvent accessibility are not well-defined in real protein structures. Thus, a number of methods have been developed to directly predict the real value ASA based on evolutionary information such as position specific scoring matrix (PSSM).</p> <p>Results</p> <p>This study enhances the PSSM-based features for real value ASA prediction by considering the physicochemical properties and solvent propensities of amino acid types. We propose a systematic method for identifying residue groups with respect to protein solvent accessibility. The amino acid columns in the PSSM profile that belong to a certain residue group are merged to generate novel features. Finally, support vector regression (SVR) is adopted to construct a real value ASA predictor. Experimental results demonstrate that the features produced by the proposed selection process are informative for ASA prediction.</p> <p>Conclusion</p> <p>Experimental results based on a widely used benchmark reveal that the proposed method performs best among several of existing packages for performing ASA prediction. Furthermore, the feature selection mechanism incorporated in this study can be applied to other regression problems using the PSSM. The program and data are available from the authors upon request.</p

THE EFFECTS OF EXTERNAL LOAD ON LOWER EXTREMiTY ELECTROMYOGRAPHY AMPLITUDE DURING COUNTERMOVEMENT JUMP

The purpose of this study was to investigate the effects of different loads on the mean electromyography (EMG) amplitude of the gluteus maximus, biceps fernoris, vastus medialis, gastrocnemius, soleus, and tibialis anterior during the deceleration phase and the acceleration phase of the countermovement jumps (CMJ). Ten male physical education students performed different CMJs with and without an external load (0,2.5,5.0, 7.5, or 10.0 kg hold in arms). The results s h o w the amplitude of the gluteus maximus with load of 7.5 kg was higher than with load of 2.5 kg during the deceleration phase (p < .05), and the amplitude of the soleus with load of 10.0 kg was higher than with load of 2.5 kg during the acceleration phase (p < .05). It indicated that the activities of lower limb muscles were not influenced by the relative lower of external loading during CMJ