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The electrical characterization and ammonia vapor (NH₃) response of a p‐Si/n‐poly[benzimidazobenzophenanthroline] (n‐BBL) thin‐film junction diode are reported. The presence of a depletion layer at the n‐BBL/p‐Si interface is verifiedviacapacitance–voltage measurements, and the built‐in potential is ≈1.8 V. Using the standard diode equation for data analysis, the turn‐on voltage, rectification ratio, and ideality parameter are found to be 2 V, 16, and 6, respectively. The diode is also tested in the presence of NH₃vapor where it retained its asymmetricJ–Vbehavior with increased currents and an insignificant change in device parameters. NH₃is believed to interact with the adsorbed O₂⁻species on the n‐BBL surface liberating electrons that enhance the diode current. The response time, recovery time, and sensitivity of the diode are 65 s, 121 s, and 52%, respectively. The removal of the gas restores the diode characteristics to their near original shape making it reusable. The diode is also electrically characterized as a function of temperature and is found to retain its rectifying behavior down to 150 K. The rectifying and gas‐sensing features make the diode multifunctional, which expands the range of applications of this ladder‐type conducting polymer.

 

Developing stable and efficient electrocatalysts is vital for boosting oxygen evolution reaction (OER) rates in sustainable hydrogen production. High-entropy oxides (HEOs) consist of five or more metal cations, providing opportunities to tune their catalytic properties toward high OER efficiency. This work combines theoretical and experimental studies to scrutinize the OER activity and stability for spinel-type HEOs. Density functional theory confirms that randomly mixed metal sites show thermodynamic stability, with intermediate adsorption energies displaying wider distributions due to mixing-induced equatorial strain in active metal-oxygen bonds. The rapid sol-flame method is employed to synthesize HEO, comprising five 3d-transition metal cations, which exhibits superior OER activity and durability under alkaline conditions, outperforming lower-entropy oxides, even with partial surface oxidations. The study highlights that the enhanced activity of HEO is primarily attributed to the mixing of multiple elements, leading to strain effects near the active site, as well as surface composition and coverage.

 

Electrochemical two-electron water oxidation reaction (2e-WOR) has drawn significant attention as a promising process to achieve the continuous on-site production of hydrogen peroxide (H₂O₂). However, compared to the cathodic H₂O₂generation, the anodic 2e-WOR is more challenging to establish catalysts due to the severe oxidizing environment. In this study, we combine density functional theory (DFT) calculations with experiments to discover a stable and efficient perovskite catalyst for the anodic 2e-WOR. Our theoretical screening efforts identify LaAlO₃perovskite as a stable, active, and selective candidate for catalyzing 2e-WOR. Our experimental results verify that LaAlO₃achieves an overpotential of 510 mV at 10 mA cm⁻²in 4 M K₂CO₃/KHCO₃, lower than those of many reported metal oxide catalysts. In addition, LaAlO₃maintains a stable H₂O₂Faradaic efficiency with only a 3% decrease after 3 h at 2.7 V vs. RHE. This computation-experiment synergistic approach introduces another effective direction to discover promising catalysts for the harsh anodic 2e-WOR towards H₂O₂.

 

Protein–protein interaction (PPI) is vital for life processes, disease treatment, and drug discovery. The computational prediction of PPI is relatively inexpensive and efficient when compared to traditional wet-lab experiments. Given a new protein, one may wish to find whether the protein has any PPI relationship with other existing proteins. Current computational PPI prediction methods usually compare the new protein to existing proteins one by one in a pairwise manner. This is time consuming.

In this work, we propose a more efficient model, called deep hash learning protein-and-protein interaction (DHL-PPI), to predict all-against-all PPI relationships in a database of proteins. First, DHL-PPI encodes a protein sequence into a binary hash code based on deep features extracted from the protein sequences using deep learning techniques. This encoding scheme enables us to turn the PPI discrimination problem into a much simpler searching problem. The binary hash code for a protein sequence can be regarded as a number. Thus, in the pre-screening stage of DHL-PPI, the string matching problem of comparing a protein sequence against a database withMproteins can be transformed into a much more simpler problem: to find a number inside a sorted array of lengthM. This pre-screening process narrows down the search to a much smaller set of candidate proteins for further confirmation. As a final step, DHL-PPI uses the Hamming distance to verify the final PPI relationship.

The experimental results confirmed that DHL-PPI is feasible and effective. Using a dataset with strictly negative PPI examples of four species, DHL-PPI is shown to be superior or competitive when compared to the other state-of-the-art methods in terms of precision, recall or F1 score. Furthermore, in the prediction stage, the proposed DHL-PPI reduced the time complexity from$$O(M^2)$$$O\left(M^2\right)$to$$O(M\log M)$$$O(MlogM)$for performing an all-against-all PPI prediction for a database withMproteins. With the proposed approach, a protein database can be preprocessed and stored for later search using the proposed encoding scheme. This can provide a more efficient way to cope with the rapidly increasing volume of protein datasets.

 

Electrochemical oxidation of water and electrolyte ions is a sustainable method for producing energy carriers and valuable chemicals. Among known materials for catalyzing oxidation reactions, titanium dioxide (TiO 2 ) offers excellent electrochemical stability but is less active than many other metal oxides. Herein, we used density functional theory calculations to predict an increase in catalytic activity by doping anatase TiO 2 with manganese atoms (Mn). We synthesized Mn-doped TiO 2 and then utilized X-ray absorption spectroscopy to study the chemical environment around the Mn site in the TiO 2 crystal structure. Our electrochemical experiments confirmed that TiO 2 , with the optimal amount of Mn, reduces the onset potential by 260 mV in a 2 M KHCO 3 (pH = ∼8) electrolyte and 370 mV in a 0.5 M H 2 SO 4 (pH = ∼0.5) electrolyte. Moreover, in 0.5 M H 2 SO 4 , we observed that the amount of Mn doping greatly impacts the selectivity towards oxygen production versus peroxysulfate formation. In 2 M KHCO 3 , the Mn doping of TiO 2 slightly decreases the selectivity towards oxygen production and increases the hydrogen peroxide formation. The Mn-doped TiO 2 shows good electrochemical stability for over 24 hours in both electrolytes. 

Data‐driven, machine learning (ML)‐assisted approaches have been used to study structure‐property relationships at the atomic scale, which have greatly accelerated the screening process and new material discovery. However, such approaches are not easily applicable to modulating material properties of a soft material in a laboratory with specific ingredients. Moreover, it is desirable to relate material properties directly to the experimental recipes. Herein, a data‐driven approach to tailoring mechanical properties of a soft material is demonstrated using ML‐assisted predictions of mechanical properties based on experimental synthetic recipes. Polyurethane (PU) elastomer is used as a model soft material to demonstrate the approach and experimentally varied mechanical properties of the PU elastomer by modulating the mixing ratio between components of the elastomer. Twenty‐five experimental conditions are selected based on the design of experiment and use those data points to train a linear regression model. The resulting model takes desired mechanical properties as input and returns synthetic recipes of a soft material, which is subsequently validated by experiments. Lastly, the prediction accuracies of different machine learning algorithms is compared. It is believed that the approach is widely applicable to other material systems to establish experimental conditions and material property relationships for soft materials.