High throughput exploration of the oxidation landscape in high entropy alloys

High entropy alloying strategy for accomplishing quintuple-nanoparticles grafted carbon towards exceptional high-performance overall seawater splitting

High entropy alloys (HEAs), a novel class of material, have been explored in terms of their excellent mechanical properties. Seawater electrolysis is a step towards sustainable production of carbon-neutral fuels such as H2, O2, and industrially demanding Cl2. Herein, we report a practically viable FeCoNiMnCr HEA nanoparticles system grafted on a conductive carbon matrix for promising seawater electrolysis. The comprehensive kinetics analysis of the hydrogen evolution reaction (HER), oxygen evolution reaction (OER), and chlorine evolution reaction (CER) confirms the effectiveness of our system. As an electrocatalyst, HEAs grafted on carbon black show trifunctionality with promising kinetics, selectivity and enduring performance, towards seawater splitting. We optimize high entropy alloy decorated/grafted carbon black (HEACB) catalysts, studying their synthesis temperature to scrutinize the effect of alloy formation variation on the catalysis efficacy. During the catalysis, selectivity between two mutually competing reactions, CER and OER, in the electrochemical catalysis of seawater is controlled by the reaction media pH. We employ Mott-Schottky measurements to probe the band structure of the intrinsically induced metal-semiconductor junction in the HEACB catalyst, where the carrier density and flat band potential are optimized. The HEACB sample provides promising results towards overall seawater electrolysis with a net half-cell potential of about 1.65 V with good stability, which strongly implies its broad practical applicability.

A systematic review on the state-of-the-art strategies for protein representation

The study of drug-target protein interaction is a key step in drug research. In recent years, machine learning techniques have become attractive for research, including drug research, due to their automated nature, predictive power, and expected efficiency. Protein representation is a key step in the study of drug-target protein interaction by machine learning, which plays a fundamental role in the ultimate accomplishment of accurate research. With the progress of machine learning, protein representation methods have gradually attracted attention and have consequently developed rapidly. Therefore, in this review, we systematically classify current protein representation methods, comprehensively review them, and discuss the latest advances of interest. According to the information extraction methods and information sources, these representation methods are generally divided into structure and sequence-based representation methods. Each primary class can be further divided into specific subcategories. As for the particular representation methods involve both traditional and the latest approaches. This review contains a comprehensive assessment of the various methods which researchers can use as a reference for their specific protein-related research requirements, including drug research.