Adsorption
Research at the State University of New York (SUNY) Binghamton has shed light on the fundamental mechanisms underlying the reduction of iron oxide (Fe3O4) by hydrogen. The study, published in the Journal of Physical Chemistry C, provides atom-level insights into the processes involved in hydrogen adsorption and water formation on
Catalysts
Researchers at Northeast Petroleum University have made a significant discovery in the development of zinc-air batteries, a crucial component in the transition to renewable energy sources. By designing a novel single-atom catalyst, the team has optimized the electrocatalytic performance of these batteries, addressing a long-standing issue of sluggish kinetics. The
Aluminum oxide
Researchers from the East China University of Science and Technology have made a groundbreaking discovery in the field of photocatalytic H2O2 production. According to their study published in Research on Chemical Intermediates, the introduction of amorphous aluminum oxide clusters onto crystalline carbon nitride (CCN) enhances charge separation, suppresses H2O2 decomposition,
Technical institutes
Physicists at the Beijing University of Technology have made a groundbreaking discovery about the exceptional potential of cubic Ti2C MXene in energy storage and optoelectronic applications. According to research published in the International Journal of Modern Physics C, the team used density functional theory (DFT)-based simulations to analyze the
Thermodynamics
Research conducted at North China Electric Power University has shed new light on the catalytic hydrogenation of CO2 to methanol, a promising low-carbon approach. Investigators utilized density functional theory calculations to elucidate the CO2 interaction mechanisms on the metakaolin surface doping with Ni and Zn. The findings indicate that the
Hydrogen
Researchers at Guilin University of Electronic Technology have utilized machine learning models to predict hydrogen adsorption capacities of Metal Organic Frameworks (MOFs) with high accuracy. The study employed classical density functional theory (cDFT) calculations and trained Gradient Boosting Regression (GBR), Random Forest (RF), XGBoost, and Support Vector Regression (SVR) models
Hydrogen
Researchers at Guizhou Normal University have made significant strides in the field of sustainable energy technologies by designing cost-effective hydrogen evolution reaction (HER) electrocatalysts. Their study employed an integrated computational approach combining density functional theory (DFT) with machine learning (ML) algorithms to investigate transition metal (TM) intercalation effects in Ti-doped
