Workflow-driven catalytic modulation from single-atom catalysts to Au-alloy clusters on graphene.

Gold-based (Au) nanostructures are efficient catalysts for CO oxidation, hydrogen evolution (HER), and oxygen evolution (OER) reactions, but stabilizing them on graphene (Gr) is challenging due to weak affinity from delocalized [Formula: see text] carbon orbitals. This study investigates forming metal alloys to enhance stability and catalytic performance of Au-based nanocatalysts. Using ab initio density functional theory, we characterize [Formula: see text] sub-nanoclusters (M = Ni, Pd, Pt, Cu, and Ag) with atomicities [Formula: see text], both in gas-phase and supported on Gr.

Crown Ether-Grafted Graphene Oxide-Based Materials-Synthesis, Characterization and Study of Lithium Adsorption from Complex Brine.

Direct lithium extraction from unconventional resources requires the development of effective adsorbents. Crown ether-containing materials have been reported as promising structures in terms of lithium selectivity, but data on adsorption in real, highly saline brines are scarce. Crown ether-grafted graphene oxides were synthesized using 2-hydroxymethyl-12-crown-4, hydroxy-dibenzo-14-crown-4 and epichlorohydrin as a source of anchoring groups.

Titanium(IV), Zirconium(IV), and Cerium(IV) Phosphates Synthesized Under Mild Conditions-Composition Characteristics and Evaluation of Sorption Properties Towards Copper Ions in Comparison to Commercially Available Ion-Exchange Resins.

The removal of copper from wastewater of mine origin requires the use of an appropriate method. Sorption methods are considered to be one of the best solutions for removing copper from industrial wastewater at low levels. Metal(IV) phosphates have been reported as excellent sorption materials that can be highly selective for copper.

Computational condensed matter science contributions to addressing water emerging contaminant pollution: a comprehensive review.

The study of emerging contaminants (ECs) in water resources has garnered significant attention due to their potential risks to human health and the environment. This review examines the contribution from computational approaches, focusing on the application of machine learning (ML) and molecular dynamics (MD) simulations to understand and optimize experimental applications of ECs adsorption on carbon-based nanomaterials. Condensed matter physics plays a crucial role in this research by investigating the fundamental properties of materials at the atomic and molecular levels, enabling the design and engineering of materials optimized for contaminant removal.

Design and application of synthetic 17B-HSD13 substrates reveals preserved catalytic activity of protective human variants.

Several hydroxysteroid dehydrogenase 17-beta 13 variants have previously been identified as protective against metabolic dysfunction-associated steatohepatitis (MASH) fibrosis, ballooning and inflammation, and as such this target holds significant therapeutic potential. However, over 5 years later, the function of 17B-HSD13 remains unknown. Structure-aided design enables the development of potent and selective sulfonamide-based 17B-HSD13 inhibitors.