Stabilizing supported atom-precise low-nuclearity platinum cluster catalysts by nanoscale confinement
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Photo-assisted technologies for environmental remediation
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Anion vacancies activate N2 to ammonia on Ba–Si orthosilicate oxynitride-hydride
Anion vacancies on metal oxide surfaces have been studied as either active sites or promoting sites in various chemical reactions involving oxidation/reduction processes. However, oxide materials rarely work effectively as catalysts in the absence of transition metal sites. Here we report a Ba–Si orthosilicate oxynitride–hydride as a transition-metal-free catalyst for efficient ammonia synthesis via an anion-vacancy–mediated mechanism. The facile desorption of H− and N3− anions plus the flexibility of the crystal structure can accommodate a high density of electrons at vacancy sites, where N2 can be captured and directly activated to ammonia through hydrogenation processes. The ammonia synthesis rates reach 40.1 mmol g−1 h−1 at 300 °C by loading ruthenium nanoparticles. Although not found to dissociate N2, Ru instead facilitates the formation of anion vacancies at the Ru–support interface. This demonstrates a new route for anion-vacancy–mediated heterogeneous catalysis.
Narrowing the gap between machine learning scoring functions and free energy perturbation using augmented data
Machine learning offers great promise for fast and accurate binding affinity predictions. However, current models lack robust evaluation and fail on tasks encountered in (hit-to-) lead optimisation, such as ranking the binding affinity of a congeneric series of ligands, thereby limiting their application in drug discovery. Here, we address these issues by first introducing a novel attention-based graph neural network model called AEV-PLIG (atomic environment vector–protein ligand interaction graph). Second, we introduce a new and more realistic out-of-distribution test set called the OOD Test. We benchmark our model on this set, CASF-2016, and a test set used for free energy perturbation (FEP) calculations, that not only highlights the competitive performance of AEV-PLIG, but provides a realistic assessment of machine learning models with rigorous physics-based approaches. Moreover, we demonstrate how leveraging augmented data (generated using template-based modelling or molecular docking) can significantly improve binding affinity prediction correlation and ranking on the FEP benchmark (weighted mean PCC and Kendall’s τ increases from 0.41 and 0.26 to 0.59 and 0.42). These strategies together are closing the performance gap with FEP calculations (FEP+ achieves weighted mean PCC and Kendall’s τ of 0.68 and 0.49 on the FEP benchmark) while being ~400,000 times faster.
Strong-weak dual interface engineered electrocatalyst for large current density hydrogen evolution reaction
Supported nanocatalysts are crucial for hydrogen production, yet their activity and stability are challenging to manage due to complex metal-support interfaces. Herein, we design Pt@ anatase&rutile-TiO2 with a strong-weak dual interface by modifying TiO2 using high-energy ball milling and in-situ reduction to vary surface energies. Experiments and density functional theory calculations reveal that the strong Pt-anatase TiO2 interface enhances hydrogen adsorption. In contrast, the weak Pt-rutile TiO2 interface facilitates hydrogen desorption, simultaneously preventing Pt agglomeration and increasing reaction rate. As a result, the tailored catalyst has a 529.3 mV overpotential at 1000 mA cm−2 in 0.5 M H2SO4, 0.69 times less than commercial Pt/C. It also possesses 8.8 times the mass activity of commercial Pt/C and maintains a low overpotential after 2000 cyclic voltammetry cycles, suggesting high activity and stability. This strong-weak dual interface engineering strategy shows potential for overall water splitting and proton exchange membrane water electrolyzer, advancing the design of efficient supported nanocatalysts.
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