Modulating proton binding energy on the tungsten carbide nanowires surfaces for boosting hydrogen evolution in acid

ungsten carbides have attracted wide attentions as Pt substitute electrocatalysts for hydrogen evolution reaction (HER), due to their good stability in an acid environment and Pt-like behaviour in hydrolysis. However, quantum chemistry calculations predict that the strong tungsten-hydrogen bonding hinders hydrogen desorption and restricts the overall catalytic activity. Synergistic modulation of host and guest electronic interaction can change the local work function of a compound, and therefore, improve its electrocatalytic activity over either of the elements individually. Herein, we develop a creative and facile solid-state approach to synthesize self-supported carbon-encapsulated single-phase WC hybrid nanowires arrays (nanoarrays) as HER catalyst. The theoretical calculations reveal that carbon encapsulation modifies the Gibbs free energy of H* values for the WC adsorption sites, endowing a more favorable C@WC active site for HER. The experimental results exhibit that the hybrid WC nanoarrays possess remarkable Pt-like catalytic behavior, with superior activity and stability in an acidic media, which can be compared to the best non-noble metal catalysts reported to date for hydrogen evolution reaction. The present results and the facile synthesis method open up an exciting avenue for developing cost-effective catalysts with controllable morphology and functionality for scalable hydrogen generation and other carbide nanomaterials applicable to a range of electrocatalytic reactions.

Topological representations of crystalline compounds for the machine-learning prediction of materials properties

Accurate theoretical predictions of desired properties of materials play an important role in materials research and development.Machine learning(ML)can accelerate the materials design by building a model from input data.For complex datasets,such as those of crystalline compounds,a vital issue is how to construct low-dimensional representations for input crystal structures with chemical insights.In this work,we introduce an algebraic topology-based method,called atom-specific persistent homology(ASPH),as a unique representation of crystal structures.The ASPH can capture both pairwise and many-body interactions and reveal the topology-property relationship of a group of atoms at various scales.Combined with composition-based attributes,ASPH-based ML model provides a highly accurate prediction of the formation energy calculated by density functional theory(DFT).After training with more than 30,000 different structure types and compositions,our model achieves a mean absolute error of 61 meV/atom in cross-validation,which outperforms previous work such as Voronoi tessellations and Coulomb matrix method using the same ML algorithm and datasets.Our results indicate that the proposed topology-based method provides a powerful computational tool for predicting materials properties compared to previous works.