Promoting the efficient utilization of both the pores and heteroatoms remains a highly challenging endeavor, yet is of crucial significance to enhance the specific capacitance and energy density of carbon-based...

Synchronously achieving high-capacity and high-rate sodium-ion storage is critical for large-scale energy stationary application of sodium-ion batteries (SIB). Here we present a facile bottom-up strategy to covalently encapsulate sulfur chains...

The electrocatalytic CO2 reduction process has gained enormous attention for both environmental protection and chemicals production. Thereinto, the design of new electrocatalysts with high activity and selectivity can draw inspiration from the abundant scientific literature. An annotated and verified corpus made from massive literature can assist the development of natural language processing (NLP) models, which can offer insight to help guide the understanding of these underlying mechanisms. To facilitate data mining in this direction, we present a benchmark corpus of 6,086 records manually extracted from 835 electrocatalytic publications, along with an extended corpus with 145,179 records in this article. In this corpus, nine types of knowledge such as material, regulation method, product, faradaic efficiency, cell setup, electrolyte, synthesis method, current density, and voltage are provided by either annotating or extracting. Machine learning algorithms can be applied to the corpus to help scientists find new and effective electrocatalysts. Furthermore, researchers familiar with NLP can use this corpus to design domain-specific named entity recognition (NER) models.

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