Electrochemical conversion of nitrate (NO 3 − ) into ammonia (NH 3 ) recycles nitrogen and offers a route to the production of NH 3 , which is more valuable than dinitrogen gas. However, today's development of NO 3 − electroreduction remains hindered by the lack of a mechanistic picture of how catalyst structure may be tuned to enhance catalytic activity. Here we demonstrate enhanced NO 3 − reduction reaction (NO 3 − RR) performance on Cu 50 Ni 50 alloy catalysts, including a 0.12 V upshift in the half-wave potential and a 6-fold increase in activity compared to those obtained with pure Cu at 0 V vs reversible hydrogen electrode (RHE). Ni alloying enables tuning of the Cu d-band center and modulates the adsorption energies of intermediates such as *NO 3 − , *NO 2 , and *NH 2 . Using density functional theory calculations, we identify a NO 3 − RR-to-NH 3 pathway and offer an adsorption energy−activity relationship for the CuNi alloy system. This correlation between catalyst electronic structure and NO 3 − RR activity offers a design platform for further development of NO 3 − RR catalysts.

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In this article, we describe a long-non-coding RNA (lncRNA) and disease association database (LncRNADisease), which is publicly accessible at http://cmbi.bjmu.edu.cn/lncrnadisease. In recent years, a large number of lncRNAs have been identified and increasing evidence shows that lncRNAs play critical roles in various biological processes. Therefore, the dysfunctions of lncRNAs are associated with a wide range of diseases. It thus becomes important to understand lncRNAs’ roles in diseases and to identify candidate lncRNAs for disease diagnosis, treatment and prognosis. For this purpose, a high-quality lncRNA–disease association database would be extremely beneficial. Here, we describe the LncRNADisease database that collected and curated approximately 480 entries of experimentally supported lncRNA–disease associations, including 166 diseases. LncRNADisease also curated 478 entries of lncRNA interacting partners at various molecular levels, including protein, RNA, miRNA and DNA. Moreover, we annotated lncRNA–disease associations with genomic information, sequences, references and species. We normalized the disease name and the type of lncRNA dysfunction and provided a detailed description for each entry. Finally, we developed a bioinformatic method to predict novel lncRNA–disease associations and integrated the method and the predicted associated diseases of 1564 human lncRNAs into the database.