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- ItemInterfacial Covalent Bonds Regulated Electron-Deficient 2D Black Phosphorus for Electrocatalytic Oxygen Reactions(Weinheim : Wiley-VCH, 2021) Wang, Xia; Raghupathy, Ramya Kormath Madam; Querebillo, Christine Joy; Liao, Zhongquan; Li, Dongqi; Lin, Kui; Hantusch, Martin; Sofer, Zdeněk; Li, Baohua; Zschech, Ehrenfried; Weidinger, Inez M.; Kühne, Thomas D.; Mirhosseini, Hossein; Yu, Minghao; Feng, XinliangDeveloping resource-abundant and sustainable metal-free bifunctional oxygen electrocatalysts is essential for the practical application of zinc–air batteries (ZABs). 2D black phosphorus (BP) with fully exposed atoms and active lone pair electrons can be promising for oxygen electrocatalysts, which, however, suffers from low catalytic activity and poor electrochemical stability. Herein, guided by density functional theory (DFT) calculations, an efficient metal-free electrocatalyst is demonstrated via covalently bonding BP nanosheets with graphitic carbon nitride (denoted BP-CN-c). The polarized P-N covalent bonds in BP-CN-c can efficiently regulate the electron transfer from BP to graphitic carbon nitride and significantly promote the OOH* adsorption on phosphorus atoms. Impressively, the oxygen evolution reaction performance of BP-CN-c (overpotential of 350 mV at 10 mA cm−2, 90% retention after 10 h operation) represents the state-of-the-art among the reported BP-based metal-free catalysts. Additionally, BP-CN-c exhibits a small half-wave overpotential of 390 mV for oxygen reduction reaction, representing the first bifunctional BP-based metal-free oxygen catalyst. Moreover, ZABs are assembled incorporating BP-CN-c cathodes, delivering a substantially higher peak power density (168.3 mW cm−2) than the Pt/C+RuO2-based ZABs (101.3 mW cm−2). The acquired insights into interfacial covalent bonds pave the way for the rational design of new and affordable metal-free catalysts. © 2021 The Authors. Advanced Materials published by Wiley-VCH GmbH
- ItemDeepsmirUD: Prediction of Regulatory Effects on microRNA Expression Mediated by Small Molecules Using Deep Learning(Basel : Molecular Diversity Preservation International, 2023) Sun, Jianfeng; Ru, Jinlong; Ramos-Mucci, Lorenzo; Qi, Fei; Chen, Zihao; Chen, Suyuan; Cribbs, Adam P.; Deng, Li; Wang, XiaAberrant miRNA expression has been associated with a large number of human diseases. Therefore, targeting miRNAs to regulate their expression levels has become an important therapy against diseases that stem from the dysfunction of pathways regulated by miRNAs. In recent years, small molecules have demonstrated enormous potential as drugs to regulate miRNA expression (i.e., SM-miR). A clear understanding of the mechanism of action of small molecules on the upregulation and downregulation of miRNA expression allows precise diagnosis and treatment of oncogenic pathways. However, outside of a slow and costly process of experimental determination, computational strategies to assist this on an ad hoc basis have yet to be formulated. In this work, we developed, to the best of our knowledge, the first cross-platform prediction tool, DeepsmirUD, to infer small-molecule-mediated regulatory effects on miRNA expression (i.e., upregulation or downregulation). This method is powered by 12 cutting-edge deep-learning frameworks and achieved AUC values of 0.843/0.984 and AUCPR values of 0.866/0.992 on two independent test datasets. With a complementarily constructed network inference approach based on similarity, we report a significantly improved accuracy of 0.813 in determining the regulatory effects of nearly 650 associated SM-miR relations, each formed with either novel small molecule or novel miRNA. By further integrating miRNA–cancer relationships, we established a database of potential pharmaceutical drugs from 1343 small molecules for 107 cancer diseases to understand the drug mechanisms of action and offer novel insight into drug repositioning. Furthermore, we have employed DeepsmirUD to predict the regulatory effects of a large number of high-confidence associated SM-miR relations. Taken together, our method shows promise to accelerate the development of potential miRNA targets and small molecule drugs.