Electrocatalytic reduction of carbon dioxide (CO2ER) in rechargeable Zn–CO2 battery still remains a great challenge. Herein, a highly efficient CO2ER electrocatalyst composed of coordinatively unsaturated single‐atom copper coordinated with nitrogen sites anchored into graphene matrix (Cu–N2/GN) is reported. Benefitting from the unsaturated coordination environment and atomic dispersion, the ultrathin Cu–N2/GN nanosheets exhibit a high CO2ER activity and selectivity for CO production with an onset potential of −0.33 V and the maximum Faradaic efficiency of 81% at a low potential of −0.50 V, superior to the previously reported atomically dispersed Cu–N anchored on carbon materials. Experimental results manifest the highly exposed and atomically dispersed Cu–N2 active sites in graphene framework where the Cu species are coordinated by two N atoms. Theoretical calculations demonstrate that the optimized reaction free energy for Cu–N2 sites to capture CO2 promote the adsorption of CO2 molecules on Cu–N2 sites; meanwhile, the short bond lengths of Cu–N2 sites accelerate the electron transfer from Cu–N2 sites to *CO2, thus efficiently boosting the *COOH generation and CO2ER performance. A designed rechargeable Zn–CO2 battery with Cu–N2/GN nanosheets deliver a peak power density of 0.6 mW cm−2, and the charge process of battery can be driven by natural solar energy.

Electrochemically driven carbon dioxide (CO2) conversion is an emerging research field due to the global warming and energy crisis. Carbon monoxide (CO) is one key product during electroreduction of CO2; however, this reduction process suffers from tardy kinetics due to low local concentration of CO2 on a catalyst's surface and low density of active sites. Herein, presented is a combination of experimental and theoretical validation of a Ni porphyrin‐based covalent triazine framework (NiPor‐CTF) with atomically dispersed NiN4 centers as an efficient electrocatalyst for CO2 reduction reaction (CO2RR). The high density and atomically distributed NiN4 centers are confirmed by aberration‐corrected high‐angle annular dark field scanning transmission electron microscopy and extended X‐ray absorption fine structure. As a result, NiPor‐CTF exhibits high selectivity toward CO2RR with a Faradaic efficiency of >90% over the range from −0.6 to −0.9 V for CO conversion and achieves a maximum Faradaic efficiency of 97% at −0.9 V with a high current density of 52.9 mA cm−2, as well as good long‐term stability. Further calculation by the density functional theory method reveals that the kinetic energy barriers decreasing for *CO2 transition to *COOH on NiN4 active sites boosts the performance.

Development of inexpensive and efficient oxygen evolution reaction (OER)catalysts in acidic environment is very challenging, but important for practical proton exchange membrane (PEM) water electrolyzers. Here we develop a molecular iron-nitrogen coordinated carbon nanofiber supported on electrochemically exfoliated graphene (FeN 4 /NF/EG) electrocatalyst through carbonizing the precursor composed of iron ions absorbed on polyaniline-electrodeposited EG. Benefitting from the unique 3D structure, the FeN 4 /NF/EG hybrid exhibits a low overpotential of ~294 mV at 10 mA cm -2 for the OER in This article is protected by copyright. All rights reserved. 5precursor was uniformly electrodeposited on EG surface that was constructed by electrochemical exfoliation of graphite (Figure S1). After soaking in iron nitrate solution, carbonization, and acid etching treatments, the precursor was in situ converted into FeN x /NF/EG catalyst, which is supported by Fourier-transform infrared spectroscopy (FTIR) results (Figure S2). We systematically explored the influence of annealing at different temperatures (800-1000 o C) affecting the OER activity. The optimized carbonization temperature was 900 o C (FeN x /NF/EG), which exhibited the best electrocatalytic performance for OER in acid (Figure S3-S4). Moreover, this synthesis method can be further This article is protected by copyright. All rights reserved. 13 support from U.S. DOE fuel cell technologies Offices. M. Qiu thanks the support of Self-determined Research Funds of CCNU from Colleges' Basic Research and Operation of MOE ( 23020205170456). This research was supported by Dr. Y. Hu (Yongfeng Hu) to provide valuable discussion about the XAS analysis.Received: ((will be filled by the editorial staff))Revised: ((will be filled by the editorial staff))

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Knowledge of the interactions between proteins and nucleic acids is the basis of understanding various biological activities and designing new drugs. How to accurately identify the nucleic-acid-binding residues remains a challenging task. In this paper, we propose an accurate predictor, GraphBind, for identifying nucleic-acid-binding residues on proteins based on an end-to-end graph neural network. Considering that binding sites often behave in highly conservative patterns on local tertiary structures, we first construct graphs based on the structural contexts of target residues and their spatial neighborhood. Then, hierarchical graph neural networks (HGNNs) are used to embed the latent local patterns of structural and bio-physicochemical characteristics for binding residue recognition. We comprehensively evaluate GraphBind on DNA/RNA benchmark datasets. The results demonstrate the superior performance of GraphBind than state-of-the-art methods. Moreover, GraphBind is extended to other ligand-binding residue prediction to verify its generalization capability. Web server of GraphBind is freely available at http://www.csbio.sjtu.edu.cn/bioinf/GraphBind/.