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您的检索式：作者名="Shuaijun Pan"
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| 1 | Tuning electronic properties of cobalt phthalocyanines for oxygen reduction and evolution reactions显示文摘Metal–phthalocyanines are a class of catalytically active materials promising in energy conversion and storage fields(e.g.,electrocatalysis).However,understanding and controlling the electrochemical properties in metal-phthalocyanine systems is challenging.Herein,we elucidate the electrocatalytic origins of a series of cobalt-phthalocyanine molecular catalysts and finetune their electronic properties at the atomic level,both experimentally and computationally.The interactions between the cobalt center and the local coordination environment are regulated by introducing either electron-donating or electron-withdrawing groups on the phthalocyanine ligand,and the spin-orbit splitting of cobalt is increased by~0.15 eV compared with the nonsubstituted ligand.Specifically,the aminated cobalt phthalocyanine-based electrocatalysts exhibit low free energies in the ratedetermining steps of the oxygen reduction(-1.68 eV)and oxygen evolution reactions(0.37 eV).This contributes to the high electrocatalytic activity(e.g.,a halfwave potential of 0.84 V and an overpotential of 0.30 V at 10 mAcm^(-2)),featuring a high selectivity of a four-electron pathway(i.e.,a negligible by-product of hydrogen peroxide).These catalysts also exhibit exceptional kinetic current density(Tafel slope of 100 mV dec^(-1))in oxygen reduction reactions,in addition to a superior power density(158 mWcm^(-2))and a high cycling stability(>1,300 cycles)in Zn-air batteries,outperforming the commercial Pt/C and/or RuO2counterparts. | Jianlin Jiang Zhen Liu Shuaijun Pan Xinnian Xia Bing Qin Yang Hu Xuxu Wang Jiamin Lan Yu Gu Encai Ou Weijian Xu Joseph J.Richardson Rui Guo | 2024 | Science China Chemistry2024，67，1： | 0 |
| 2 | Towards uniform point distribution in feature-preserving point cloud filtering显示文摘While a popular representation of 3D data,point clouds may contain noise and need filtering before use.Existing point cloud filtering methods either cannot preserve sharp features or result in uneven point distributions in the filtered output.To address this problem,this paper introduces a point cloud filtering method that considers both point distribution and feature preservation during filtering.The key idea is to incorporate a repulsion term with a data term in energy minimization.The repulsion term is responsible for the point distribution,while the data term aims to approximate the noisy surfaces while preserving geometric features.This method is capable of handling models with fine-scale features and sharp features.Extensive experiments show that our method quickly yields good results with relatively uniform point distribution. | Shuaijun Chen Jinxi Wang Wei Pan Shang Gao Meili Wang Xuequan Lu | 2023 | Computational Visual Media2023，9，2： | 0 |
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