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| 1 | Recent research progress of bimetallic phosphides-based nanomaterials as cocatalyst for photocatalytic hydrogen evolution显示文摘Hydrogen energy(H_(2)) has been considered as the most possible consummate candidates for replacing the traditional fossil fuels because of its higher combustion heat value and lower environmental pollution.Photocatalytic hydrogen evolution(PHE) from water splitting based on semiconductors is a promising technology towards converting solar energy into sustainable H_(2)fuel evolution. Developing high-activity and abundant source semiconductor materials is particularly important to realize highly efficient hydrogen evolution as for photocatalysis technology. However, unmodified pristine photocatalysts are often unable to overcome the weakness of low performance due to their limitations. In recent years, transition metal phosphides(TMPs) were used as valid co-catalysts to replace the classic precious metal materials in the process of photocatalytic reaction owing to their lower cost and higher combustion heat value.What is more, bimetallic phosphides have been also caused widespread concern in H_(2)evolution reaction owing to its much lower overpotential, more superior conductivity, and weaker charge carriers transfer impedance in comparison to those of single metal phosphides. In this minireview, we concluded the latest developments of bimetallic phosphides for a series of photocatalytic reactions. Firstly, we briefly summarize the present loading methods of bimetallic phosphides(BMPs) anchored on the photocatalyst. After that, the H;evolution efficiency based on BMPs as cocatalyst is also studied in detail. Besides, the application of BMPs-based host photocatalyst for H_(2)evolution under dye sensitization effect has also been discussed. At last, the current development prospects and prospective challenges in many ways of BMPs are proposed. We sincerely hope this minireview has certain reference value for great developments of BMPs in the future research. | Chunmei Li Daqiang Zhu Shasha Cheng Yan Zuo Yun Wang Changchang Ma Hongjun Dong | 2022 | Chinese Chemical Letters2022，33，3： | 2 |
| 2 | Sur- face Characteristics of 10Ni3MnCuA1 Steel by Shot Peening显示文摘 | Miao Hong Zuo Dunwen Wang Hongjun | 2010 | Transactions of Nanjing University of Aeronautics & Astronautics2010，26，3： | 1 |
| 3 | Synthesis and characterization of melt-processable polyimides derived from 1,4-bis(4-amino-2-trifluoromethyiphenoxy) benzene显示文摘 | Zuo Hongjun Chen Jiansheng Fan Lin | 2008 | Journal of Applied Polymer Science2008，107，： | 1 |
| 4 | Surface char-acteristics of 10Ni3MnCuA1 steel by shot peening 显示文摘 | Miao Hong Zuo Dunwen Wang Hongjun | 2010 | Transactions of Nanjing University of Aeronautics & Astro-nautics2010，26，3： | 1 |
| 5 | Deep reinforcement learning based multi-level dynamic reconfiguration for urban distribution network:a cloud-edge collaboration architecture显示文摘With the construction of the power Internet of Things(IoT),communication between smart devices in urban distribution networks has been gradually moving towards high speed,high compatibility,and low latency,which provides reliable support for reconfiguration optimization in urban distribution networks.Thus,this study proposed a deep reinforcement learning based multi-level dynamic reconfiguration method for urban distribution networks in a cloud-edge collaboration architecture to obtain a real-time optimal multi-level dynamic reconfiguration solution.First,the multi-level dynamic reconfiguration method was discussed,which included feeder-,transformer-,and substation-levels.Subsequently,the multi-agent system was combined with the cloud-edge collaboration architecture to build a deep reinforcement learning model for multi-level dynamic reconfiguration in an urban distribution network.The cloud-edge collaboration architecture can effectively support the multi-agent system to conduct“centralized training and decentralized execution”operation modes and improve the learning efficiency of the model.Thereafter,for a multi-agent system,this study adopted a combination of offline and online learning to endow the model with the ability to realize automatic optimization and updation of the strategy.In the offline learning phase,a Q-learning-based multi-agent conservative Q-learning(MACQL)algorithm was proposed to stabilize the learning results and reduce the risk of the next online learning phase.In the online learning phase,a multi-agent deep deterministic policy gradient(MADDPG)algorithm based on policy gradients was proposed to explore the action space and update the experience pool.Finally,the effectiveness of the proposed method was verified through a simulation analysis of a real-world 445-node system. | Siyuan Jiang Hongjun Gao Xiaohui Wang Junyong Liu Kunyu Zuo | 2023 | Global Energy Interconnection2023，6，1： | 0 |
| 6 | Hot Deformation Behaviors in Ti-6Al-4V/(TiB+TiC)Composites显示文摘Thermal compression testing was investigated using the Gleeble 3800 thermal simulator,and thermal deformation behavior of particle-reinforced titanium matrix composites(TMCs)was studied under deformation temperatures of 750-900°C,strain rates of 0.001-1 s^(-1),and experimental deformation of 60%.According to obtained flow stress curves,the hot deformation characteristics were analyzed.Based on the Arrhenius hyperbolic sinusoidal model,the constitutive equation at high temperature was established.Based on the theory of dynamic material models,a hot processing map of TMCs at high temperature was established,and the peak region of power dissipation rate and the instability region in the hot processing map were both determined.At the same time,the corresponding microstructures in the peak power dissipation rate and rheological instability regions were observed.The results showed that flow stress decreased with increasing deformation temperature and increased with increasing strain rate.The thermal deformation activation energy of titanium matrix composites was 301.8 kJ/mol.The Ti-6Al-4V/(TiB+TiC)composites possessed only one instability zone under high-temperature compression at a strain of 0.5,with corresponding temperatures at 750-840°C and strain rates at 0.1-1 s^(-1).The optimal thermal deformation parameters included corresponding temperatures of 830-880°C and strain rates of 0.001-0.05 s^(-1).The microstructures corresponding to optimal hot working parameters in processing maps were more homogeneous than the microstructures in the instability zone,including the distribution uniformity of reinforcement and the degree of dynamic recrystallization,and no instability phenomena including abnormal grain growth,microcracks or intensive fracture of reinforcements were found,indicating that the hot processing map had a positive guiding effect on the option of desirable material thermal-working parameters. | Xuejian Lin Hongjun Huang Fuyu Dong Yue Zhang Xiaoguang Yuan Bowen Zheng Xiaojiao Zuo | 2021 | Acta Metallurgica Sinica(English Letters)2021，34，12： | 0 |
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