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15. Defective Fe3O4‐x Few‐Atom Clusters Anchored on Nitrogen‐Doped Carbon as Efficient Oxygen Reduction Electrocatalysts for High‐Performance Zinc–Air Batteries

Author

Chu, Panpan, Zhang, Yingmeng, He, Jiajie, Chen, Jinhong, Zhuang, Jingjun, Li, Yongliang, Ren, Xiangzhong, Zhang, Peixin, Sun, Lingna, Yu, Bingzhe, and Chen, Shaowei

Subjects
Engineering, Materials Engineering, Chemical Sciences, Physical Chemistry, Affordable and Clean Energy, Fe(3)O(4-x )clusters, oxygen reduction reaction, oxygen vacancies, plasma-enhanced atomic layer deposition, zinc-air batteries, Fe 3O 4-x clusters
Abstract

It remains a challenge to develop cost-effective, high-performance oxygen electrocatalysts for rechargeable metal-air batteries. Herein, zinc-mediated zeolitic imidazolate frameworks are exploited as the template and nitrogen and carbon sources, onto which is deposited a Fe3 O4 layer by plasma-enhanced atomic layer deposition. Controlled pyrolysis at 1000 °C leads to the formation of high density of Fe3 O4- x few-atom clusters with abundant oxygen vacancies deposited on an N-doped graphitic carbon framework. The resulting nanocomposite (Fe3 O4- x /NC-1000) exhibits a markedly enhanced electrocatalytic performance toward oxygen reduction reaction in alkaline media, with a remarkable half-wave potential of +0.930 V versus reversible hydrogen electrode, long-term stability, and strong tolerance against methanol poisoning, in comparison to samples prepared at other temperatures and even commercial Pt/C. Notably, with Fe3 O4- x /NC-1000 as the cathode catalyst, a zinc-air battery delivers a high power density of 158 mW cm-2 and excellent durability at 5 mA cm-2 with stable 2000 charge-discharge cycles over 600 h. This is ascribed to the ready accessibility of the Fe3 O4- x catalytic active sites, and enhanced electrical conductivity, oxygen adsorption, and electron-transfer kinetics by surface oxygen vacancies. Further contributions may arise from the highly conductive and stable N-doped graphitic carbon frameworks.

Published
2022

22. Introduction

Author

Chen, Jianhang, Li, Yongliang, Zhang, Junwen, Chen, Jianhang, Li, Yongliang, and Zhang, Junwen

Published
2023
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43. Matrix embedding method in match for session-based recommendation

Author

Zhang, Qizhi, Lin, Yi, Wu, Kangle, Li, Yongliang, and Zeng, Anxiang

Subjects
Computer Science - Machine Learning, Computer Science - Information Retrieval
Abstract

Session based model is widely used in recommend system. It use the user click sequence as input of a Recurrent Neural Network (RNN), and get the output of the RNN network as the vector embedding of the session, and use the inner product of the vector embedding of session and the vector embedding of the next item as the score that is the metric of the interest to the next item. This method can be used for the "match" stage for the recommendation system whose item number is very big by using some index method like KD-Tree or Ball-Tree and etc.. But this method repudiate the variousness of the interest of user in a session. We generated the model to modify the vector embedding of session to a symmetric matrix embedding, that is equivalent to a quadratic form on the vector space of items. The score is builded as the value of the vector embedding of next item under the quadratic form. The eigenvectors of the symmetric matrix embedding corresponding to the positive eigenvalues are conjectured to represent the interests of user in the session. This method can be used for the "match" stage also. The experiments show that this method is better than the method of vector embedding.

Published
2019

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