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High-entropy perovskite oxides: A versatile class of materials for nitrogen reduction reactions

高熵钙钛矿氧化物: 一种应用于氮气还原的通用材料

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Abstract

Despite the intense research efforts directed to electrocatalytic nitrogen reduction reaction (eNRR), the NH3 yield and selectivity are still not up to the standard of practical application. Here, high-entropy perovskite oxides with composition Bax(FeCoNiZrY)0.2O3−δ (Bx(FCNZY)0.2 (x = 0.9, 1) are reported as eNRR catalysts. The eNRR activity of high-entropy perovskite oxide is enhanced by changing the nonstoichiometric metal elements at the A-site, thus generating additional oxygen vacancies. The NH3 yield and Faraday efficiency for B0.9(FCNZY)0.2 are 1.51 and 1.95 times higher than those for B(FCNZY)0.2, respectively. The d-band center theory is used to theoretically predict the catalytically active center at the B-site, and as a result, nickel was identified as the catalytic site. The free energy values of the intermediate states in the optimal distal pathway show that the third protonation step (*NNH2 → *NNH3) is the rate-determining step and that the increase in oxygen vacancies in the high-entropy perovskite contributes to nitrogen adsorption and reduction. This work provides a framework for applying high-entropy structures with active site diversity for electrocatalytic nitrogen fixation.

摘要

在过去的几年里, 电催化氮还原反应(eNRR)吸引了大量的研究兴趣. 尽管如此, NH3的产量和选择性仍然没有达到实际应用的标准. 本论文报道了成分为Bax(FeCoNiZrY)0.2O3−δ (Bx(FCNZY)0.2 (x = 0.9, 1)的高熵钙钛矿作为eNRR催化剂的新材料研究平台. 通过改变A位金属元素的非化学计量比, 使材料产生更高密度的氧缺陷, 进而提升氮气还原性能. B0.9(FCNZY)0.2的NH3产率和法拉第效率是B(FCNZY)0.2的1.51 和1.95倍. 理论上, 利用d-带中心理论预测了B-位点的催化活性中心, 并确定了镍元素为催化位点. 最佳远端反应途径中的中间状态的自由 能值表明, 第三个质子化步骤(*NNH2 → * NNH3)是决定速率的步骤, 高熵钙钛矿氧化物中氧空位的增加对氮的吸附和还原都有贡献. 这项工作为具有多个活性位点的高熵结构应用于电催化固氮提供了一个新的研究框架.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (52161135302, 21674019, and 51801075), the Research Foundation Flanders (G0F2322N), Shanghai Scientific and Technological Innovation Project (18JC1410600), and the Program of the Shanghai Academic Research Leader (17XD1400100). Hofkens J and Martens JA gratefully acknowledge the financial support from the Flemish Government through the Moonshot cSBO project P2C (HBC.2019.0108), the Long-term Structural Funding (Methusalem CASAS2, Meth/15/04) and Interne Fondsen KU Leuven through project C3/20/067. De Ras M greatfully acknowledges the support from the Research Foundation-Flanders (FWO) in the form of a doctoral fellowship (1SA3321N). Chu K gratefully acknowledges the financial support from China Scholarship Council in the form of a visiting Ph.D. Student (File No. 202106790090). Theoretical work was carried out at the LvLiang Cloud Computing Center of China, and the calculations were performed on a TianHe-2 system. We also thank the characterizations supported by the Central Laboratory, School of Chemical and Material Engineering, Jiangnan University.

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Authors and Affiliations

  1. The Key Laboratory of Synthetic and Biological Colloids, Ministry of Education, School of Chemical and Material Engineering, International Joint Research Laboratory for Nano Energy Composites, Jiangnan University, Wuxi, 214122, China

    Kaibin Chu  (楚凯斌), Jingjing Qin  (秦静静), Haiyan Zhu  (朱海燕), Longsheng Zhang  (张龙生), Nan Zhang  (张楠) & Tianxi Liu  (刘天西)

  2. Department of Chemistry, KU Leuven, Celestijnenlaan 200F, Leuven, 3001, Belgium

    Michiel De Ras, Chuang Wang  (王闯), Johan Hofkens & Feili Lai  (赖飞立)

  3. State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, 214122, China

    Lei Xiong  (熊磊)

  4. Centre of Surface Chemistry and Catalysis: Characterisation and Application Team, KU Leuven, Leuven, 3001, Belgium

    Johan A. Martens

  5. Max Planck Institute for Polymer Research, Ackermannweg 10, Mainz, 55128, Germany

    Johan Hofkens

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  1. Kaibin Chu  (楚凯斌)
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  2. Jingjing Qin  (秦静静)
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  3. Haiyan Zhu  (朱海燕)
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  11. Feili Lai  (赖飞立)
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Contributions

Chu K, Zhang L, Zhang N, Hofkens J, Lai F, and Liu T came up with the concept. Chu K, Qin J, Zhu H, De Ras M, and Xiong L proposed the topic. Chu K, Zhu H, and Xiong L collected the data. Chu K, Qin J, De Ras M, Wang C, Zhang N, and Martens JA analyzed the data. Chu K wrote the original draft. All authors contributed to the general discussion.

Corresponding authors

Correspondence to Feili Lai  (赖飞立) or Tianxi Liu  (刘天西).

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Conflict of interest

The authors declare that they have no conflict of interest.

Kaibin Chu received his BE and MS at Jiangnan University. He is currently a PhD student in Tianxi Liu’s group at the School of Chemical and Material Engineering, Jiangnan University. His research focuses on the design of inorganic perovskite materials for electrocatalytic ammonia synthesis.

Feili Lai received his BS degree from Donghua University (2014), master’s degree from Fudan University (2017), and PhD degree from Max Planck Institute of Colloids and Interfaces/Universität Potsdam (2019). He is now a research fellow of the Department of Chemistry, KU Leuven. His current interests include machine learning in materials science, and the design and synthesis of low-dimensional solids for energy storage and conversion applications.

Tianxi Liu received his BS degree from Henan University (1992) and PhD degree from the Changchun Institute of Applied Chemistry, Chinese Academy of Sciences (1998). He is currently a full professor at Jiangnan University. His main research interests include polymer nanocomposites, organic/inorganic hybrid materials, nanofibers and their composites, advanced energy materials and energy conversion and storage.

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Chu, K., Qin, J., Zhu, H. et al. High-entropy perovskite oxides: A versatile class of materials for nitrogen reduction reactions. Sci. China Mater. 65, 2711–2720 (2022). https://doi.org/10.1007/s40843-022-2021-y

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