电催化硝酸盐与一氧化氮还原合成氨: 计算见解与策略选择

doi:10.1016/S1872-2067(25)64776-0

催化学报 ›› 2025, Vol. 77: 220-226.DOI: 10.1016/S1872-2067(25)64776-0

电催化硝酸盐与一氧化氮还原合成氨: 计算见解与策略选择

郭璞^a, 杨绍雪^b^,^*(), 井会娟^a, 栾东^a, 龙军^a^,^*(), 肖建平^a^,^c^,^*()

^a中国科学院大连化学物理研究所, 催化基础国家重点实验室, 辽宁大连 116023
^b浙江肿瘤医院, 浙江杭州 310022
^c中国科学院大学, 北京 100049

收稿日期:2025-05-14 接受日期:2025-06-25 出版日期:2025-10-18 发布日期:2025-10-05
通讯作者: *电子信箱: yangsx@zjcc.org.cn (杨绍雪),longjun@dicp.ac.cn (龙军),xiao@dicp.ac.cn (肖建平).
基金资助:
国家重点研发计划(2023YFA1509103);国家自然科学基金(22425207);国家自然科学基金(22172156);国家自然科学基金(22402186);榆林清洁能源创新研究所能源革命科技计划(YIICE E411050316);催化国家重点实验室(2024SKL-A-016);大连化物所基金(DICP I202314);大连化物所基金(DICP I202425);国家博士后基金(GZC20232584);国家博士后基金(2024M763199)

Computational insights and strategic choices of nitrate and nitric oxide electroreduction to ammonia

Pu Guo^a, Shaoxue Yang^b^,^*(), Huijuan Jing^a, Dong Luan^a, Jun Long^a^,^*(), Jianping Xiao^a^,^c^,^*()

^aState Key Laboratory of Catalysis, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, Liaoning, China
^bZhejiang Cancer Hospital, Hangzhou 310022, Zhejiang, China
^cUniversity of Chinese Academy of Sciences, Beijing 100049, China

Received:2025-05-14 Accepted:2025-06-25 Online:2025-10-18 Published:2025-10-05
Contact: *E-mail: yangsx@zjcc.org.cn (S. Yang), longjun@dicp.ac.cn (J. Long), xiao@dicp.ac.cn (J. Xiao).
Supported by:
National Key Research and Development Program of China(2023YFA1509103);National Natural Science Foundation of China(22425207);National Natural Science Foundation of China(22172156);National Natural Science Foundation of China(22402186);Energy Revolution S&T Program of Yulin Innovation Institute of Clean Energy(YIICE E411050316);State Key Laboratory of Catalysis(2024SKL-A-016);DICP(DICP I202314);DICP(DICP I202425);China Postdoctoral Science Foundation(GZC20232584);China Postdoctoral Science Foundation(2024M763199)

摘要/Abstract

摘要：

氨(NH₃)作为全球化学工业的核心基础原料, 广泛应用于肥料生产、化工合成等多个领域, 并在氢能储存方面展现出巨大潜力. 目前, 工业上主要依赖Haber-Bosch工艺实现氨合成, 该工艺通过高温高压条件下的氮气与氢气反应生成氨(N₂ + 3H₂ → 2NH₃), 但存在能耗高、碳排放强度大的问题, 已成为制约其可持续发展的技术瓶颈. 近年来, 基于非热等离子体辅助的氮氧化级联电化学系统的“反向人工氮循环路线”(N₂ → NO_x → NH₃)作为一种绿色的替代方案备受关注. 其中, 电催化一氧化氮还原(eNORR)和电催化硝酸盐还原(eNO₃RR)合成氨是两条极具前景的技术路线. 实验表明, Cu₆Sn₅催化剂在eNORR合成氨中表现出比eNO₃RR更优异的性能, 但造成这种性能差异的深层微观机制仍有待阐明.

本文结合“电场控制恒电势(EFC-CP)”模拟方法与微动力学模拟, 系统解析了Cu₆Sn₅表面eNORR和eNO₃RR的电位依赖性、选择性与活性差异, 旨在揭示性能差异的根本原因, 并为高效催化剂的理性设计提供指导. 研究发现, eNORR规避了eNO₃RR中的关键中间体^*NO₂的解离和脱附步骤, 导致两条反应路径的关键中间体覆盖度显著不同. 具体而言, 在eNO₃RR路径中, 随着电位降低, 关键中间体^*NO₂极易从催化剂表面脱附, 导致其覆盖度不足, 进而抑制了整体的反应活性. 相比之下, eNORR路径中的^*NO中间体则能维持较高的表面覆盖度, 为后续合成氨提供了充足的反应物. 微动力学模拟进一步证实, eNORR和eNO₃RR的反应速率分别与^*NO和^*NO₂的表面覆盖度呈正相关. 在eNO₃RR中, ^*NO₂的覆盖度随电位负移而降低, 从而限制了其法拉第效率的恒定和提升; 而eNORR路径则得益于表面充足的^*NO覆盖和较低的反应能垒, 使其能够在宽电位窗口内保持稳定且高效的氨合成选择性.

综上, 本文通过恒电势模拟和微动力学分析, 揭示了电催化氮氧化物还原合成氨的微观机制, 阐明了关键中间体表面覆盖度在决定催化性能中的主导作用, 为新型高效催化剂的设计提供了清晰的理论框架, 对推动氨合成技术向绿色化低碳化转型具有重要的科学意义.

关键词: 合成氨反应, 电催化, 一氧化氮还原, 硝酸根还原, 恒电势, 密度泛函理论计算, 微动力学模拟

Abstract:

Electrochemical nitrate reduction (eNO₃RR) and nitric oxide reduction (eNORR) to ammonia have emerged as promising and sustainable alternatives to the traditional Haber-Bosch method for ammonia production, particularly within the recently proposed reverse artificial nitrogen cycle route: N₂ → NO_x → NH₃. Notably, experimental studies have demonstrated that eNORR exhibits superior performance over eNO₃RR on Cu₆Sn₅ catalysts. However, the fundamental mechanisms underlying this difference remain poorly understood. Herein, we performed systematic theoretical calculations to explore the reaction pathways, electronic structure effects, and potential-dependent Faradic efficiency associated with ammonia production via these two distinct electrochemical pathways (eNORR and eNO₃RR) on Cu₆Sn₅. By implementing an advanced ‘adaptive electric field controlled constant potential (EFC-CP)’ methodology combined with microkinetic modeling, we successfully reproduced the experimental observations and identified the key factors affecting ammonia production in both reaction pathways. It was found that eNORR outperforms eNO₃RR because it circumvents the ^*NO₂ dissociation and ^*NO₂ desorption steps, leading to distinct surface coverage of key intermediates between the two pathways. Furthermore, the reaction rates were found to exhibit a pronounced dependence on the surface coverage of ^*NO in eNORR and ^*NO₂ in eNO₃RR. Specifically, the facile desorption of ^*NO₂ on the Cu₆Sn₅ surface in eNO₃RR limits the attainable surface coverage of ^*NO, thereby impeding its performance. In contrast, the eNORR can maintain a high surface coverage of adsorbed ^*NO species, contributing to its enhanced ammonia production performance. These fundamental insights provide valuable guidance for the rational design of catalysts and the optimization of reaction routes, facilitating the development of more efficient, sustainable, and scalable techniques for ammonia production.

Key words: Reverse ammonia production, Electrocatalysis, Nitric oxide reduction, Nitrate reduction, Constant potential, Density functional theory calculation, Microkinetic modeling

郭璞, 杨绍雪, 井会娟, 栾东, 龙军, 肖建平. 电催化硝酸盐与一氧化氮还原合成氨: 计算见解与策略选择[J]. 催化学报, 2025, 77: 220-226.

Pu Guo, Shaoxue Yang, Huijuan Jing, Dong Luan, Jun Long, Jianping Xiao. Computational insights and strategic choices of nitrate and nitric oxide electroreduction to ammonia[J]. Chinese Journal of Catalysis, 2025, 77: 220-226.

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图/表 4

Fig. 1. (a) Computational model. (b) The free energies of the Volmer reaction at different potentials and dielectric constants. (c) Fitting results of slopes for εHP, Volmer reaction as a model system. (d) Linear relationships of ?G(HER) vs. ?M.

Fig. 2. (a) Free energy diagram for eNO3RR and eNORR to NH3 on the bare surface of Cu6Sn5 (22-) at 0 V vs. RHE. (b) Geometries for the intermediates in the optimal pathway. The red, blue, white, purple, dark grayish cyan and soft red colors denote the oxygen, nitrogen, hydrogen, potassium, tin and copper atoms, respectively.

Fig. 3. (a) Desorption energy for nitrite ion and the corresponding electron transfer during NO2 desorption. (b) Density of states of Cu6Sn5 at 0 V vs. RHE and ?0.22 V vs. RHE. Note that the fermi level at ?0.22 V vs. RHE is respect to that at 0 V vs. RHE. $\Delta N=\int_{-\infty}^{E f} \operatorname{DOS}(E) d E(-0.22 V)-\int_{-\infty}^{E f} \operatorname{DOS}(E) d E(0 V)$ (c) Activation energy barriers for *NO2 dissociation, *NO2 protonation and *NO protonation. (d) Charge transfer coefficients β [β = 1 - (qTS - qFS)] for *NO2 protonation (top) and *NO protonation (bottom) over Cu6Sn5 at different potentials. (e) Geometries of the transition states for *NO2 protonation (top) and *NO protonation (bottom) on Cu6Sn5 at 0 V vs. RHE. (f) ?COHP and ?ICOHP analyses for *NO2 protonation (left) and *NO protonation (right) at 0 V vs. RHE. The ?ICOHP at the Fermi level is indicated. (g) ?ICOHP of O?H bond at initial state for *NO2 protonation and NO protonation over Cu6Sn5 at different potentials. (h) Activation energy barrier for the Volmer and Heyrovsky reactions at 0 V vs. RHE over Cu6Sn5. (i) Charge transfer coefficients (top) and activation energy barriers (bottom) for the Volmer and Heyrovsky reactions over Cu6Sn5 at different potentials. The red, blue, white, purple, dark grayish cyan and soft red colors denote the oxygen, nitrogen, hydrogen, potassium, tin and copper atoms, respectively. IS, TS and FS denote the initial state, transition state and final state.

Fig. 4. (a) Simulated and experimental Faradaic efficiencies for ammonia in eNORR and eNO3RR on bare Cu6Sn5 at various potentials. (b) Simulated and experimental Faradaic efficiency for eNORR on bare Cu6Sn5 (top) and eNO3RR on semi-covered Cu6Sn5 (bottom) at 0 V vs. RHE. Degree of rate control for different elementary steps (c) and surface coverage of intermediates (d) in eNORR and eNO3RR over the Cu6Sn5 at different potentials. (R1: *NO + H2O + e- ? *NOH + OH?, R2: *NO2 + e- ? NO2? + *, R3: *NO2 + * ? *NO + *O, R4: NO3? + * ? *NO3 + e-).

参考文献

[1]	V. Smil, Nature, 1999, 400, 415.
[2]	J. G. Chen, R. M. Crooks, L. C. Seefeldt, K. L. Bren, R. M. Bullock, M. Y. Darensbourg, P. L. Holland, B. Hoffman, M. J. Janik, A. K. Jones, M. G. Kanatzidis, P. King, K. M. Lancaster, S. V. Lymar, P. Pfromm, W. F. Schneider, R. R. Schrock, Science, 2018, 360, eaar6611.
[3]	C. H. Christensen, T. Johannessen, R. Z. Sørensen, J. K. Nørskov, Catal. Today, 2006, 111, 140-144.
[4]	G. Chehade, I. Dincer, Fuel, 2021, 299, 120845.
[5]	D. R. MacFarlane, P. V. Cherepanov, J. Choi, B. H. R. Suryanto, R. Y. Hodgetts, J. M. Bakker, F. M. Ferrero Vallana, A. N. Simonov, Joule, 2020, 4, 1186-1205.
[6]	J. Cha, Y. Park, B. Brigljević, B. Lee, D. Lim, T. Lee, H. Jeong, Y. Kim, H. Sohn, H. Mikulčić, K. M. Lee, D. H. Nam, K. B. Lee, H. Lim, C. W. Yoon, Y. S. Jo, Renew. Sustain. Energy Rev., 2021, 152, 111562.
[7]	S. Wu, N. Salmon, M. M. Li, R. Bañares-Alcántara, S. C. E. Tsang, ACS Energy Lett., 2022, 7, 1021-1033.
[8]	I. McPherson, J. Zhang, Joule, 2020, 4, 12-14.
[9]	F. Chang, W. Gao, J. Guo, P. Chen, Adv. Mater., 2021, 33, 2005721.
[10]	T. Kandemir, M. E. Schuster, A. Senyshyn, M. Behrens, R. Schlögl, Angew. Chem. Int. Ed., 2013, 52, 12723-12726.
[11]	M. Wang, M. A. Khan, I. Mohsin, J. Wicks, A. H. Ip, K. Z. Sumon, C.-T. Dinh, E. H. Sargent, I. D. Gates, M. G. Kibria, Energy Environ. Sci., 2021, 14, 2535-2548.
[12]	G. Soloveichik, Nat. Catal., 2019, 2, 377-380.
[13]	B. Yang, W. Ding, H. Zhang, S. Zhang, Energy Environ. Sci., 2021, 14, 672-687.
[14]	S. Z. Andersen, M. J. Statt, V. J. Bukas, S. G. Shapel, J. B. Pedersen, K. Krempl, M. Saccoccio, D. Chakraborty, J. Kibsgaard, P. C. K. Vesborg, J. Nørskov, I. Chorkendorff, Energy Environ. Sci., 2020, 13, 4291-4300.
[15]	J. Long, S. Chen, Y. Zhang, C. Guo, X. Fu, D. Deng, J. Xiao, Angew. Chem. Int. Ed., 2020, 59, 9711-9718.
[16]	B. H. Ko, B. Hasa, H. Shin, Y. Zhao, F. Jiao, J. Am. Chem. Soc., 2022, 144, 1258-1266.
[17]	J. Shao, H. Jing, P. Wei, X. Fu, L. Pang, Y. Song, K. Ye, M. Li, L. Jiang, J. Ma, R. Li, R. Si, Z. Peng, G. Wang, J. Xiao, Nat. Energy, 2023, 8, 1273-1283.
[18]	F.-Y. Chen, Z.-Y. Wu, S. Gupta, D. J. Rivera, S. V. Lambeets, S. Pecaut, J. Y. T. Kim, P. Zhu, Y. Z. Finfrock, D. M. Meira, G. King, G. Gao, W. Xu, D. A. Cullen, H. Zhou, Y. Han, D. E. Perea, C. L. Muhich, H. Wang, Nat. Nanotechnol., 2022, 17, 759-767.
[19]	X. Fu, J. B. Pedersen, Y. Zhou, M. Saccoccio, S. Li, R. Sažinas, K. Li, S. Z. Andersen, A. Xu, N. H. Deissler, J. B. V. Mygind, C. Wei, J. Kibsgaard, P. C. K. Vesborg, J. K. Nørskov, I. Chorkendorff, Science, 2023, 379, 707-712.
[20]	G.-F. Chen, Y. Yuan, H. Jiang, S.-Y. Ren, L.-X. Ding, L. Ma, T. Wu, J. Lu, H. Wang, Nat. Energy, 2020, 5, 605-613.
[21]	Q. Wang, P. Guo, H. Li, J. Long, S. Yang, J. Xiao, Small Methods, 2025, 9, 2401208.
[22]	J. Li, G. Zhan, J. Yang, F. Quan, C. Mao, Y. Liu, B. Wang, F. Lei, L. Li, A. W. M. Chan, L. Xu, Y. Shi, Y. Du, W. Hao, P. K. Wong, J. Wang, S.-X. Dou, L. Zhang, J. C. Yu, J. Am. Chem. Soc., 2020, 142, 7036-7046.
[23]	P. H. van Langevelde, I. Katsounaros, M. T. M. Koper, Joule, 2021, 5, 290-294.
[24]	J. John, D. R. MacFarlane, A. N. Simonov, Nat. Catal., 2023, 6, 1125-1130.
[25]	L. R. Winter, J. G. Chen, Joule, 2021, 5, 300-315.
[26]	Y. Ren, C. Yu, L. Wang, X. Tan, Z. Wang, Q. Wei, Y. Zhang, J. Qiu, J. Am. Chem. Soc., 2022, 144, 10193-10200.
[27]	J. Long, D. Luan, X. Fu, H. Li, H. Jing, J. Xiao, ACS Catal., 2024, 14, 4423-4431.
[28]	A. Wu, J. Yang, B. Xu, X.-Y. Wu, Y. Wang, X. Lv, Y. Ma, A. Xu, J. Zheng, Q. Tan, Y. Peng, Z. Qi, H. Qi, J. Li, Y. Wang, J. Harding, X. Tu, A. Wang, J. Yan, X. Li, Appl. Catal. B, 2021, 299, 120667.
[29]	L. Li, C. Tang, X. Cui, Y. Zheng, X. Wang, H. Xu, S. Zhang, T. Shao, K. Davey, S.-Z. Qiao, Angew. Chem. Int. Ed., 2021, 60, 14131-14137.
[30]	C. Sellers, T. P. Senftle, Nat. Energy, 2023, 8, 1184-1185.
[31]	G. Kresse, J. Hafner, Phys. Rev. B, 1993, 47, 558-561.
[32]	G. Kresse, J. Hafner, Phys. Rev. B, 1994, 49, 14251-14269.
[33]	G. Kresse, J. Furthmüller, Comput. Mater. Sci., 1996, 6, 15-50.
[34]	B. Hammer, L. B. Hansen, J. K. Nørskov, Phys. Rev. B, 1999, 59, 7413-7421.
[35]	W. Schmickler, Chem. Rev., 1996, 96, 3177-3200.
[36]	D. Luan, J. Xiao, J. Phys. Chem. Lett., 2023, 14, 685-693.
[37]	I. Borukhov, D. Andelman, H. Orland, Electrochim. Acta, 2000, 46, 221-229.
[38]	L.-L. Zhang, C.-K. Li, J. Huang, J. Electrochem., 2022, 28, 2108471.
[39]	P. Guo, D. Luan, H. Li, L. Li, S. Yang, J. Xiao, J. Am. Chem. Soc., 2024, 146, 13974-13982.
[40]	H. Li, D. Luan, J. Long, P. Guo, J. Xiao, ACS Catal., 2024, 14, 12814-12823.
[41]	J. Long, D. Luan, X. Fu, H. Li, J. Xiao, ACS Catal., 2024, 14, 14678-14687.
[42]	C. Stegelmann, A. Andreasen, C. T. Campbell, J. Am. Chem. Soc., 2009, 131, 8077-8082.
[43]	C. T. Campbell, ACS Catal., 2017, 7, 2770-2779.

电催化硝酸盐与一氧化氮还原合成氨: 计算见解与策略选择

Computational insights and strategic choices of nitrate and nitric oxide electroreduction to ammonia

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[15]	成雪峰, 刘青, 孙启孟, 董慧龙, 陈冬赟, 郑莹, 徐庆锋, 路建美. 相邻Ni位点近邻电子效应增强Cu位点加氢和脱氧匹配度促进硝酸盐电还原产氨[J]. 催化学报, 2025, 70(3): 285-298.