电催化硝酸盐还原合成氨电势相关性的计算见解

doi:10.1016/S1872-2067(23)64413-4

催化学报 ›› 2023, Vol. 48: 205-213.DOI: 10.1016/S1872-2067(23)64413-4

电催化硝酸盐还原合成氨电势相关性的计算见解

井会娟^a^,^b, 龙军^a, 李欢^a^,^b, 傅笑言^a, 肖建平^a^,^b^,^*()

^a中国科学院大连化学物理研究所, 催化国家重点实验室, 大连洁净能源国家实验室(筹), 辽宁大连116023
^b中国科学院大学, 北京100049

收稿日期:2022-12-14 接受日期:2023-02-13 出版日期:2023-05-18 发布日期:2023-04-20
通讯作者: * 电子信箱: xiao@dicp.ac.cn (肖建平).
基金资助:
国家重点研发计划(2021YFA1500702);国家重点研发计划(22YFE0108000);国家自然科学基金(22172156);中国科学院洁净能源创新研究院合作基金资助项目(DNL202003);中国科学院战略先导计划(XDB36030200);中国科学院大连清洁能源国家实验室榆林分中心人工智能科技计划(DNL-YLA202205)

Computational insights on potential dependence of electrocatalytic synthesis of ammonia from nitrate

Huijuan Jing^a^,^b, Jun Long^a, Huan Li^a^,^b, Xiaoyan Fu^a, Jianping Xiao^a^,^b^,^*()

^aState Key Laboratory of Catalysis, Dalian National Laboratory for Clean Energy, Dalian Institute of Chemical Physics, Chinese Academy of Sciences,Dalian 116023, Liaoning, China
^bUniversity of Chinese Academy of Sciences, Beijing 100049, China

Received:2022-12-14 Accepted:2023-02-13 Online:2023-05-18 Published:2023-04-20
Contact: * E-mail: xiao@dicp.ac.cn (J. Xiao).
Supported by:
National Key Research and Development Program of China(2021YFA1500702);National Key Research and Development Program of China(22YFE0108000);National Natural Science Foundation of China(22172156);DNL Cooperation Fund, CAS(DNL202003);Strategic Priority Research Program of the Chinese Academy of Sciences(XDB36030200);AI S&T Program of Yulin Branch, Dalian National Laboratory for Clean Energy, CAS(DNL-YLA202205)

摘要/Abstract

摘要：

人类活动和工业生产导致全球氮循环严重失衡, 对生命系统的健康造成了一定威胁. 因此, 开发高效、环保的NO_x脱除技术具有重要意义. 其中, 电化学硝酸盐还原反应(eNO₃RR)是反向人工氮循环的重要组成部分, 同时也被认为是一种非集成式合成氨(NH₃)的可行途径. NH₃是生产肥料的重要原料之一, 传统哈伯法(H-B)合成氨能耗高, 且会排放大量温室气体CO₂, 由可再生电力驱动的电催化氮气还原(eNRR)合成氨被认为是一条更环保和可持续的路线. 目前, 一些研究表明电催化硝酸盐还原具有比eNRR更高的产氨活性和选择性. 然而, 一个主要的挑战是在低过电位下产物的选择性, 即亚硝酸盐(HNO₂)和NH₃之间的竞争.
本文选择了六个具有MN₄单元的石墨烯负载的过渡金属单原子催化剂(SACs)作为模型, 通过密度泛函理论(DFT)计算研究eNO₃RR机理. 采用本课题组开发的反应相图分析(RPD)方法从全局热力学最优的角度构建了六个MN₄催化剂的二维准活性和选择性相图, 其中FeN₄催化剂展示出较高的准活性和适当的选择性. DFT计算结果表明, 产生HNO₂和NH₃的最优路径共享一个关键中间体(NO₂*), 其吸附结构和在后续转化中的偏好决定了产物选择性. 结合电势相关性动力学势垒计算和微观动力学模拟结果, 动力学研究表明, 理论上两种产物不同电压下的法拉第效率(FE)趋势会发生反转, 与FeN₄催化剂上实验测得的HNO₂和NH₃的FE趋势和反转电压吻合, 这归因于两个重要的基元步骤(R4: NO₂* → cisHNO₂*和R7: NO₂* → HNO₂ + *)具有不同的电荷转移系数(β), 前者(R4)具有更大的电荷转移系数, 使得生成NH₃的动力学能垒随电压的降低而下降得更快, 这就把NH₃从低过电势下的低选择性到高过电势下的高选择性归因到动力学问题, 较好地理解了两种产物的FE发生发转的原因. 最后, 从电子结构方面对R4和R7两个关键基元步骤的初态和过渡态进行了电子局域化函数和晶体轨道哈密顿布居分析.
综上, 理论计算的机理研究很好地解释实验观察的结果, 其机理方面的理解对于将来理性设计高效的eNO₃RR合成NH₃催化剂具有一定的指导意义.

关键词: 电催化合成氨, 密度泛函理论计算, 反应相图分析, 活性, 选择性

Abstract:

Electrochemical nitrate reduction reaction (eNO₃RR) has been considered as an alternative route for decentralized ammonia (NH₃) synthesis. However, a major challenge is products selectivity at low overpotentials, namely, the competition between nitrite (HNO₂) and ammonia. Herein, we employed a single-atom catalyst (FeN₄) as model to study the competitive mechanism of NH₃ and HNO₂ by density functional theory calculations. It was found the optimal paths for ammonia and nitrite productions share a key intermediate (NO₂*), whose adsorption structures and preference in the following conversion determines the selectivity. We have incorporated potential-dependent barriers and microkinetic modeling to understand the Faradaic efficiency at different potentials. Our results are in good agreement with the experimental trend of Faradaic efficiencies of NH₃ and HNO₂, which can be rationalized well by the charge transfer coefficient (β) for NO₂* protonation to cisHNO₂* with respect to that to HNO₂. A low selectivity of ammonia production at small overpotentials can be ascribed to a kinetic issue. The electron localization function and crystal orbital Hamilton population were analyzed on the initial and transition states for NO₂* protonation to cisHNO₂* and HNO₂. The computational mechanistic insights can help to design new catalyst for eNO₃RR highly active and selective to NH₃.

Key words: Electrochemical ammonia synthesis, Density functional theory calculation, Reaction phase diagram, Activity, Selectivity

井会娟, 龙军, 李欢, 傅笑言, 肖建平. 电催化硝酸盐还原合成氨电势相关性的计算见解[J]. 催化学报, 2023, 48: 205-213.

Huijuan Jing, Jun Long, Huan Li, Xiaoyan Fu, Jianping Xiao. Computational insights on potential dependence of electrocatalytic synthesis of ammonia from nitrate[J]. Chinese Journal of Catalysis, 2023, 48: 205-213.

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链接本文: https://www.cjcatal.com/CN/10.1016/S1872-2067(23)64413-4

https://www.cjcatal.com/CN/Y2023/V48/I5/205

图/表 5

Scheme 1. (a) A complex reaction network for eNO3RR to nitrite and ammonia, constructed from the elementary steps listed in Table S1. (b) A scheme of global energy optimization to search optimal pathway, where the rA, rB and rC are the elementary steps with the largest ΔGRPD in given paths; (c) The green and red arrows connect a specific pathway of eNO3RR to ammonia and nitrite over FeN4 catalyst.

Fig. 1. The 2D (quasi) activity maps as a function of NH* [Gad(NH*)] and HNO2* [Gad(HNO2*)] adsorption free energies for eNO3RR to NH3 (a) and HNO2 (b). (c) The 2D (quasi) activity and selectivity map for eNO3RR to NH3 and HNO2, the areas marked by the black line represent the border of different selectivity. The 2D maps are shown at -0.55 V vs. RHE, marked with a standard error bar of 0.2 eV. (d) The limiting (solid black line) and path-selective steps (solid color line) for eNO3RR to NH3 and HNO2. For the one-dimensional reaction phase diagram, the shadow (light green) and blank background represent selective NH3 and HNO2 production, respectively.

Fig. 2. Kinetic barriers on FeN4 for eNO3RR to NH3 and HNO2 at -0.50 V vs. RHE (in eV). The most feasible pathways to produce NH3 and HNO2 are highlighted with green and red lines, respectively. The pink, red, light blue, brown and bright yellow represent H, O, N, C and Fe atoms, respectively.

Fig. 3. (a) Free energy diagram of eNO3RR to NH3 on FeN4 at three different potentials (-0.50, -0.66 and -0.85 V vs. RHE). (b) Free energy diagram of eNO3RR to HNO2 on FeN4 at three different potentials (-0.50, -0.66 and -0.85 V vs. RHE). (c) FE trend of NH3 and HNO2 as a function of potential by microkinetic simulations. (d) FE trend of NH3 and HNO2 as a function of potential by experiment from Ref. [14]. (e) The kinetic barriers of key elementary steps at various potentials. (f) Comparison between theoretical activity (logTOFNH3) and experimental partial current density of NH3 (logjNH3) at each applied potential ranging from -0.50 to -0.85 V vs. RHE.

Fig. 4. (a) (1-4) The geometry and ELF of key initial states for R4: NO2* → cisHNO2* and R7: NO2* → HNO2. (b) (1-4) The geometry and ELF of key transition states for R4: NO2* → cisHNO2* and R7: NO2* → HNO2. (c) The COHP analysis for the interaction of Fe-N bond in the optimized initial states for (1) R4: NO2* → cisHNO2* and (2) R7: NO2* → HNO2. (d) The COHP analysis for the interaction of H???O bond in the optimized transition states for (1) R4: NO2* → cisHNO2* and (2) R7: NO2* → HNO2. (As shown in Fig. 4(a) (1 and 2) and Fig. 4(b) (1 and 2), the pink, green, red, light blue, brown and bright yellow represent H, transferred H, O, N, C and Fe atoms, respectively.)

参考文献

[1]	D. E. Canfield, A. N. Glazer, P. G. Falkowski, Science, 2010, 330, 192-196.
[2]	R. H. Socolow, Proc. Natl. Acad. Sci. U. S. A., 1999, 96, 6001-6008.
[3]	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, 873.
[4]	J. N. Galloway, A. R. Townsend, J. W. Erisman, M. Bekunda, Z. Cai, J. R. Freney, L. A. Martinelli, S. P. Seitzinger, M. A. Sutton, Science, 2008, 320, 889-892.
[5]	H. Ward Mary, M. deKok Theo, P. Levallois, J. Brender, G. Gulis, T. Nolan Bernard, J. VanDerslice, Environ. Health Perspect., 2005, 113, 1607-1614.
[6]	J. Liang, Q. Liu, A. A. Alshehri, X. Sun, Nano Res. Energy, 2022, 1, e9120010.
[7]	J. Long, H. Li, J. Xiao, Curr. Opin. Electrochem., 2023, 37, 101179.
[8]	T. Mou, J. Long, T. Frauenheim, J. Xiao, ChemPlusChem, 2021, 86, 1211-1224.
[9]	J. Emsley, Nature, 2001, 410, 633-634.
[10]	K. Li, S. Z. Andersen, M. J. Statt, M. Saccoccio, V. J. Bukas, K. Krempl, R. Sažinas, J. B. Pedersen, V. Shadravan, Y. Zhou, D. Chakraborty, J. Kibsgaard, P. C. K. Vesborg, J. K. Nørskov, I. Chorkendorff, Science, 2021, 374, 1593-1597.
[11]	D. Bao, Q. Zhang, F.-L. Meng, H.-X. Zhong, M.-M. Shi, Y. Zhang, J.-M. Yan, Q. Jiang, X.-B. Zhang, Adv. Mater., 2017, 29, 1604799.
[12]	H. Tao, C. Choi, L.-X. Ding, Z. Jiang, Z. Han, M. Jia, Q. Fan, Y. Gao, H. Wang, A. W. Robertson, S. Hong, Y. Jung, S. Liu, Z. Sun, Chem, 2019, 5, 204-214.
[13]	F. Lü, S. Zhao, R. Guo, J. He, X. Peng, H. Bao, J. Fu, L. Han, G. Qi, J. Luo, X. Tang, X. Liu, Nano Energy, 2019, 61, 420-427.
[14]	Z.-Y. Wu, M. Karamad, X. Yong, Q. Huang, D. A. Cullen, P. Zhu, C. Xia, Q. Xiao, M. Shakouri, F.-Y. Chen, J. Y. Kim, Y. Xia, K. Heck, Y. Hu, M. S. Wong, Q. Li, I. Gates, S. Siahrostami, H. Wang, Nat. Commun., 2021, 12, 2870.
[15]	Y. Ren, C. Yu, L. Wang, X. Tan, Z. Wang, Q. Wei, Y. Zhang, J. Qiu, J. Am. Chem. Soc., 2022, 144, 10193-10200.
[16]	Y. Wang, A. Xu, Z. Wang, L. Huang, J. Li, F. Li, J. Wicks, M. Luo, D. H. Nam, C. S. Tan, Y. Ding, J. Wu, Y. Lum, C. T. Dinh, D. Sinton, G. Zheng, E. H. Sargent, J. Am. Chem. Soc., 2020, 142, 5702-5708.
[17]	Y. Wang, W. Zhou, R. Jia, Y. Yu, B. Zhang, Angew. Chem. Int. Ed., 2020, 59, 5350-5354.
[18]	Q. Hu, Y. Qin, X. Wang, Z. Wang, X. Huang, H. Zheng, K. Gao, H. Yang, P. Zhang, M. Shao, C. He, Energy Environ. Sci., 2021, 14, 4989-4997.
[19]	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.
[20]	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.
[21]	R. Jia, Y. Wang, C. Wang, Y. Ling, Y. Yu, B. Zhang, ACS Catal., 2020, 10, 3533-3540.
[22]	R. Daiyan, T. Tran-Phu, P. Kumar, K. Iputera, Z. Tong, J. Leverett, M. H. A. Khan, A. Asghar Esmailpour, A. Jalili, M. Lim, A. Tricoli, R.-S. Liu, X. Lu, E. Lovell, R. Amal, Energy Environ. Sci., 2021, 14, 3588-3598.
[23]	J. Sun, D. Alam, R. Daiyan, H. Masood, T. Zhang, R. Zhou, P. J. Cullen, E. C. Lovell, A. Jalili, R. Amal, Energy Environ. Sci., 2021, 14, 865-872.
[24]	R. Yang, H. Li, J. Long, H. Jing, X. Fu, J. Xiao, ACS Sustainable Chem. Eng., 2022, 10, 14343-14350.
[25]	T. Mou, Y. Wang, P. Deak, H. Li, J. Long, X. Fu, B. Zhang, T. Frauenheim, J. Xiao, J. Phys. Chem. Lett., 2022, 13, 9919-9927.
[26]	S. Han, C. Wang, Y. Wang, Y. Yu, B. Zhang, Angew. Chem. Int. Ed., 2021, 60, 4474-4478.
[27]	W. Zhang, H. Li, J. Xiao, X. Zhu, W. Yang, Chem. Eng. J., 2023, 456, 141060.
[28]	J.-X. Liu, D. Richards, N. Singh, B. R. Goldsmith, ACS Catal., 2019, 9, 7052-7064.
[29]	D. Xu, Y. Li, L. Yin, Y. Ji, J. Niu, Y. Yu, Front. Environ. Sci. Eng., 2018, 12, 1-9.
[30]	S. Guo, K. Heck, S. Kasiraju, H. Qian, Z. Zhao, L. C. Grabow, J. T. Miller, M. S. Wong, ACS Catal., 2018, 8, 503-515.
[31]	S. Garcia-Segura, M. Lanzarini-Lopes, K. Hristovski, P. Westerhoff, Appl. Catal. B, 2018, 236, 546-568.
[32]	I. Katsounaros, M. Dortsiou, C. Polatides, S. Preston, T. Kypraios, G. Kyriacou, Electrochim. Acta, 2012, 71, 270-276.
[33]	J. Long, X. Fu, J. Xiao, J. Mater. Chem. A, 2020, 8, 17078-17088.
[34]	J. Yang, H. Qi, A. Li, X. Liu, X. Yang, S. Zhang, Q. Zhao, Q. Jiang, Y. Su, L. Zhang, J.-F. Li, Z.-Q. Tian, W. Liu, A. Wang, T. Zhang, J. Am. Chem. Soc., 2022, 144, 12062-12071.
[35]	Y. Ma, T. Yang, H. Zou, W. Zang, Z. Kou, L. Mao, Y. Feng, L. Shen, S. J. Pennycook, L. Duan, X. Li, J. Wang, Adv. Mater., 2020, 32, 2002177.
[36]	Z. Wang, J. Zhao, J. Wang, C. R. Cabrera, Z. Chen, J. Mater. Chem. A, 2018, 6, 7547-7556.
[37]	M. Shibata, N. Furuya, J. Electroanal. Chem., 2001, 507, 177-184.
[38]	Y. Wu, Z. Jiang, Z. Lin, Y. Liang, H. Wang, Nat. Sustainable, 2021, 4, 725-730.
[39]	C. Guo, X. Fu, J. Long, H. Li, G. Qin, A. Cao, H. Jing, J. Xiao, WIREs Comput. Mol. Sci., 2021, 11, e1514.
[40]	X. Fu, J. Li, J. Long, C. Guo, J. Xiao, ACS Catal., 2021, 11, 12264-12273.
[41]	K. Chan, J. K. Norskov, J. Phys. Chem. Lett., 2015, 6, 2663-2668.
[42]	K. Chan, J. K. Norskov, J. Phys. Chem. Lett., 2016, 7, 1686-1690.
[43]	J. Chen, M. Jia, P. Hu, H. Wang, J. Comput. Chem., 2021, 42, 379-391.
[44]	V. L. Deringer, A. L. Tchougréeff, R. Dronskowski, J. Phys. Chem. A, 2011, 115, 5461-5466.
[45]	R. Dronskowski, P. E. Bloechl, J. Phys. Chem., 1993, 97, 8617-8624.
[46]	G. Kresse, J. Furthmüller, Comp. Mater. Sci., 1996, 6, 15-50.
[47]	G. Kresse, J. Furthmüller, Phys. Rev. B, 1996, 54, 11169-11186.
[48]	P. E. Blöchl, Phys. Rev. B, 1994, 50, 17953-17979.
[49]	B. Hammer, L. B. Hansen, J. K. Nørskov, Phys. Rev. B, 1999, 59, 7413-7421.
[50]	J. P. Perdew, J. A. Chevary, S. H. Vosko, K. A. Jackson, M. R. Pederson, D. J. Singh, C. Fiolhais, Phys. Rev. B, 1992, 46, 6671-6687.
[51]	S. Grimme, J. Comput. Chem., 2006, 27, 1787-1799.
[52]	R. Nelson, C. Ertural, J. George, V. L. Deringer, G. Hautier, R. Dronskowski, J. Comput. Chem., 2020, 41, 1931-1940.
[53]	G. Henkelman, B. P. Uberuaga, H. Jónsson, J. Chem. Phys., 2000, 113, 9901-9904.
[54]	M. Duca, M. T. M. Koper, Energy Environ. Sci., 2012, 5, 9726-9742.
[55]	N. Barrabes, J. Sa, Appl. Catal. B, 2011, 104, 1-5.
[56]	Y.-J. Shih, Z.-L. Wu, Y.-H. Huang, C.-P. Huang, Chem. Eng. J. 2020, 383, 123157.
[57]	J. Long, S. Chen, Y. Zhang, C. Guo, X. Fu, D. Deng, J. Xiao, Angew. Chem. Int. Ed., 2020, 59, 9711-9718.
[58]	F. Abild-Pedersen, J. Greeley, F. Studt, J. Rossmeisl, T. R. Munter, P. G. Moses, E. Skulason, T. Bligaard, J. K. Nørskov, Phys. Rev. Lett., 2007, 99, 016105.
[59]	J.-F. Chen, Y. Mao, H.-F. Wang, P. Hu, ACS Catal., 2016, 6, 7078-7087.
[60]	J. Long, C. Guo, X. Fu, H. Jing, G. Qin, H. Li, J. Xiao, J. Phys. Chem. Lett., 2021, 12, 6988-6995.

电催化硝酸盐还原合成氨电势相关性的计算见解

Computational insights on potential dependence of electrocatalytic synthesis of ammonia from nitrate

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