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Received June 19, 2023
Revised September 5, 2023
Accepted September 9, 2023
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암모니아 합성 및 분해를 위한 촉매 탐색의 최근 연구 동향

Recent Research Trends of Exploring Catalysts for Ammonia Synthesis and Decomposition

Pukyong National University
Korean Chemical Engineering Research, November 2023, 61(4), 487-495(9), 10.9713/kcer.2023.61.4.487 Epub 1 November 2023
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암모니아는 인류의 식량문제를 해결할 수 있는 비료 생산의 주요 원료임과 동시에 무탄소 연료이면서 친환경적인

수소 운반자로서 중요한 에너지원으로 알려져 있다. 그래서 지금까지도 암모니아를 합성하거나 분해하는 기술들이 각

광을 받고 있다. 암모니아 합성 및 분해 반응을 촉진시키기 위해서는 반드시 촉매 재료가 필요하다. 고성능 및 값싼 암

모니아 합성 및 분해용 신촉매를 설계하기 위해서는 무수히 많은 합성 가능한 촉매 후보군들을 다루어야만 하는데 전

통적인 접근법만으로 탐색 및 분석을 하기엔 시간적, 경제적인 비용이 많이 들 수밖에 없다. 최근에 4차 산업혁명의 핵

심기술에 속하는 머신러닝을 이용하여 이용하여 고성능 촉매를 빠르고 정확하게 찾을 수 있는 탐색 모델이 개발되어

왔다. 본 연구에서는 암모니아 합성 및 분해용 반응 메커니즘에 대해서 알아보고, 고성능 및 경제적인 암모니아 합성

및 분해 촉매를 효율적으로 탐색할 수 있는 머신러닝 기반 방법에 대한 최신 연구 및 전망을 기술하였다.

Ammonia is either a crucial resource of fertilizer production for solving the food problem of mankind or an

important energy source as both an eco-friendly hydrogen carrier and a carbon-free fuel. Therefore, nowadays ammonia

synthesis and decomposition become promising. Then, a catalyst is required to effectively perform the ammonia synthesis

and decomposition. In order to design high-performing as well as cheap novel catalysts for ammonia synthesis and

decomposition, it is necessary to test huge amount of catalyst candidates, but it is inevitably time-consuming and

expensive to search and analyze using only traditional approaches. Recently, new methods using machine learning which is

one of the core technologies of the 4th industrial revolution that can quickly and accurately search high-performance

catalysts has been emerging. In this paper, we investigate reaction mechanisms of ammonia synthesis and decomposition, and

we described recent research and prospects of machine learning-driven methods that can efficiently find high-performing and

economical catalysts for ammonia synthesis and decomposition.


1. Ekwurzel, B., Boneham, J., Dalton, M. W., Heede, R., Mera, R.
J., Allen, M. R. and Frumho, P. C., “The Rise in Global Atmospheric
CO2, Surface Temperature, and Sea Level From Emissions
Traced to Major Carbon Producers,” Clim. Change, 144,
2. York, R. and Bell, S. E., “Energy Transitions or Additions? Why
a Transition from Fossil Fuels Requires More than the Growth
of Renewable Energy,” Energy Res. Soc. Sci., 51, 40-43(2019).
3. Hauglustaine, D., Paulot, F., Collins, W., Derwent, R., Sand, M.
and Boucher, O., “Climate Benefit of a Future Hydrogen Economy,”
Commun. Earth Environ., 3, 295(2022).
4. Bockris, J. O. M., “The Hydrogen Economy: Its History,” Int. J.
Hydrogen Energy, 38, 6, 2579-2588(2013).
5. Cha, J., Jo, Y. S., Jeong, H., Han, J., Nam, S. W., Song, K. H. and
Yoon, C. W., “Ammonia as An Efficient COX-free Hydrogen
Carrier: Fundamentals and Feasibility Analyses for Fuel Cell
Applications,” Appl. Energy, 224, 194-204(2018).
6. Navarro, R. M., Peña, M. A. and Fierro, J. L. G., “Hydrogen Production
Reactions from Carbon Feedstocks: Fossil Fuels and
Biomass,” Chem. Rev., 107, 3952-3991(2007).
7. Kalamaras, C. M. and Efstathiou, A. M., “Hydrogen Production
Technologies: Current State and Future Developments,” Conf.
Pap. Energy, 1-9(2013).
8. Züttel, A., “Hydrogen Storage Methods,” Naturwissenschaften,
91, 157-172(2004).
9. Aziz, M., Wijayanta, A. T. and Nandiyanto, A. B. D., “Ammonia
as Effective Hydrogen Storage: A Review on Production, Storage
and Utilization,” Energies, 13, 3062(2020).
10. Chen, C., Wu, K., Ren, H., Zhou, C., Luo, Y., Lin, L., Au, C. and
Jiang, L., “Ru-Based Catalysts for Ammonia Decomposition: A
Mini-Review,” Energy Fuels, 35, 11693-11706(2021).
11. Erisman, J. W., Sutton, M. A., Galloway, J., Klimont, Z. and
Winiwarter, W., “How a Century of Ammonia Synthesis Changed
the World,” Nat. Geosci., 1, 636(2008).
12. Liu, X., Elgowainy, A. and Wang, M., “Life Cycle Energy Use
and Greenhouse Gas Emissions of Ammonia Production from
Renewable Resources and Industrial By-products,” Green Chem.,
22, 5751-5761(2020).
13. Humphreys, J., Lan, R. and Tao, S., “Development and Recent
Progress on Ammonia Synthesis Catalysts for Haber-Bosch Process,”
Advanced Energy and Sustainability Research, 2, 2000043
14. Mittasch, A. and Frankenburg, W., “Early Studies of Multicomponent
Catalysts,” Adv. Catal., 2, 81-104(1950).
15. Chen, B. W. J., Xu, L. and Mavrikakis, M., “Computational Methods
in Heterogeneous Catalysis,” Chem. Rev., 121, 1007-1048
16. Logadottir, A., Rod, T. H., Nørskov, J. K., Hammer, B., Dahl, S.
and Jacobsen, C. J. H., “The Brønsted−Evans−Polanyi Relation
and the Volcano Plot for Ammonia Synthesis over Transition Metal
Catalysts,” J. Catal., 197, 229-231(2001).
17. Kim, M., Yeo, B. C., Park, Y., Lee, H. M., Han, S. S. and Kim,
D., “Artificial Intelligence to Accelerate the Discovery of N2
Electroreduction Catalysts,” Chem. Mater., 32, 709-720(2020).
18. Saidi, W. A., Shadid, W. and Veser, G., “Optimization of High-
Entropy Alloy Catalyst for Ammonia Decomposition and Ammonia
Synthesis,” J. Phys. Chem. Lett., 12, 5185-5192(2021).
19. Smart, K., “Review of Recent Progress in Green Ammonia Synthesis,”
Johns. Matthey Technol. Rev., 66, 230-244(2022).
20. Smil, V., “Enriching the Earth: Fritz Haber, Carl Bosch, and the
Transformation of World Food Production,” MIT Press: Cambridge,
21. Barański, A., Zagan, M., Pattek, A., Reizer, A., Christiansen, L.
J. and Topsøe, H., “The Activation of Iron Catalyst for Ammonia
Synthesis,” Stud. Surf. Sci. Catal., 3, 353-364(1979).
22. Logadóttir, Á. and Nørskov, J. K., “Ammonia Synthesis over a
Ru(0001) Surface Studied by Density Functional Calculations,”
J. Catal., 220, 273-279(2003).
23. Ertl, G., “Primary Steps in Catalytic Synthesis of Ammonia,” J.
Vac. Sci. Technol. A, 1, 1247-1253(1983).
24. Foster, S. L., Perez Bakovic, S. I., Duda, R. D., Maheshwari, S.,
Milton, R. D., Minteer, S. D., Janik, M. J., Renner, J. N. and
Greenlee, L. F., “Catalysts for Nitrogen Reduction to Ammonia,”
Nat. Catal., 1, 490-500(2018).
25. Deng, J., Iniguez, J. A., and Liu, C., “Electrocatalytic Nitrogen
Reduction at Low Temperature,” Joule 2, 846-856(2018).
26. Liu, S., Liu, Y., Cheng, Z., Tan, Y., Ren, Y., Yuan, T. and Shen,
Z., “Catalytic Role of Adsorption of Electrolyte/Molecules as
Functional Ligands on Two-Dimensional TM-N4 Monolayer Catalysts
for the Electrocatalytic Nitrogen Reduction Reaction,” ACS
Appl. Mater. Interfaces 13, 40590-40601(2021).
27. Lindley, B. M., Appel, A. M., Krogh-Jespersen, K., Mayer, J. M.
and Miller, A. J. M., “Evaluating the Thermodynamics of Electrocatalytic
N2 Reduction in Acetonitrile,” ACS Energy Lett., 1,
28. Ertl, G., “Mechanism and Kinetics of Ammonia Decomposition
on Iron,” J. Catal., 61, 537-539(1980).
29. Ganley, J. C., Thomas, F. S., Seebauer, E. G. and Masel, R. I.,
“A Priori Catalytic Activity Correlations: the Difficult Case of
Hydrogen Production from Ammonia,” Catal. Letters, 96, 3-4
30. Lucentini, I., Garcia, X., Vendrell, X and Llorca, J., “Review of
the Decomposition of Ammonia to Generate Hydrogen,” Ind.
Eng. Chem. Res., 60, 18560-18611(2021).
31. Lee, S. A., Lee, M. G. and Jang, H. W., “Catalysts for Electrochemical
Ammonia Oxidation: Trend, Challenge, and Promise,”
Sci. China Mater., 65, 3334-3352(2022).
32. Xi, X., Fan, Y., Zhang, K., Liu, Y., Nie, F., Guan, H. and Wu, J.,
“Carbon-free Sustainable Energy Technology: Electrocatalytic
Ammonia Oxidation Reaction,” Chem. Eng. J., 435, 134818(2022).
33. Bunce, N. J. and Bejan, D., “Mechanism of Electrochemical
Oxidation of Ammonia,” Electrochim. Acta, 56, 8085-8093(2011).
34. Oswin, H. G. and Salomon, M., “The Anodic Oxidation of
Ammonia at Platinum Black Electtrofes in Aqueous Koh Electrolyte,”
Can. J. Chem., 41, 1686-1694(1963).
35. Gerischer, H. and Mauerer, A., “Untersuchungen Zuranodischen
Oxidation Vonammoniak An Platin An Platin-elektroden,” J. Electroanal.
Chem. Interfacial Electrochem., 25, 421-433(1970).
36. Pillai, H. S. and Xin, H., “New Insights into Electrochemical
Ammonia Oxidation on Pt(100) from First Principles,” Ind. Eng.
Chem. Res., 58, 10819-10828(2019).
37. Yeo, B. C., Nam, H., Nam, H., Kim, M.-C., Lee, H. W., Kim, S.-C.,
Won, S. O., Kim, D., Lee, K.-Y., Lee, S. Y. and Han, S. S., “Highthroughput
Computational-experimental Screening Protocol for
the Discovery of Bimetallic Catalysts,” Npj Comput. Mater., 7,
38. Noh, J., Back, S., Kim, J. and Jung, Y., “Active Learning with
Non-ab Initio Input Features Toward Efficient CO2 Reduction
Catalysts,” Chem. Sci., 9, 5152(2018).
39. Panapitiya, G., Avendano-Franco, G., Ren, P., Wen, X., Li, Y. and
Lewis, J. P., “Machine-learning Prediction of CO Adsorption in
Thiolated, Ag-alloyed Au Nanoclusters,” J. Am. Chem. Soc., 140,
40. Ma, X., Li, Z., Achenie, L. E. and Xin, H., “Machine-learningaugmented
Chemisorption Model for CO2 Electroreduction Catalyst
Screening,” J. Phys. Chem. Lett., 6, 3528-3533(2015).
41. Toyao, T., Suzuki, K., Kikuchi, S., Takakusagi, S., Shimizu, K.
and Takigawa, I., “Toward Effective Utilization of Methane: Machine
Learning Prediction of Adsorption Energies on Metal Alloys,” J.
Phys. Chem. C, 122, 8315-8326(2018).
42. O’Connor, N. J., Jonayat, A. S. M., Janik, M. J. and Senftle, T.
P., “Interaction Trends Between Single Metal Atoms and Oxide
Supports Identified with Density Functional Theory and Statistical
Learning,” Nat. Catal. 1, 531-539(2018).
43. Zhao, Z.-J. and Gong, J., “Catalyst Design via Descriptors,” Nat.
Nanotechnol., 17, 563-564(2022).
44. Xie, P., Yao, Y., Huang, Z., Liu, Z., Zhang, J., Li, T., Wang, G.,
Shahbazian-Yassar, R., Hu, L. Wang, C., “High-efficient Decomposition
of Ammonia Using High-entropy Alloy Catalysts,” Nat.
Commun., 10, 4011(2019).
45. Saidi, W. A., Shadid, W. and Castelli, I. E., “Machine-learning
Structural and Electronic Properties of Metal Halide Perovskites
Using a Hierarchical Convolutional Neural Network,” Npj Comput.
Mater., 6, 36(2020).

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