Abstracts of the 3rd International Electronic Conference on Catalysis Sciences
of 64Ammonia synthesis is crucial for global fertilizer production, traditionally relying on iron-based catalysts in the Haber-Bosch process [1,2].However, the limitations of iron catalysts-such as low reaction rates and susceptibility to deactivation-have driven interest in alternative catalysts, particularly Ru-, Co-and high-entropy-based transition metal systems.The use of promoters, both in single and double settings, has become key to enhancing the efficiency and stability of these transition metal catalysts [3].This presentation focuses on the role of various promoters in optimizing Ru-, Co-and Fe-based catalysts, exploring their mechanisms and potential to advance ammonia synthesis.Results: The inclusion of promoters such as K 2 O, Ba, Ce, or BaO, among others, significantly enhances the catalytic performance of Ru-, Co-and Fe-based catalysts.For iron, these promoters improve the adsorption of N 2 and its dissociation, leading to higher ammonia yields and longer catalyst lifespans.In cobalt-based systems, these promoters stabilize the active metal sites and promote efficient nitrogen dissociation within a temperature range of below 500 C. The promoters modify the electronic properties of the catalysts, improving their overall efficiency.The nature of the rate-determining step cannot always be limited to N 2 decomposition; it can also involve other hydrogenation steps.Conclusion: Promoters are crucial in optimizing the performance of transition metal catalysts in ammonia synthesis.These promoters enhance catalyst stability, increase reaction rates, and suppress deactivation, offering a promising pathway for more efficient and sustainable ammonia production.Future research should focus on refining promoter-metal interactions to further improve catalyst performance and reduce energy consumption.
Authors
- Evangelos Topakas
- Keith L. Hohn
Institutions
- National Technical University of Athens (GR)
- Kansas State University (US)
Publication Details
- Published
- 2025-12-18
- DOI
- https://doi.org/10.3390/chemproc2025017013
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Russian Science Foundation
- Lomonosov Moscow State University