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Computationally aided development of materials to enable environmentally friendly NH₃ synthesis and storage
Liu, Tsung Wei
Liu, Tsung Wei
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2024
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2026-04-04
Abstract
Ammonia production relates to 1 % of the world's CO2 emissions. However, ammonia is necessary for fertilizer production, and it may also be useful in the future as a hydrogen carrier. Therefore ammonia synthesis must be decarbonized, which probably requires using green hydrogen as a feedstock. The decentralized and intermittent nature of the current green hydrogen production demands intermittent reactor operation. Such a scenario requires ammonia synthesis to be done under mild conditions to prevent spending time in the reactor's heat up and pressure up. Plasma catalysis is one of the promising approaches for synthesizing ammonia under milder conditions because it improves the reaction kinetics by utilizing input energy power to activate or pre-activate the reactants. But, the understanding of the
synergy between plasma and catalysis is in a preliminary stage and hinders plasma catalysis from being economically feasible for ammonia synthesis.
This thesis developed two different classes of materials, metal–organic frameworks (MOFs) and alloy catalysts, to improve plasma-assisted ammonia synthesis by conducting multi-scale molecular simulations and using machine learnings. First, I conducted an energetic study to understand the kinetics and thermodynamics of over 51 reaction pathways in plasma catalysis through Density Functional Theory (DFT) calculations. A robust catalyst descriptor (experimental turn over frequencies linearly correlate with reaction energy for the Eley–Rideal hydrogenation reaction H• + HNNH2* → HNNH3*) was identified by the analysis of the energetics and the experimental results. In addition, based on the minimal plasma microkinetic modeling, which used DFT energetics as input, I clarified the role of chemical species (including H•, N•, and N2HY) in plasma catalysis and the dominant ammonia formation pathways under different model assumptions. Eventually, I discovered promising catalysts through hierarchical screening and genetics algorithms on the basis of the catalyst nitrophobicity and the evaluated descriptor. On the other hand, I studied two approaches to take advantage of porous materials to protect ammonia from decomposition due to collisions with energetic plasma species. Using an innovative machine-learning framework I found MOFs suitable to protect ammonia as adsorbents or membranes depending on the approach evaluated. In the last chapter, I pinpoint MOFs for ammonia storage under conditions more feasible than ammonia condensation through Vendi-Bayesian optimization. A more comprehensive understanding of plasma catalysis and the promising materials proposed in this thesis will provide meaningful improvement in ammonia synthesis powered by green hydrogen.
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