Performance-oriented model learning for control via multi-objective Bayesian optimization

A Study on the Role of Electric Field in Low-Temperature Plasma Catalytic Ammonia Synthesis via Integrated Density Functional Theory and Microkinetic Modeling

Low-temperature plasma catalysis has shown promise for various chemical processes such as light hydrocarbon conversion, volatile organic compounds removal, and ammonia synthesis. Plasma-catalytic ammonia synthesis has the potential advantages of leveraging renewable energy and distributed manufacturing principles to mitigate the pressing environmental challenges of the energy-intensive Haber-Bosh process, towards sustainable ammonia production. However, lack of foundational understanding of plasma-catalyst interactions poses a key challenge to optimizing plasma-catalytic processes. Recent studies suggest electro- and photoeffects, such as electric field and charge, can play an important role in enhancing surface reactions. These studies mostly rely on using density functional theory (DFT) to investigate surface reactions under these effects. However, integration of DFT with microkinetic modeling in plasma catalysis, which is crucial for establishing a comprehensive understanding of the interplay between the gas-phase chemistry and surface reactions, remains largely unexplored. This paper presents a first-principles framework coupling DFT calculations and microkinetic modeling to investigate the role of electric field on plasma-catalytic ammonia synthesis. The DFT-microkinetic model shows more consistent predictions with experimental observations, as compared to the case wherein the variable effects of plasma process parameters on surface reactions are neglected. In particular, predictions of the DFT-microkinetic model indicate electric field can have a notable effect on surface reactions relative to other process parameters. A global sensitivity analysis is performed to investigate how ammonia synthesis pathways will change in relation to different plasma process parameters. The DFT-microkinetic model is then used in conjunction with active learning to systematically explore the complex parameter space of the plasma-catalytic ammonia synthesis to maximize the amount of produced ammonia while inhibiting reactions dissipating energy, such as the recombination of H2 through gas-phase H radicals and surface-adsorbed H. This paper demonstrates the importance of accounting for the effects of electric field on surface reactions when investigating and optimizing the performance of plasma-catalytic processes.