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Deep Learning-Assisted Investigation of Electric Field-Dipole Effects on Catalytic Ammonia Synthesis.

ABSTRACT: External electric fields can modify binding energies of reactive surface species and enhance catalytic performance of heterogeneously catalyzed reactions. In this work, we used density functional theory (DFT) calculations-assisted and accelerated by a deep learning algorithm-to investigate the extent to which ruthenium-catalyzed ammonia synthesis would benefit from application of such external electric fields. This strategy allows us to determine which electronic properties control a molecule's degree of interaction with external electric fields. Our results show that (1) field-dependent adsorption/reaction energies are closely correlated to the dipole moments of intermediates over the surface, (2) a positive field promotes ammonia synthesis by lowering the overall energetics and decreasing the activation barriers of the potential rate-limiting steps (e.g., NH₂ hydrogenation) over Ru, (3) a positive field (>0.6 V/Å) favors the reaction mechanism by avoiding kinetically unfavorable N≡N bond dissociation over Ru(1013), and (4) local adsorption environments (i.e., dipole moments of the intermediates in the gas phase, surface defects, and surface coverage of intermediates) influence the resulting surface adsorbates' dipole moments and further modify field-dependent reaction energetics. The deep learning algorithm developed here accelerates field-dependent energy predictions with acceptable accuracies by five orders of magnitudes compared to DFT alone and has the capacity of transferability, which can predict field-dependent energetics of other catalytic surfaces with high-quality performance using little training data.

SUBMITTER: Wan M

PROVIDER: S-EPMC9241008 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description: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.

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Deep Learning-Assisted Investigation of Electric Field-Dipole Effects on Catalytic Ammonia Synthesis.

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