Exploring active surfaces of ZnxCryOz for syngas conversion via MLPs simulation

Abstract

The identification of true active surfaces under operando conditions is essential for the targeted development of catalysts and the advancement of catalytic science. However, pinpointing these active surfaces remains a significant challenge due to the inherent complexity of heterogeneous catalysts and the reactions systems. Traditionally, research has primarily relied on first-principles calculations, which, although insightful, are constrained by their focus on simplified models of catalyst structures. This limitation often restricts the thorough exploration of true active surfaces. Recently, the emergence of machine learning potentials (MLPs) has offered a solution by combining the computational efficiency of empirical methods with the accuracy of density functional theory (DFT) calculations. This advancement enables the exploration of vast structural spaces and enhances our capability to study complex catalytic systems in greater detail. Our research focuses on the ZnxCryOz heterogeneous catalyst, particularly its application in converting syngas (CO/H2) into light olefins—a critical industrial process. The outcomes of this research provide several key insights: (a) We examine the potential active surface of ZnO, exploring how the concentration and distribution of oxygen vacancies (OVs) influence the catalytic activation of syngas conversion. (b) We establish principles for investigating metal-doped metal oxides towards CO activation, offering a methodological framework to study active surfaces in metal-doped system. (c) We propose a comprehensive methodological framework for identifying true active surfaces in complex heterogeneous catalyst systems, encompassing Bulk Stability (Phase Separation vs. Phase Mixing), Surface Stability (Thermodynamic/Dynamic) and Reaction Activity Evaluation. This approach significantly contributes to the field of catalysis by identifying the true active surfaces under the reaction conditions, thereby aiding in the rational design and optimization of next-generation catalysts. Our research guides future theoretical studies and practical applications, paving the way for the development of superior catalysts that can efficiently accelerate industrially significant chemical reactions.

Thesis is embargoed until 31 December 2027.

Date of Award	Dec 2024
Original language	English
Awarding Institution	Queen's University Belfast
Sponsors	Chinese Scholarship Council (CSC)
Supervisor	Peijun Hu (Supervisor) & Meilan Huang (Supervisor)