INTDKIN · Reaction Mechanism of Methanol Conversion in Zeolite by Integrated Diffusion/Reaction Kinetics Model

Reaction Mechanism of Methanol Conversion in Zeolite by Integrated Diffusion/Reaction Kinetics Model

The INTDKIN project was developed in response to several long-standing scientific and technological challenges in heterogeneous catalysis, particularly in the field of zeolite-catalyzed C1 chemistry and sustainable carbon conversion. Zeolite catalysts are widely used in industrial processes such as methanol-to-hydrocarbons (MTH), syngas conversion, and CO2 reduction because of their strong acidity, shape selectivity, and confined microporous structures. These catalytic technologies are highly relevant for the transition toward sustainable chemical production and low-carbon energy systems, especially within the broader context of carbon-neutral and circular-carbon strategies promoted by the European Green Deal and related international sustainability initiatives. Despite decades of research, the fundamental reaction mechanisms governing methanol and CO2 conversion in zeolites remain incompletely understood. One of the major unresolved problems is the difficulty in quantitatively describing the strong coupling between molecular diffusion and chemical reactions inside zeolite micropores. Conventional static density functional theory (DFT) approaches have been highly successful in identifying elementary reaction pathways under idealized conditions, but they often neglect the dynamic nature of catalytic processes under realistic reaction environments. In particular, the diffusion and confinement effects of reactive oxygenate intermediates within zeolite channels can substantially influence reaction thermodynamics, kinetics, and catalytic selectivity, yet these effects are difficult to capture using traditional computational methods. This limitation has contributed to persistent discrepancies between theoretical predictions and experimental observations in many zeolite-catalyzed reactions. The INTDKIN project aimed to address these challenges by developing an integrated diffusion/reaction kinetics model capable of describing diffusion–reaction entanglement and reaction mechanism during methanol conversion in zeolites at the quantum-mechanical level. The overall objective of the project was to establish a new computational methodology that combines ab initio molecular dynamics (AIMD), machine-learning-potential molecular dynamics (MLP-MD), enhanced sampling techniques, and free-energy-based kinetic analysis to investigate catalytic reaction mechanisms under realistic reaction conditions. By moving beyond conventional static calculations toward dynamic simulations, the project sought to provide a more realistic and comprehensive description of catalytic processes occurring in confined zeolite environments. A second major objective of the project was to apply the developed methodology to important reactions in sustainable C1 chemistry, including methanol-to-hydrocarbons conversion, syngas conversion, and CO2-to-hydrocarbons processes. Through these studies, the project aimed to uncover the dynamic evolution of key oxygenate intermediates within zeolite micropores and clarify how diffusion–reaction coupling governs catalytic selectivity and reactivity. In particular, the project addressed fundamental scientific questions related to the induction period of methanol-to-hydrocarbons conversion and proposed a formaldehyde-mediated carbon–carbon bond formation mechanism, contributing new mechanistic understanding to one of the long-standing debates in zeolite catalysis. The project pathway to impact was built around the development and dissemination of advanced computational methodologies capable of bridging the gap between theoretical simulations and experimentally observed catalytic behavior. By enabling quantitative descriptions of diffusion–reaction entanglement, the project provides researchers with new tools for understanding catalytic processes under realistic operating conditions. The developed framework is expected to support the rational design, optimization, and screening of zeolite catalysts with improved catalytic efficiency, product selectivity, and energy utilization. The methodology is broadly applicable not only to methanol and CO2 conversion, but also to wider classes of heterogeneous catalytic systems involving confined reactions and dynamic catalytic environments. The expected impacts of the project extend across scientific, technological, and strategic dimensions. Scientifically, the project contributes to advancing the state of the art in computational heterogeneous catalysis by integrating dynamic simulations, enhanced sampling methods, machine-learning-assisted modeling, and high-performance computing into a unified framework. Technologically, the project supports the development of cleaner and more efficient catalytic processes relevant to sustainable fuel and chemical production. From a broader strategic perspective, the project aligns strongly with European priorities related to sustainable carbon utilization, digital transformation, and advanced computational technologies. In particular, the integration of machine learning and large-scale simulations contributes to the development of digital and data-driven approaches in materials and catalysis research, supporting the strategic objective of strengthening Europe’s leadership in sustainable and digital technologies. The project results are also relevant to a wide range of stakeholders, including academic researchers in catalysis and theoretical chemistry, industrial researchers in petrochemical and energy-related sectors, and developers of computational methodologies and digital tools for materials science. The dissemination of the project outcomes through high-impact scientific publications, international conferences, and research collaborations further strengthens the project’s pathway toward long-term scientific and technological impact.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз