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Protonation dynamics of confined ethanol–water mixtures in H-ZSM-5 from machine learning-driven metadynamics

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Abstract

Zeolites are indispensable heterogeneous catalysts in industrial chemical processes, valued for their strong Brønsted acidity, well-defined microporous frameworks, and tunable pore structures. Their catalytic activity arises primarily from Brønsted acid sites (BAS), typically present as bridging hydroxyl groups (Si–OH–Al). Under aqueous reaction conditions, these protons interact dynamically with water and alcohol molecules, leading to complex solvation and protonation behavior within confined pores. In this study, we investigate the protonation equilibrium occurring between ethanol and water at the BAS of acidic zeolites under varying hydration levels, i.e., C2H5OH–(H2O)n, n = 1–4. Local structure was analyzed through an adaptive-learning global optimization algorithm, while enhanced sampling molecular dynamics simulations with Well-Tempered Metadynamics (WTMetaD) and machine learning interatomic potentials (MLPs) provide free-energy surfaces (FES) at variable hydration levels. The results reveal a strong dependence of proton localization on the degree of hydration. In presence of just 1 water molecule, the proton resides predominantly on ethanol; with 2 water molecules, it shifts toward water, and starting at 3, it becomes delocalized over the water cluster. These findings underscore the critical role of solvation in modulating acid site behavior and suggest that a minimum of three water molecules is necessary to fully stabilize the proton on water within the zeolite framework. This solvation threshold has significant implications for catalytic processes, particularly in biomass conversion reactions where alcohol protonation is a key step in dehydration mechanisms.

Original languageEnglish
Article number116658
JournalJournal of Catalysis
Volume454
DOIs
StatePublished - Feb 2026

Funding

P.J and G.P. gratefully acknowledge the grant funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) under Germany’s Excellence Strategy, Cluster of Excellence 2186 “ The Fuel Science Center ” ID 390919832 . They also acknowledge computing time provided to them at the NHR Center NHR4CES at RWTH Aachen University (project ID p0024037 ). This is funded by the Federal Ministry of Research, Technology and Space , and the state governments participating on the basis of the resolutions of the GWK for national high performance computing at universities. B.A.J. and M.-S.L. were supported by the U.S. Department of Energy (DOE), Office of Science , Office of Basic Energy Sciences (BES), Division of Chemical Sciences , and Geosciences & Biosciences at Pacific Northwest National Laboratory (PNNL) ( FWP 47319 ). PNNL is a multiprogram national laboratory operated for DOE by Battelle under Contract DE-AC05-76RL01830 . D.Z., V.-A.G., and R.R. acknowledge the support from the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Chemical Sciences, Geosciences, and Biosciences Division, Catalysis Science Program (Grant ERKCC96 ) at Oak Ridge National Laboratory (ORNL). ORNL is operated by UT-Battelle under contract no. DE-AC05-00OR22725 for the U.S. Department of Energy. This research partly used resources of the National Energy Research Scientific Computing Center (NERSC), a Department of Energy User Facility using NERSC award BES-ERCAP0032412 and BES-ERCAP0032671 . The views expressed herein are those of the authors and do not reflect the position of the United States Military Academy , the Department of the Army , the Department of Defense , or the U.S. Government . P.J and G.P. gratefully acknowledge the grant funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy, Cluster of Excellence 2186 “The Fuel Science Center” ID 390919832. They also acknowledge computing time provided to them at the NHR Center NHR4CES at RWTH Aachen University (project ID p0024037). This is funded by the Federal Ministry of Research, Technology and Space, and the state governments participating on the basis of the resolutions of the GWK for national high performance computing at universities. B.A.J. and M.-S.L. were supported by the U.S. Department of Energy (DOE), Office of Science, Office of Basic Energy Sciences (BES), Division of Chemical Sciences, and Geosciences & Biosciences at Pacific Northwest National Laboratory (PNNL) (FWP 47319). PNNL is a multiprogram national laboratory operated for DOE by Battelle under Contract DE-AC05-76RL01830. D.Z. V.-A.G. and R.R. acknowledge the support from the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Chemical Sciences, Geosciences, and Biosciences Division, Catalysis Science Program (Grant ERKCC96) at Oak Ridge National Laboratory (ORNL). ORNL is operated by UT-Battelle under contract no. DE-AC05-00OR22725 for the U.S. Department of Energy. This research partly used resources of the National Energy Research Scientific Computing Center (NERSC), a Department of Energy User Facility using NERSC award BES-ERCAP0032412 and BES-ERCAP0032671. The views expressed herein are those of the authors and do not reflect the position of the United States Military Academy, the Department of the Army, the Department of Defense, or the U.S. Government.

Keywords

  • Global optimization
  • Machine learning
  • Metadynamics
  • Molecular dynamics
  • Protonation equilibrium

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