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Understanding proton transfer and water splitting reactions in nano-porous materials is critical for a wide range of emerging technologies, including hydrogen production through photoelectrochemical water splitting. However, elucidating mechanism and energetics of these processes remains a significant challenge for experimental probes. In this work, we combine large-scale molecular dynamics simulations with machine learning potential derived from first-principles calculations to investigate kinetics of proton transfer in nano-porous TiO₂ as a representative photocatalyst material. We developed and applied a deep neural network potential to reconstruct the free energy surface of water dissociation and proton transport for a wide range of pore sizes to elucidate confinement effects. We show that reactivity of porous TiO₂ with water recovers its behavior at the interface with the bulk liquid for a pore diameter larger than 10 Å. On the other hand, confinement below 5 Å diameter significantly affects the water reactivity and proton transport near the interface with the pores. Specifically, we show that a limited hydrogen bond network in these narrow pores facilitates redox reactions and enhances proton transfer. Our study highlights the critical competition between kinetic and thermodynamic factors introduced by nano-confinement, suggesting potential strategies for optimization of photocatalytic systems for efficient water splitting reactions.

This work was supported as part of the Center for Enhanced Nanofluidic Transport (CENT), an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences under award DE-SC0019112. T.O. acknowledges support from Ensembles of Photosynthetic Nanoreactors (EPN) funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences. The work at the Lawrence Livermore National Laboratory was performed under the auspices of the U.S. Department of Energy under contract DE-AC52-07NA27344.

Co-Author(s): Marcos Calegari (Lawrence Livermore National Laboratory), Tadashi Ogitsu (Lawrence Livermore National Laboratory), Tuan Anh Pham (Lawrence Livermore National Laboratory)

Hyuna Kwon

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