A HydroGEN team’s discovery of a new mechanism that can boost the efficiency of hydrogen production through water splitting was featured on the ACS Applied Materials & Interfaces journal cover.
HydroGEN Advanced Water Splitting Materials Consortium
July 29, 2024A HydroGEN consortium team’s discovery of a new mechanism that can boost the efficiency of hydrogen production through water splitting was featured on the cover of the June 2024 ACS Applied Materials & Interfaces journal. The research provides new insights into the behavior of water reactivity and proton transfer under extreme confinement, which offer potential strategies to improve the performance of electrocatalysts for hydrogen production while protecting the catalyst from degradation.
“Our findings demonstrate that in extremely confined environments, the activation energy for water dissociation is reduced, leading to more frequent proton transfer events and rapid proton transport,” said Hyuna Kwon, a materials scientist in LLNL’s Quantum Simulations Group and Laboratory for Energy Applications for the Future (LEAF). “This insight could pave the way for optimizing porous oxides to improve the efficiency of hydrogen production systems by tuning the porosity and surface chemistry of the oxides.”
Publication
Kwon, H., Calegari Andrade, M.F., Ardo, S., Esposito, D.V., Pham, T.A., Ogitsu, T. Confinement Effects on Proton Transfer in TiO2 Nanopores from Machine Learning Potential Molecular Dynamics Simulations. ACS Applied Materials & Interfaces (2024). https://doi.org/10.1021/acsami.4c02339
Background
Improved understanding of proton transfer in nanopores is critical for a wide range of applications, such as the development of efficient and affordable electrolyzers for hydrogen production, but experimentally probing mechanisms and energetics of this process are not well understood. To address this knowledge gap, the team developed and applied a machine learning potential derived from first-principles calculations to examine water reactivity and proton transfer in TiO2 slit-pores. The availability of a well-constructed machine learning data set holds the potential for accelerating discoveries about the electrochemical interface.
The research was supported through a joint effort between the HydroGEN Energy Materials Network as well as two of the U.S. Department of Energy’s Office of Science Basic Energy Sciences program’s research centers Ensembles of Photosynthetic Nanoreactors and Center of Nanofluidic Transport.
Authors and Affiliations
- Lawrence Livermore National Laboratory – Hyuna Kwon, Marcos F. Calegari Andrade, Tuan Anh Pham, Tadashi Ogitsu
- Columbia University – Daniel V. Esposito
- University of California, Irvine – Shane Ardo