{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Zhang C"],"funding":["U.S. Department of Energy","U.S. Department of Energy (DOE)","DOE | SC | Oak Ridge National Laboratory","National Energy Research Scientific Computing Center","National Energy Research Scientific Computing Center (NERSC)","DOE | SC | Oak Ridge National Laboratory (ORNL)"],"pagination":["e2505929122"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12403145"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["122(34)"],"pubmed_abstract":["Understanding how electric fields influence water dissociation at heterogeneous interfaces is crucial for controlling interfacial chemical reactions and advancing next-generation energy technologies. Herein, ab initio-based machine learning simulations show that even small electric field changes can significantly alter the water dissociation fraction at planar TiO<sub>2</sub>-electrolyte interfaces. The resulting free energy difference between undissociated and dissociated interfacial water exhibits a linear dependence on the field change with a slope of 1.97 <i>e</i>Å, which far exceeds the dissociation-induced dipole change of a water molecule. Employing a machine-learned collective variable to investigate the reaction statistics of thousands of water dissociation/recombination events, w"],"journal":["Proceedings of the National Academy of Sciences of the United States of America"],"pubmed_title":["Tuning water dissociation at oxide-electrolyte interfaces with electric fields."],"pmcid":["PMC12403145"],"funding_grant_id":["DE-AC05-00OR22725","DE-AC02-05CH11231","DE-SC0019394"],"pubmed_authors":["Car R","Selloni A","Zhang C","Yu Z"],"additional_accession":[]},"is_claimable":false,"name":"Tuning water dissociation at oxide-electrolyte interfaces with electric fields.","description":"Understanding how electric fields influence water dissociation at heterogeneous interfaces is crucial for controlling interfacial chemical reactions and advancing next-generation energy technologies. Herein, ab initio-based machine learning simulations show that even small electric field changes can significantly alter the water dissociation fraction at planar TiO<sub>2</sub>-electrolyte interfaces. The resulting free energy difference between undissociated and dissociated interfacial water exhibits a linear dependence on the field change with a slope of 1.97 <i>e</i>Å, which far exceeds the dissociation-induced dipole change of a water molecule. Employing a machine-learned collective variable to investigate the reaction statistics of thousands of water dissociation/recombination events, w","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Aug","modification":"2026-07-16T09:22:49.446Z","creation":"2026-07-09T10:46:30.051Z"},"accession":"S-EPMC12403145","cross_references":{"pubmed":["40833416"],"doi":["10.1073/pnas.2505929122"]}}