Structure of a model TiO2 photocatalytic interface
arXiv:1611.06146 · doi:10.1038/nmat4793
Abstract
The interaction of water with TiO2 is crucial to many of its practical applications, including photocatalytic water splitting. Following the first demonstration of this phenomenon 40 years ago there have been numerous studies of the rutile single-crystal TiO2(110) interface with water. This has provided an atomic-level understanding of the water-TiO2 interaction. However, nearly all of the previous studies of water/TiO2 interfaces involve water in the vapour phase. Here, we explore the interfacial structure between liquid water and a rutile TiO2(110) surface pre-characterized at the atomic level. Scanning tunnelling microscopy and surface X-ray diffraction are used to determine the structure, which is comprised of an ordered array of hydroxyl molecules with molecular water in the second layer. Static and dynamic density functional theory calculations suggest that a possible mechanism for formation of the hydroxyl overlayer involves the mixed adsorption of O2 and H2O on a partially defected surface. The quantitative structural properties derived here provide a basis with which to explore the atomistic properties and hence mechanisms involved in TiO2 photocatalysis.
References in corpus (1)
Cited by in corpus (10)
- Coupling of surface chemistry and electric double layer at TiO electrochemical interfaces
- Measuring surface charge: why experimental characterization and molecular modeling should be coupled
- Tuning the stability of Electrochemical Interfaces by Electron Transfer reactions
- Small polarons and the Janus nature of
- Apparatus for dosing liquid water in ultrahigh vacuum
- On shape dependence of the toxicity of rutile nanoparticles
- Quantum-chemical studies of rutile nanoparticles toxicity I. Defect-free rod-like model clusters
- The color center singlet state of oxygen vacancies in TiO
- Optical Stark Effect of a Single Defect on TiO2(110) Surface
- Atomistic Simulations of Oxide-Water Interfaces using Machine Learning Potentials