First-principles characterisation and comparison of clean, hydrated, and defect α-Al₂O₃ and α-Fe₂O₃ (110) surfaces

ABSTRACT

Structural models of the (110) termination of α-Al₂O₃ and α-Fe₂O₃ are studied using Density Functional Theory (DFT) calculations and thermodynamics to determine the details of the mineral-water interface structure and stability. Prior experiments have characterised these interfaces, but the X-ray reflectivity techniques used cannot identify the distribution of protons on surface sites. In addition, the alumina (110) surface displays two different surface structures stable in water, with their occurrence dependent on sample preparation conditions. We use theory and modelling to determine the thermodynamically preferred surface structures, including protonation states of surface oxygen functional groups, as a function of temperature and pressure. Consistent with studies of other facets of alumina and hematite, we find that thermodynamically unfavourable defect structures, upon hydration and hydroxylation, show comparable stability to the hydrated forms of ideal terminations. The model results are compared to experimental characterisation of hydrated (110) alumina and hematite surfaces with good agreement between the best-fit structures from experiments and the lowest surface free energy structures from theory and modelling. Electronic structure analysis of the exposed surface functional groups is presented, and we highlight instances in which the electronic density of states differs between oxygen functional groups that have similar coordination environments.

KEYWORDS:

• Alumina
• hematite
• surface characterisation
• environmental interfaces
• DFT

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the U.S. National Science Foundation (NSF) Environmental Chemical Sciences Program, award number CHE-1505766 and CHE-1505532. This research was supported in part through computational resources provided by The University of Iowa, Iowa. This work used the Extreme Science and Engineering Discovery Environment (XSEDE), [86] which is supported by the National Science Foundation Grant Number ACI-1548562.