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IrO_{2} Surface Complexions Identified through Machine Learning and Surface Investigations.

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This study reveals that rutile Iridium Dioxide (IrO2) (101) facets are stable in reducing environments, matching experimental findings and theoretical predictions for advanced materials.

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Area of Science:

  • Materials Science
  • Surface Science
  • Computational Chemistry

Background:

  • Rutile Iridium Dioxide (IrO2) is a material with potential applications in catalysis and energy storage.
  • Understanding the surface structure and stability of IrO2 facets is crucial for optimizing its performance.
  • Previous studies have explored various IrO2 terminations, but a comprehensive understanding of their stability under different conditions is lacking.

Purpose of the Study:

  • To computationally determine the most stable low-index rutile IrO2 facet structures.
  • To investigate the surface reconstructions and thermodynamic stability of IrO2 facets in reducing environments.
  • To experimentally validate the predicted stable structures and their properties.

Main Methods:

  • Training a Gaussian approximation potential using density-functional theory (DFT) data.
  • Performing global geometry optimization of rutile IrO2 facets via simulated annealing.
  • Utilizing ab initio thermodynamics to predict surface stability.
  • Conducting single-crystal experiments, including x-ray photoelectron spectroscopy (XPS).

Main Results:

  • The (101) and (111) (1x1) terminations of rutile IrO2 were identified as thermodynamically competitive with the (110) termination in reducing environments.
  • Experimental studies on single crystals confirmed the dominance of (101) facets.
  • The experiments verified the theoretically predicted (1x1) periodicity and x-ray photoelectron spectroscopy core-level shifts for the (101) facets.
  • The observed structures share similarities with complexions found in ceramic battery materials.

Conclusions:

  • The (101) facet of rutile IrO2 is a stable and dominant surface termination under reducing conditions.
  • Computational methods combined with experimental validation provide accurate predictions of surface structures and properties.
  • The findings contribute to the understanding of IrO2 surface science and its potential use in energy-related applications.