Characterisation of intergrowth in metal oxide materials using structure-mining: the case of γ-MnO2

Manganese dioxide MnO2 compounds are widely used in electrochemical applications e.g. as electrode materials or photocatalysts. One of the most used polymorphs is γ-MnO2 which is a disordered intergrowth of Pyrolusite β-MnO2 and Ramsdellite R-MnO2. The presence of intergrowth defects alters the material properties, however, they are difficult to characterise using standard X-ray diffraction due to anisotropic broadening of Bragg reflections. We here propose a characterisation method for intergrown structures by modelling of X-ray diffraction patterns and Pair Distribution Functions using γ-MnO2 as an example. Firstly, we present a model-free analysis approach, where features in experimental diffraction patterns and PDFs are matched to simulated patterns from intergrowth structures, allowing quick characterisation of defect densities. Secondly, we present a structure-mining-based analysis on experimental data using simulated γ-MnO2 superstructures. Further analysis of the structure-mining results using Machine Learning can enable the extraction of more nanostructural information such as the distribution and size of intergrown domains in the structure. Our results show that with synthesis time, the intergrowth structure reorders from a R-like to a β-like structure, with segregation of R- and β-MnO2 domains. While R-MnO2 domains keep a constant size, the β-MnO2 domains grow with synthesis time.