Characterisation of intergrowth in metal oxide materials using structure-mining: the case of γ-MnO 2 .

Manganese dioxide compounds are widely used in electrochemical applications e.g. as electrode materials or photocatalysts. One of the most used polymorphs is γ-MnO 2 which is a disordered intergrowth of pyrolusite β-MnO 2 and ramsdellite R-MnO 2 . 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 (PDF) using γ-MnO 2 as an example. Firstly, we present a fast peak-fitting 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 using simulated γ-MnO 2 superstructures which are compared to our experimental data to extract trends on defect densities with synthesis conditions. We applied the methodology to a series of γ-MnO 2 samples synthesised by a hydrothermal route. Our results show that with synthesis time, the intergrowth structure reorders from a R-like to a β-like structure, with the β-MnO 2 fraction ranging from ca . 27 to 82% in the samples investigated here. Further analysis of the structure-mining results using machine learning can enable extraction of more nanostructural information such as the distribution and size of intergrown domains in the structure. Using this analysis, we observe segregation of R- and β-MnO 2 domains in the manganese oxide nanoparticles. While R-MnO 2 domains keep a constant size of ca . 1-2 nm, the β-MnO 2 domains grow with synthesis time.