TY - JOUR

T1 - Characterisation of intergrowth in metal oxide materials using structure-mining

T2 - the case of gamma-MnO2

AU - Magnard, Nicolas P. L.

AU - Anker, Andy S.

AU - Aalling-Frederiksen, Olivia

AU - Kirsch, Andrea

AU - Jensen, Kirsten M. O.

N1 - Correction: https://doi.org/10.1039/d3dt90071a

PY - 2022

Y1 - 2022

N2 - Manganese dioxide compounds are widely used in electrochemical applications e.g. as electrode materials or photocatalysts. One of the most used polymorphs is gamma-MnO2 which is a disordered intergrowth of pyrolusite beta-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 (PDF) using gamma-MnO2 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 gamma-MnO2 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 gamma-MnO2 samples synthesised by a hydrothermal route. Our results show that with synthesis time, the intergrowth structure reorders from a R-like to a beta-like structure, with the beta-MnO2 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 beta-MnO2 domains in the manganese oxide nanoparticles. While R-MnO2 domains keep a constant size of ca. 1-2 nm, the beta-MnO2 domains grow with synthesis time.

AB - Manganese dioxide compounds are widely used in electrochemical applications e.g. as electrode materials or photocatalysts. One of the most used polymorphs is gamma-MnO2 which is a disordered intergrowth of pyrolusite beta-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 (PDF) using gamma-MnO2 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 gamma-MnO2 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 gamma-MnO2 samples synthesised by a hydrothermal route. Our results show that with synthesis time, the intergrowth structure reorders from a R-like to a beta-like structure, with the beta-MnO2 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 beta-MnO2 domains in the manganese oxide nanoparticles. While R-MnO2 domains keep a constant size of ca. 1-2 nm, the beta-MnO2 domains grow with synthesis time.

KW - PAIR DISTRIBUTION FUNCTION

KW - ELECTROCHEMICAL PROPERTIES

KW - HYDROTHERMAL SYNTHESIS

KW - MNO2 NANOSTRUCTURES

KW - MANGANESE OXIDES

KW - WATER OXIDATION

KW - INSERTION

KW - MICROSTRUCTURE

KW - PROGRAM

KW - NANOPARTICLES

U2 - 10.1039/d2dt02153f

DO - 10.1039/d2dt02153f

M3 - Journal article

C2 - 36156665

VL - 51

SP - 17150

EP - 17161

JO - Dalton Transactions (Online)

JF - Dalton Transactions (Online)

SN - 1477-9234

IS - 45

ER -