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For a systematic materials selection and for design and synthesis of systems for electrochemical energy conversion with specific properties, it is essential to clarify the general relationship between physicochemical properties of the materials and the electrocatalytic performance and stability of the system or device. The design of highly performant and durable 3D electrocatalysts requires an optimized hierarchical morphology and surface structures with high activity. A systematic approach to determine the 3D morphology of hierarchically structured materials with high accuracy is described, based on a multi-scale X-ray tomography study. It is applied to a novel transition-metal-based materials system: MoNi4 electrocatalysts anchored on MoO2 cuboids aligned on Ni foam. The high accuracy of 3D morphological data of the formed micro- and nanostructures is ensured by applying machine learning algorithms for the correction of imaging artefacts of high-resolution X-ray tomography such as beam hardening and for the compensation of experimental inaccuracies such as misalignment and motions of samples and tool components. This novel approach is validated based on the comparison of virtual cross-sections through the MoNi4 electrocatalysts and real FIB cross-sections imaged in the SEM.

Multi-scale X-ray tomography and machine learning algorithms to study MoNi4 electrocatalysts anchored on MoO2 cuboids aligned on Ni foam