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Nanocrystalline olivine-structured Mg2SiO4 and MgCoSiO4, with an average particle size of 27 nm and 31 nm, respectively, were successfully synthesized from oxide precursors via mechanochemical methods. The two nanocrystalline products were obtained after milling for 360 min and displayed high concentrations of Mg2SiO4 (>94%) and MgCoSiO4 (>95%), together with minor amounts of WC (~3%) contaminant originating as debris abraded off milling balls and chambers. The macroscopic temperature monitoring of the grinding jars during milling trials recorded a peak temperature of 75 °C. A combination of analytical techniques that included XRD, TEM, SAED, and EDS were employed for the characterization of the synthesized products. 

Co 2 SiO 4 is a ceramic pigment and promising battery material of significant technological interest, as well as a model end-member of one of the most important mineral families in the Earth's crust and upper mantle. All previously developed methods for synthesis of Co 2 SiO 4 require high-temperature processing, which promotes grain growth, while the nanocrystalline form is required for some important technological applications. Here, we report a successful method for synthesizing nanocrystalline Co 2 SiO 4 via a simple and inexpensive high-energy ball milling mechanochemical process. Products of the synthesis were characterized by a combination of XRD and TEM, and their crystal structures and elemental compositions are reported. 

Abstract Image‐based machine learning tools are an ascendant ‘big data’ research avenue. Citizen science platforms, like iNaturalist, and museum‐led initiatives provide researchers with an abundance of data and knowledge to extract. These include extraction of metadata, species identification, and phenomic data. Ecological and evolutionary biologists are increasingly using complex, multi‐step processes on data. These processes often include machine learning techniques, often built by others, that are difficult to reuse by other members in a collaboration.We present a conceptual workflow model for machine learning applications using image data to extract biological knowledge in the emerging field of imageomics. We derive an implementation of this conceptual workflow for a specific imageomics application that adheres to FAIR principles as a formal workflow definition that allows fully automated and reproducible execution, and consists of reusable workflow components.We outline technologies and best practices for creating an automated, reusable and modular workflow, and we show how they promote the reuse of machine learning models and their adaptation for new research questions. This conceptual workflow can be adapted: it can be semi‐automated, contain different components than those presented here, or have parallel components for comparative studies.We encourage researchers—both computer scientists and biologists—to build upon this conceptual workflow that combines machine learning tools on image data to answer novel scientific questions in their respective fields.