High-throughput mineralogical characterization using automated in-situ microscopy and Raman spectroscopy

Mineral precipitation in natural and engineered systems is controlled by complex interactions among physicochemical variables and organic molecules, making large-scale systematic exploration of mineralization pathways challenging. Here we present a high-throughput experimental and analytical workflow that enables rapid, particle-resolved characterization of mineral formation. The platform integrates miniaturized mineralization experiments in multi-well plates with automated optical microscopy, particle detection, morphometric analysis, machine-learning classification, and targeted Raman spectromicroscopy. Applied to calcium carbonate precipitation mediated by organic additives, the workflow enables particle-resolved morphological analysis and mineral phase identification. Across 307 experiments involving 23 organic additives, automated imaging revealed ten reproducible particle morphologies. Raman spectromicroscopy showed that each morphotype consistently corresponds to a single mineral phase, either calcite or vaterite. A Random Forest model trained on morphological descriptors classified particle types with a mean accuracy of 97 ± 1%, enabling automated quantification of morphotype populations across experimental conditions. Systematic screening showed that organic additives strongly influence particle populations, modifying particle number, size distributions, and accessible particle morphologies. Time-lapse imaging further enables rapid estimation of particle growth rates, providing a practical metric to identify conditions that promote or inhibit mineralization. This integrated workflow provides a scalable framework for mapping mineralization pathways across multidimensional chemical spaces.