{
  "abstract": "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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "name": "Luca Stigliano",
      "orcid": "0000-0002-2678-8233"
    },
    {
      "name": "Julie Cosmidis"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/feqyke0dm78p3.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-78258-y",
  "fulltext_url": null,
  "id": "feqyke0dm78p3",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://www.nature.com/articles/s41598-026-78258-y",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [],
  "openalex": null,
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-07",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/feqyke0dm78p3.ris",
  "sources": [
    {
      "first_seen": "2026-11-07T12:20:25Z",
      "native_id": "10.1038/s41598-026-78258-y",
      "source": "crossref",
      "url": "https://www.nature.com/articles/s41598-026-78258-y"
    }
  ],
  "subjects": [],
  "title": "High-throughput mineralogical characterization using automated in-situ microscopy and Raman spectroscopy",
  "title_text": "High-throughput mineralogical characterization using automated in-situ microscopy and Raman spectroscopy",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T00:20:23Z",
  "url": "https://research.draconitic.com/paper/feqyke0dm78p3",
  "venue": "Scientific Reports",
  "versions": []
}