{
  "abstract": "Nanoparticle morphology governs the functional behaviour: plasmonic, catalytic, magnetic, electronic, of colloidal and supported nanomaterials, yet distributions of single-particle morphology are seldom reported. Published distributions typically aggregate ensemble averages or a few dozen manually segmented transmission electron microscopy (TEM) images, leaving population-level descriptors statistically underpowered. The rate-limiting step is instance segmentation: convolutional neural networks require extensive pixel-level annotations that are prohibitive to curate for every new particle system. Here we report TEMPOS, a deep-learning instance-segmentation package trained on procedurally generated synthetic TEM micrographs which simulate arbitrary particle distributions, substrate, noise, and imaging aberrations. TEMPOS outputs per-particle shape descriptors to a FAIR-compliant relational database and renders image archives searchable by morphological query. Distributed with a graphical interface for nondomain experts, the package reduces large-dataset analysis from weeks of manual segmentation to minutes of active work, enabling routine statistical morphometry across nanomaterials research.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Chemistry, University College London, 20 Gordon St, London, United Kingdom, WC1H 0AJ"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Natalia Sá"
    },
    {
      "affs": [
        0
      ],
      "name": "Andrew Stewart",
      "orcid": "0000-0002-3081-5644"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dkbf8zyhp5rfp.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15010426/v1",
  "fulltext_url": null,
  "id": "dkbf8zyhp5rfp",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010426/v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1315|Advanced Electron Microscopy Techniques and Applications"
    ]
  ],
  "openalex": "W7221768201",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-13",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/dkbf8zyhp5rfp.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:24Z",
      "native_id": "10.26434/chemrxiv.15010426",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010426/v1"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1315|Advanced Electron Microscopy Techniques and Applications",
      "subfield": "structural-biology"
    }
  ],
  "title": "Automating Nanoparticle Analysis in Transmission Electron Microscopy with Synthetically Trained Segmentation Models",
  "title_text": "Automating Nanoparticle Analysis in Transmission Electron Microscopy with Synthetically Trained Segmentation Models",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:24Z",
  "url": "https://research.draconitic.com/paper/dkbf8zyhp5rfp",
  "venue": "ChemRxiv",
  "versions": []
}