{
  "abstract": "Label-free all-optical nanosizing based on interferometric or darkfield-imaging infers particle size, composition, or shape from single-particle scattering signals. These signals are, however, scaled by a spatially varying normalisation profile that combines the non-uniform illumination with position-dependent detection and data-processing effects. Common normalisation strategies estimate this profile from a direct measurement of the illumination field or background. This is not possible in background-free geometries, such as side- or light-sheet-type illumination, where the illumination does not reach the detector. Here we introduce a self-normalisation method that reconstructs the normalisation profile from the scattering signals of freely diffusing nanoparticles, without additional hardware or calibration samples. To remove the bias caused by particle-to-particle heterogeneity, single-particle trajectories are normalised against each other in regions of spatial overlap, where distinct particles sample the same normalisation profile. In a transmission geometry, the reconstructed profile agrees qualitatively with a direct illumination measurement and reduces signal variations within single trajectories from 6.5% to 2.0% (one sigma), corresponding to a diameter uncertainty of about 0.7%. Under 90-degree side-illumination, where the illumination field cannot be measured through the detection path, normalisation with the reconstructed 3D profile reduces within-trajectory signal variations from about 30% to 4-10% across most of the sampled volume. As the method solely relies on the scattering signal already acquired, our approach is directly compatible with existing interferometric and darkfield nanoscopy platforms and extendable to other scattering or fluorescent modalities, including light-sheet microscopy.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2607.14761",
  "author_total": 3,
  "authors": [
    {
      "name": "D-Dré K. J. M. J. Braam"
    },
    {
      "name": "Daan Wolters"
    },
    {
      "name": "Matz Liebel"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/em6fdbq6y6pfh.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2607.14761",
  "fulltext_url": null,
  "id": "em6fdbq6y6pfh",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2607.14761",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "physics.optics"
    ],
    [
      "openalex",
      "3107|Digital Holography and Microscopy"
    ]
  ],
  "openalex": "W7169600010",
  "pdf_url": "https://arxiv.org/pdf/2607.14761",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-30",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/em6fdbq6y6pfh.ris",
  "sources": [
    {
      "first_seen": "2026-11-02T12:21:15Z",
      "native_id": "2607.14761",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2607.14761"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.optics",
      "subfield": "optics-photonics"
    }
  ],
  "title": "Motion-Based Self-Normalisation Enables Precision Nanoparticle Sizing",
  "title_text": "Motion-Based Self-Normalisation Enables Precision Nanoparticle Sizing",
  "type": "preprint",
  "updated_at": "2026-11-04T15:20:50Z",
  "url": "https://research.draconitic.com/paper/em6fdbq6y6pfh",
  "venue": "arXiv",
  "versions": []
}