{
  "abstract": "Precise control of silver nanoparticle (Ag-NP) size is critical for application-specific performance, yet identification of optimal synthesis conditions remains reliant on trial-and-error experimentation. Here, we report a machine learning inverse design framework trained on a combined experimental dataset spanning batch and continuous flow reactor configurations. Five regression algorithms were evaluated as candidate surrogate models, with Gaussian process (GP) regression emerging as the best-performing model (coefficient of determination (R2) = 0.91 and cross-validation score = 0.80). A random sampling strategy was employed to explore the parameter space and identify synthesis conditions capable of achieving user-defined target Ag-NP hydrodynamic diameters. To differentiate between the multiple feasible solutions, a composite candidate scoring function was implemented to rank synthesis conditions according to the error, estimated Ag-NP dispersity, and posterior uncertainty. The framework was validated across 21 inverse design predictions spanning a target size range of 20–70 nm, achieving average size errors of 3.4% and 7.4% for the batch and flow reactors, respectively. Collectively, this work establishes an experimentally validated inverse design framework capable of identifying synthesis conditions for target Ag-NP hydrodynamic diameters via citrate- and tannic acid-mediated reduction of silver nitrate.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Chemical Engineering, University of Bath Bath BA2 7AY UK"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Joseph Carver",
      "orcid": "0009-0008-7154-9873"
    },
    {
      "affs": [
        0
      ],
      "name": "Y. M. John Chew"
    },
    {
      "affs": [
        0
      ],
      "name": "Semali Perera"
    },
    {
      "affs": [
        0
      ],
      "name": "Sourav Chatterjee"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/174gggbg2t9w1.bib",
  "cited_by": 0,
  "doi": "10.1039/d6na00557h",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13592209/",
  "id": "174gggbg2t9w1",
  "issns": [
    "2516-0230"
  ],
  "landing_url": "https://doi.org/10.1039/d6na00557h",
  "language": "en",
  "licence": "CC-BY-3.0",
  "licence_label": "CC BY 3.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/3.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Chemistry"
    ],
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7213873846",
  "pdf_url": "https://pubs.rsc.org/na/article-pdf/doi/10.1039/d6na00557h/14743333/d6na00557h.pdf",
  "pmcid": "PMC13592209",
  "pmid": "42769686",
  "published": "2026-09-21",
  "publisher": "Royal Society of Chemistry",
  "ris": "https://research.draconitic.com/paper/174gggbg2t9w1.ris",
  "sources": [
    {
      "first_seen": "2026-10-31T12:20:59Z",
      "native_id": "PMC13592209",
      "source": "pmc",
      "url": "https://doi.org/10.1039/d6na00557h"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "Data-driven machine learning optimisation of silver nanoparticle synthesis",
  "title_text": "Data-driven machine learning optimisation of silver nanoparticle synthesis",
  "type": "peer-reviewed",
  "updated_at": "2026-10-31T12:20:59Z",
  "url": "https://research.draconitic.com/paper/174gggbg2t9w1",
  "venue": "Nanoscale Advances",
  "versions": []
}