{
  "abstract": "Biodiesel is a promising sustainable alternative to fossil diesel, but its deployment is limited by challenges in catalyst efficiency, stability, reusability, and process optimization. Metal-doped activated carbon (AC) and biochar-based catalysts have attracted considerable attention owing to their high surface area, tunable porosity, and low-cost production from waste biomass. In parallel, machine learning (ML) has emerged as a powerful tool for biodiesel yield prediction, process modeling, and optimization. However, these research areas have largely progressed separately. This review combines scientometric analysis with a systematic review to examine advances in waste-derived AC and biochar-based catalysts and ML applications in biodiesel production. Catalyst synthesis, activation, metal doping, physicochemical properties, catalytic performance, stability, and reusability are critically evaluated alongside ML-based prediction and process optimization approaches. An integrated catalyst-process-performance framework is further discussed to highlight opportunities for incorporating catalyst descriptors into data-driven biodiesel modeling. The review identifies key research trends, knowledge gaps, and future opportunities for advancing sustainable and scalable biodiesel production.",
  "abstract_shown": true,
  "affiliations": [
    "Chemical Engineering Department, Faculty of Engineering, Cairo University Giza 12613 Egypt menna.202211310@eng-st.cu.edu.eg.",
    "Public Works Department, Faculty of Engineering, Cairo University Giza 12613 Egypt."
  ],
  "article_type": "review article",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Menna Ebrahim",
      "orcid": "0009-0005-6153-8431"
    },
    {
      "affs": [
        1
      ],
      "name": "Mostafa Mahmoud",
      "orcid": "0000-0002-9100-7966"
    },
    {
      "affs": [
        0
      ],
      "name": "Fatma H Ashour",
      "orcid": "0000-0001-7541-4644"
    },
    {
      "affs": [
        0
      ],
      "name": "Osama A Ibrahim"
    },
    {
      "affs": [
        0
      ],
      "name": "Nourhan H Khashaba",
      "orcid": "0000-0001-5571-1182"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7eqwfcvmw6r8c.bib",
  "cited_by": 0,
  "doi": "10.1039/d6ra05871j",
  "fulltext_url": "https://europepmc.org/articles/PMC13678681",
  "id": "7eqwfcvmw6r8c",
  "issns": [
    "2046-2069"
  ],
  "landing_url": "https://europepmc.org/article/MED/42898746",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2204|Biodiesel Production and Applications"
    ]
  ],
  "openalex": "W7222960555",
  "pdf_url": "https://europepmc.org/articles/PMC13678681?pdf=render",
  "pmcid": "PMC13678681",
  "pmid": "42898746",
  "published": "2026-10-19",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/7eqwfcvmw6r8c.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:32Z",
      "native_id": "MED:42898746",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42898746"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "issn:2046-2069",
      "subfield": "general"
    }
  ],
  "title": "Bridging waste-derived carbon catalyst innovation and machine learning for sustainable biodiesel production: a comprehensive review.",
  "title_text": "Bridging waste-derived carbon catalyst innovation and machine learning for sustainable biodiesel production: a comprehensive review.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:19:32Z",
  "url": "https://research.draconitic.com/paper/7eqwfcvmw6r8c",
  "venue": "RSC advances",
  "versions": []
}