{
  "abstract": "Laser-driven photocatalysis provides a wavelength-tunable approach for antibiotic removal, but its performance depends on complex interactions among catalyst properties, irradiation parameters, and operating conditions. This study developed an interpretable machine learning-guided framework integrating entropy weight-based evaluation, predictive modeling, and SHAP analysis to investigate the apparent removal efficiency of tetracycline hydrochloride (TCH) over ZnO/Cu1.35O and ZnO/Cu1.35O/g-C3N4 composites under red, blue, and mixed laser irradiation. At an initial TCH concentration of 20 mg L−1, entropy weight analysis identified 0.02 g ZnO/Cu1.35O under red laser irradiation at 50 μmol m−2 s−1 as the highest-scoring scheme (0.73). At 30 mg L−1, ZCG-3 under mixed red/blue laser irradiation at 200 μmol m−2 s−1 achieved the highest score (0.90). Among five machine learning models, CCO-XGBoost exhibited the best average predictive performance under five-fold group-based cross-validation, with a mean R2 of 0.92 ± 0.06 and a mean RMSE of 8.27 ± 2.79%. SHAP analysis identified reaction time, laser intensity, and initial TCH concentration as the most influential predictive features, reflecting model-based associations rather than physicochemical causality. These findings provide a data-driven framework for treatment-scheme evaluation and UV-Vis-derived apparent TCH removal prediction within the investigated experimental domain",
  "abstract_shown": true,
  "affiliations": [
    "Intelligent Agriculture Research Institute, Jilin Agricultural University, Changchun 130000, China",
    "Jilin Provincial Science and Technology Innovation Center of Health Products and Medical Materials with Characteristic Resources, Changchun 130000, China",
    "School of Food Science and Engineering, Jilin Agricultural University, Changchun 130000, China",
    "Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130000, China",
    "School of Municipal and Environmental Engineering, Jilin University of Architecture, Changchun 130000, China"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 11,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Weiping Li"
    },
    {
      "affs": [
        0
      ],
      "name": "Leijinyu Zhou"
    },
    {
      "affs": [
        0
      ],
      "name": "Shuangshuang Yang"
    },
    {
      "affs": [
        0
      ],
      "name": "Jiahao Zhang"
    },
    {
      "affs": [
        2
      ],
      "name": "Jiage Wu"
    },
    {
      "affs": [
        0
      ],
      "name": "Wenbo Fang"
    },
    {
      "affs": [
        0
      ],
      "name": "Helong Yu",
      "orcid": "0000-0003-1800-3739"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Zhiqiang Cheng",
      "orcid": "0000-0003-1156-0540"
    },
    {
      "affs": [
        3
      ],
      "name": "Lijun Wang"
    },
    {
      "affs": [
        0
      ],
      "name": "Xuemei Liang"
    },
    {
      "affs": [
        4
      ],
      "name": "Yuxin Zhao"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6qk65kgew1yyk.bib",
  "cited_by": 0,
  "doi": "10.3390/catal16110937",
  "fulltext_url": null,
  "id": "6qk65kgew1yyk",
  "issns": [
    "2073-4344"
  ],
  "landing_url": "https://www.mdpi.com/2073-4344/16/11/937",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2105|Advanced Photocatalysis Techniques"
    ]
  ],
  "openalex": "W7223271882",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-22",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/6qk65kgew1yyk.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:43Z",
      "native_id": "10.3390/catal16110937",
      "source": "crossref",
      "url": "https://www.mdpi.com/2073-4344/16/11/937"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "issn:2073-4344",
      "subfield": "catalysis"
    }
  ],
  "title": "Interpretable Machine Learning-Guided Optimization of Laser-Driven Photocatalytic Degradation of Tetracycline Hydrochloride over ZnO/Cu1.35O/g-C3N4 Composites",
  "title_text": "Interpretable Machine Learning-Guided Optimization of Laser-Driven Photocatalytic Degradation of Tetracycline Hydrochloride over ZnO/Cu1.35O/g-C3N4 Composites",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:43Z",
  "url": "https://research.draconitic.com/paper/6qk65kgew1yyk",
  "venue": "Catalysts",
  "versions": []
}