{
  "abstract": "Predicting the performance of multicomponent corrosion inhibitors requires models that relate composition to electrochemical response and provide interpretable formulation guidance. This study combined Latin hypercube sampling and Bayesian-optimized stacking to investigate an imidazoline–sodium molybdate–Tween 80 inhibitor for mild steel in 3.5 wt.% NaCl. A dataset of 90 formulations linked component concentrations to total interfacial resistance, Rtotal, obtained from electrochemical impedance spectroscopy. The response comprised pore resistance and charge-transfer resistance. The stacking ensemble combined K-nearest neighbors, support vector regression and LightGBM with a ridge regression meta-learner. In ten-fold evaluation following dataset-level hyperparameter selection, stacking achieved R2 = 0.9036, a mean squared error of 969.46 (Ω·cm2)2 and a mean absolute percentage error of 2.22%. SHapley Additive exPlanations (SHAP) assigned normalized mean absolute attributions of 49.7%, 48.0% and 2.3% to Tween 80, imidazoline and sodium molybdate, respectively. These values identify influential predictors within the sampled domain rather than independent chemical contributions. Complementary polarization and surface observations supported adsorption-mediated protection with possible molybdate-associated interfacial reinforcement. By connecting formulation composition, electrochemical response and model attribution, this framework supports the prioritization of inhibitor mixtures under the investigated conditions.",
  "abstract_shown": true,
  "affiliations": [
    "Shandong Institute of Petrochemical and Chemical Technology, Dongying 257061, China",
    "Shandong Engineering Research Center for Green Manufacturing and Intelligent Control, Dongying 257061, China",
    "College of Chemical Engineering, China University of Petroleum, Qingdao 266580, China",
    "Jack Artificial Lift Machinery Co., Ltd., Dongying 257061, China",
    "School of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266580, China",
    "College of Mechanical and Electronic Engineering, China University of Petroleum, Qingdao 266580, China"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 9,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Changfeng Fan"
    },
    {
      "affs": [
        0,
        2
      ],
      "name": "Xue Wang"
    },
    {
      "affs": [
        3
      ],
      "name": "Xinfu Che"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Xianghua Zhan"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Xiaoli Yin"
    },
    {
      "affs": [
        4
      ],
      "name": "Debao Gao"
    },
    {
      "affs": [
        4
      ],
      "name": "Binchao Gao"
    },
    {
      "affs": [
        0
      ],
      "name": "Qihao Zhao"
    },
    {
      "affs": [
        0,
        1,
        5
      ],
      "name": "Yancong Liu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7r99hhn8x5pe6.bib",
  "cited_by": 0,
  "doi": "10.3390/molecules31213978",
  "fulltext_url": null,
  "id": "7r99hhn8x5pe6",
  "issns": [
    "1420-3049"
  ],
  "landing_url": "https://www.mdpi.com/1420-3049/31/21/3978",
  "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": null,
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-04",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/7r99hhn8x5pe6.ris",
  "sources": [
    {
      "first_seen": "2026-11-05T00:20:53Z",
      "native_id": "10.3390/molecules31213978",
      "source": "crossref",
      "url": "https://www.mdpi.com/1420-3049/31/21/3978"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "issn:1420-3049",
      "subfield": "general"
    }
  ],
  "title": "Interpretable Prediction of Interfacial Resistance for an Imidazoline–Sodium Molybdate–Tween 80 Corrosion Inhibitor Using Bayesian-Optimized Stacking",
  "title_text": "Interpretable Prediction of Interfacial Resistance for an Imidazoline–Sodium Molybdate–Tween 80 Corrosion Inhibitor Using Bayesian-Optimized Stacking",
  "type": "peer-reviewed",
  "updated_at": "2026-11-06T00:21:09Z",
  "url": "https://research.draconitic.com/paper/7r99hhn8x5pe6",
  "venue": "Molecules",
  "versions": []
}