{
  "abstract": "Moisture swing adsorption (MSA) offers an energy-efficient route for direct air capture of CO2 by exploiting humidity-driven sorbent regeneration, yet its performance remains difficult to predict due to the coupled effects of sorbent chemistry, counterion properties, and operating conditions. Here, we present a data-efficient experimental and modeling strategy that systematically maps these interactions across a high-dimensional design space. Using active learning to target regions of maximal uncertainty, we expand experimental coverage from 19 to 77 conditions and, together with curated literature data, assemble a unified dataset of 290 data points (201 distinct experimental conditions) spanning 20 commercial ion-exchange resins. Interpretable machine-learning models trained on this dataset predict CO2 capture capacity (R2 = 0.702, RMSE = 0.164) and identify functional groups, anion basicity, humidity, and CO2 concentration as dominant performance determinants. Guided by these insights, we experimentally validate model-selected resin-anion combinations for two practical scenarios─maximum CO2 capture capacity and moisture-swing efficiency. This work converts fragmented experimental observations into a predictive structure-property framework and demonstrates a generalizable approach for understanding and optimizing complex adsorption systems under data and experimental constraints.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Civil and Environmental Engineering, Case Western Reserve University, Cleveland, Ohio44106, United States.",
    "Department of Earth and Environmental Engineering, Columbia University, New York, New York10027, United States."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Peiling Yu"
    },
    {
      "affs": [
        0
      ],
      "name": "Kai Zhang"
    },
    {
      "affs": [
        1
      ],
      "name": "Xiaoyang Shi",
      "orcid": "0000-0003-1097-9745"
    },
    {
      "affs": [
        0
      ],
      "name": "Zihan Zheng"
    },
    {
      "affs": [
        0
      ],
      "name": "Huichun Zhang",
      "orcid": "0000-0002-5683-5117"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cpme3vpwjw542.bib",
  "cited_by": 0,
  "doi": "10.1021/acs.est.6c07244",
  "fulltext_url": "https://europepmc.org/articles/PMC13674078",
  "id": "cpme3vpwjw542",
  "issns": [
    "0013-936X",
    "1520-5851"
  ],
  "landing_url": "https://europepmc.org/article/MED/42915970",
  "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",
      "2210|Carbon Dioxide Capture Technologies"
    ]
  ],
  "openalex": "W7214466198",
  "pdf_url": "https://europepmc.org/articles/PMC13674078?pdf=render",
  "pmcid": "PMC13674078",
  "pmid": "42915970",
  "published": "2026-10-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/cpme3vpwjw542.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:36Z",
      "native_id": "MED:42915970",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42915970"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "journal-openalex:F23",
      "subfield": "environmental-science"
    }
  ],
  "title": "Active Learning-Guided Optimization of Moisture Swing Adsorption for Direct Air Capture.",
  "title_text": "Active Learning-Guided Optimization of Moisture Swing Adsorption for Direct Air Capture.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:20:36Z",
  "url": "https://research.draconitic.com/paper/cpme3vpwjw542",
  "venue": "Environmental science & technology",
  "versions": []
}