{
    "abstract": "The increasing demand for gas molecule detection emphasizes the need for portable sensor devices possessing selectivity, a low limit of detection (LOD), and a large dynamic range. Despite substantial progress in developing nanostructured sensor materials with heightened sensitivity, achieving sufficient selectivity remains a challenge. Here, we introduce a strategy to enhance the performance of chemiresistive gas sensors by combining an advanced sensor design with machine learning (ML). Our sensor architecture consists of a tungsten oxide (WO",
    "authors": [
        {
            "affiliation": "Nanotechnology Research Laboratory, Research School of Chemistry, College of Science, The Australian National University, Canberra, ACT 2601, Australia.",
            "firstname": "Alishba T",
            "initials": "AT",
            "lastname": "John"
        },
        {
            "affiliation": "School of Computing, College of Engineering, Computing and Cybernetics, The Australian National University, Canberra, ACT 2601, Australia.",
            "firstname": "Jing",
            "initials": "J",
            "lastname": "Qian"
        },
        {
            "affiliation": "Nanotechnology Research Laboratory, School of Biomedical Engineering, Faculty of Engineering, The University of Sydney, Camperdown, NSW 2006, Australia.",
            "firstname": "Qi",
            "initials": "Q",
            "lastname": "Wang"
        },
        {
            "affiliation": "Nanotechnology Research Laboratory, School of Biomedical Engineering, Faculty of Engineering, The University of Sydney, Camperdown, NSW 2006, Australia.",
            "firstname": "Fabian S",
            "initials": "FS",
            "lastname": "Garay-Rairan"
        },
        {
            "affiliation": "Nanotechnology Research Laboratory, Research School of Chemistry, College of Science, The Australian National University, Canberra, ACT 2601, Australia.",
            "firstname": "Y M Nuwan D Y",
            "initials": "YMNDY",
            "lastname": "Bandara"
        },
        {
            "affiliation": "School of Engineering and Technology, The University of New South Wales, Canberra, ACT 2612, Australia.\nNanotechnology Research Laboratory, School of Biomedical Engineering, Faculty of Engineering, The University of Sydney, Camperdown, NSW 2006, Australia.",
            "firstname": "Artem",
            "initials": "A",
            "lastname": "Lensky"
        },
        {
            "affiliation": "Nanotechnology Research Laboratory, Research School of Chemistry, College of Science, The Australian National University, Canberra, ACT 2601, Australia.\nCommonwealth Scientific and Industrial Research Organization (CSIRO), Mineral Resources, Private Bag 10, Clayton South, VIC 3169, Australia.",
            "firstname": "Krishnan",
            "initials": "K",
            "lastname": "Murugappan"
        },
        {
            "affiliation": "School of Computing, College of Engineering, Computing and Cybernetics, The Australian National University, Canberra, ACT 2601, Australia.\nSchool of Medicine and Psychology, College of Health and Medicine, The Australian National University, Canberra, ACT 2601, Australia.\nDepartment of Computing, Faculty of Technology, University of Turku, 20014 Turku, Finland.",
            "firstname": "Hanna",
            "initials": "H",
            "lastname": "Suominen"
        },
        {
            "affiliation": "Nanotechnology Research Laboratory, Research School of Chemistry, College of Science, The Australian National University, Canberra, ACT 2601, Australia.\nNanotechnology Research Laboratory, School of Biomedical Engineering, Faculty of Engineering, The University of Sydney, Camperdown, NSW 2006, Australia.",
            "firstname": "Antonio",
            "initials": "A",
            "lastname": "Tricoli"
        }
    ],
    "conclusions": null,
    "copyrights": null,
    "doi": "10.1021/acsami.5c02081",
    "journal": "ACS applied materials & interfaces",
    "keywords": [
        "ZIF-8",
        "chemiresistive",
        "computer-assisted workflows",
        "concentration prediction",
        "evaluation studies",
        "gas discrimination",
        "machine learning",
        "nanoparticle networks"
    ],
    "methods": null,
    "publication_date": "2025-04-24",
    "pubmed_id": "40268286",
    "results": null,
    "title": "Metal Oxide-Metal Organic Framework Layers for Discrimination of Multiple Gases Employing Machine Learning Algorithms.",
    "xml": "<Element 'PubmedArticle' at 0x788505e3df80>"
}