Graphene and metal-organic framework hybrids for high-performance sensors for lung cancer biomarker detection supported by machine learning augmentation

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dc.contributor.authorTran, A.T.T.
dc.contributor.authorHassan, K.
dc.contributor.authorTung, T.T.
dc.contributor.authorTripathy, A.
dc.contributor.authorMondal, A.
dc.contributor.authorLosic, D.
dc.date.issued2024
dc.descriptionFirst published 08 Apr 2024
dc.description.abstractConventional diagnostic methods for lung cancer, based on breath analysis using gas chromatography and mass spectrometry, have limitations for fast screening due to their limited availability, operational complexity, and high cost. As potential replacement, among several low-cost and portable methods, chemoresistive sensors for the detection of volatile organic compounds (VOCs) that represent biomarkers of lung cancer were explored as promising solutions, which unfortunately still face challenges. To address the key problems of these sensors, such as low sensitivity, high response time, and poor selectivity, this study presents the design of new chemoresistive sensors based on hybridised porous zeolitic imidazolate (ZIF-8) based metal–organic frameworks (MOFs) and laser-scribed graphene (LSG) structures, inspired by the architecture of the human lung. The sensing performance of the fabricated ZIF-8@LSG hybrid sensors was characterised using four dominant VOC biomarkers, including acetone, ethanol, methanol, and formaldehyde, which are identified as metabolomic signatures in lung cancer patients’ exhaled breath. The results using simulated breath samples showed that the sensors exhibited excellent performance for a set of these biomarkers, including fast response (2–3 seconds), a wide detection range (0.8 ppm to 50 ppm), a low detection limit (0.8 ppm), and high selectivity, all obtained at room temperature. Intelligent machine learning (ML) recognition using the multilayer perceptron (MLP)-based classification algorithm was further employed to enhance the capability of these sensors, achieving an exceptional accuracy (approximately 96.5%) for the four targeted VOCs over the tested range (0.8–10 ppm). The developed hybridised nanomaterials, combined with the ML methodology, showcase robust identification of lung cancer biomarkers in simulated breath samples containing multiple biomarkers and a promising solution for their further improvements toward practical applications.
dc.description.statementofresponsibilityAnh Tuan Trong Tran, Kamrul Hassan, Tran Thanh Tung, Ashis Tripathy, Ashok Mondal and Dusan Losic
dc.identifier.citationNanoscale, 2024; 16(18):9084-9095
dc.identifier.doi10.1039/d4nr00174e
dc.identifier.issn2040-3364
dc.identifier.issn2040-3372
dc.identifier.orcidHassan, K. [0000-0002-0546-9719]
dc.identifier.orcidTung, T.T. [0000-0002-1535-5109]
dc.identifier.orcidLosic, D. [0000-0002-1930-072X]
dc.identifier.urihttps://hdl.handle.net/2440/140687
dc.language.isoen
dc.publisherRoyal Society of Chemistry
dc.relation.granthttp://purl.org/au-research/grants/arc/IH150100003
dc.relation.granthttp://purl.org/au-research/grants/arc/IH210100025
dc.rightsThis journal is © The Royal Society of Chemistry 2024
dc.source.urihttps://doi.org/10.1039/d4nr00174e
dc.subjectHumans
dc.subjectLung Neoplasms
dc.subjectZeolites
dc.subjectGraphite
dc.subjectImidazoles
dc.subjectBreath Tests
dc.subjectBiosensing Techniques
dc.subjectVolatile Organic Compounds
dc.subjectMachine Learning
dc.subjectBiomarkers, Tumor
dc.subjectMetal-Organic Frameworks
dc.titleGraphene and metal-organic framework hybrids for high-performance sensors for lung cancer biomarker detection supported by machine learning augmentation
dc.typeJournal article
pubs.publication-statusPublished

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