Graphene and metal-organic framework hybrids for high-performance sensors for lung cancer biomarker detection supported by machine learning augmentation
Date
2024
Authors
Tran, A.T.T.
Hassan, K.
Tung, T.T.
Tripathy, A.
Mondal, A.
Losic, D.
Editors
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Journal article
Citation
Nanoscale, 2024; 16(18):9084-9095
Statement of Responsibility
Anh Tuan Trong Tran, Kamrul Hassan, Tran Thanh Tung, Ashis Tripathy, Ashok Mondal and Dusan Losic
Conference Name
Abstract
Conventional 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.
School/Discipline
Dissertation Note
Provenance
Description
First published 08 Apr 2024
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This journal is © The Royal Society of Chemistry 2024