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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.

Herein, we present an innovative approach to generate a novel type of 3D optical nanostructures based on nanoporous anodic alumina (NAA), the so-called inverted nanoporous anodic alumina funnels (INAAFs). This electrochemical approach takes advantage of the differential dissolution rate of NAA with the annealing temperature, which enables the in-depth engineering of nanopores. The structural engineering of these 3D nanostructures is monitored in situ by reflectometric interference spectroscopy through changes in the effective optical thickness of NAA, making it possible to precisely control the formation of INAAFs in real-time. The resulting INAAFs are optical nanostructures featuring a stratified structure of well-defined cylindrical nanopores with increasing pore diameter from top to bottom. As a result of their geometric characteristics and optical properties, INAAFs are envisaged for replicating novel nanostructures and developing optical and photonic devices.