Lung cancer is the leading cause of death worldwide and has a significant impact on public health across society. Among all types of cancer, lung cancer is typically silent and it is commonly diagnosed at a later stage where treatment is rarely achievable. There is an urgent need for the development of the early diagnosis of lung cancer for an improved survival rate. Preliminary research shows that lung cancer is accompanied by increased oxidative stress which generates volatile organic compounds (VOCs). Hence, breath analysis offers the most promising solution for the early diagnosis of lung cancer as it is noninvasive and radiation free. Potential VOCs biomarkers in exhaled breath associated with oxidative stress and lipid peroxidation have been discussed to provide a quick approach to the diagnosis of lung cancer. Although gas chromatography-mass spectroscopy (GC-MS) able to analyze the VOCs biomarker, it is bulky, high cost, required expertise to handle and consumes a lot of time. Hence, the sensor-based technique provides the solution to overcome the limitation. Recently, acoustic wave sensors such as quartz crystal microbalance (QCM) and surface acoustic wave sensors (SAW) have been used to identify the presence of VOCs in various applications. This is due to its high selectivity, good reproducibility, and fast response sensing materials. The selection of vapour sensing materials plays a crucial role in developing a highly sensitive and selective and fast response acoustic wave sensors. For this purpose, various types of sensing layers from metal oxides, polymers, biopolymers and composites have been studied. We present a critical review of advanced vapour sensing materials that are primarily used in acoustic wave sensors in identifying the presence of various VOCs. Criteria to evaluate the performance of the acoustic wave sensors such as resonance frequency and sensitivity are also discussed.

Improvement in sensing layer properties of quartz crystal microbalance (QCM) sensors are crucial in developing gas sensors with high sensitivity and selectivity. In this work, we study the use of chitosan thin film as the sensing layer on a QCM sensor to identify the presence of volatile organic compounds specifically isopropyl alcohol (IPA). The effect of chitosan dissolved in different acetic acid concentrations towards QCM overlay with chitosan sensing performance were studied. Characterization work on chitosan thin film at different acetic acid concentrations (1.0, 1.5, 2.0, 2.5% (v/v)) were performed by using FTIR and FESEM. Higher acid concentration led to a higher degree of protonation which results in a more progressive solubilization of chitosan and promotes smoother film. For chitosan layer dissolved in 2% acetic acid , the highest resonance frequency shift (99.3 Hz) was observed during the adsorption of the analyte gas molecules on QCM sensors. This can be explained by the increase in chitosan solubility and protonation. This indicates that difference acid concentration in chitosan dissolution affects the sensing performance during the presence of the analyte gas.

The development of acoustic wave sensors was driven by the presence of modern technology. Quartzcrystal microbalance (QCM) has excellent sensing capabilitiesand has wide range ofapplications. Selection of sensing layer is crucial to ensure the performance of the QCM sensor for volatile organic compound (VOC) detection. Hence, the objective of this paper is to compare the performance of chitosan coated QCM sensor for different analyte gas: isopropyl alcohol (IPA). Finite element simulation was implemented usingCOMSOL Multiphysics to study the resonance frequency shift before and after sensing. Simulation results shows IPA detection shows a higher resonance frequency shift of 62.5 Hzcompared to acetone due to higher molar mass. Experimental work is conducted to validate the simulation results where IPA analyte gas yields in 84.8 Hz which is higher than acetone analyte gas at 41.8 Hz. The functional groups for both sensing layer and analyte gas also affects the gas detection performance. IPA analyte gas possessed hydroxyl groups that favors to hydrogen bond formation with chitosan sensing layer. Thus, the QCM sensor with chitosan as the sensing layer has the potential for VOC sensing of different molar mass and functional groups.

No abstract

No abstract