Simulation of Surface Acoustic Wave Sensor for the Detection of Lung Cancer Biomarkers in the Exhaled Breath

Abstract

Diagnosis of cancer at an early stage helps to save human life. Among the most promising cancer diagnosis technologies, an electronic nose (e-nose) serves as a simple and cost-effective tool for the detection of lung cancer biomarkers in exhaled breath. The surface acoustic wave (SAW) is one of the viable e-nose technologies, which provides features like high sensitivity, small size, less expensive, and can detect a wide range of Volatile Organic Compounds (VOCs) present in the exhaled breath. The SAW propagation is confined to the surface and its amplitude reduces with respect to the depth of penetration. The SAW sensor can be produced easily on a piezoelectric substrate using interdigitated electrodes. Due to the interaction between the VOCs and the sensing layer on top of the piezoelectric material, the resonance frequency of SAW gets modulated. Thus, the shift in the resonance frequency of the SAW reflects the proportionate concentration of VOCs present in the human breath. In this paper, we propose the simulation and design techniques of the SAW sensor using the Finite Element Method (FEM). The proposed SAW sensor model comprises Aluminium Nitride (AlN) as a piezoelectric layer. Over which, the Poly-Iso-Butylene (PIB) layer is simulated and acts as a sensing material for cancer-related VOCs (biomarkers). The sensitivity analysis exhibits that the higher molar-mass VOCs like propyl-benzene, nonane, ethylbenzene, octane, and methyl-cyclopentane have better sensitivity compared to the other VOCs. This has been confirmed by the high resonance frequency shift due to the mass loading effect. Show more