Modeling, Simulation and Fabrication of Highly Sensitive Acetone Gas Detection Sensor using MEMS Technology

Major public health concerns include obesity, diabetes, and related metabolic disorders. These conditions impact physical health, cognitive function, and psychological well-being. Acetone in human breath serves as a key biomarker. It offers a non-invasive, painless, and cost-effective means of monitoring fat metabolism and early signs of diabetes. This study introduces a microelectromechanical systems (MEMS)-based gas sensor. It is developed using the standard Polysilicon Multi-User MEMS Process (PolyMUMPs) process, for sensitive and selective breath acetone detection. The sensor includes a suspended microstructure with a gold micro-heater and temperature sensor, actuated electrothermally to produce displacement. A nanostructured TiO2 coating enhances acetone adsorption, increasing the sensor’s mass and altering its resonance. These changes are converted into electrical signals using a capacitive readout circuit (MS3110). Modeling and simulations were used to optimize performance, followed by experimental validation. The sensor detected acetone concentrations from 0.01 to 4 ppm, with a minimum detection limit of 44 ppb. Output voltage ranged from 0.176 to 0.295 V, showing a sensitivity of 0.029 V/ppm. It responded in about 90 seconds and recovered in 15 seconds. The sensor also showed strong selectivity against other VOCs, making it suitable for non-invasive breath analysis. Overall, the device offers a promising solution for portable, low-power acetone detection in applications like obesity screening, metabolic monitoring, and personalized healthcare.