Detection of volatile organic compounds (VOCs) is of great importance in fields like breath diagnostics, antibiotic susceptibility tests, and early plant disease diagnostics. However, the current sensors face two main limitations: 1) Low sensitivity and 2) poor selectivity. The limit of detection for VOC sensors needs to be in ppb or at a low ppm level to be used in the applications stated above. The VOC sensor should also be able to distinguish between different VOCs even of the same class (alcohols, ketones, and alkanes) to be useful in the above applications. To achieve this goal, our lab is developing a microfluidic electrochemical gas sensor. We believe that combining microfluidic architecture with an electrochemical detection technique can significantly enhance the sensitivity of the sensor. The microfluidic architecture also allows the use of a very low volume of sensing material/electrolyte thereby reducing the cost of the sensor and allowing the sampling of small amounts of gases. To enhance the selectivity of the sensor in detecting multiple VOCs, multiple electrochemical detection techniques like EIS, CV, DPV, IV, and amperometry can be used in tandem to gain as much information and use ML and AI techniques to differentiate between different VOCs confidently. Currently, for this project, our lab is developing and optimizing sensor architectures using carbon dioxide as the model gas and ionic liquid as the sensing material/electrolyte.
