Portable, Integrated Microscale Sensors (PIMS) for Explosives Detection
This project seeks to develop portable, integrated, microscale sensors (PIMS) that are suitable for vapor-phase explosives detection. The proposed devices are based upon bifurcation-based mass sensing principles, wherein target vapors chemomechanically interact with a functional layer deposited upon an oscillating electromechanical resonator. This interaction renders a change in the sensor’s effective mass, eliciting a shift in frequency, and, given that the system is driven into a nonlinear response regime, a marked change in vibration amplitude. Because this approach utilizes a nonlinear mechanism and a thresholding technique for sensing, the associated control electronics can be greatly simplified in comparison to more conventional sensor designs, which aids in the development of portable sensors with reduced form factors. Additionally, the sensitivity of the system can be widely tuned. In prior work by the investigators, which focused on alcohol sensing, bifurcation-based sensors were shown to yield superior performance metrics (i.e. false positive/negative rates, sensitivity, and power consumption metrics) in laboratory environments and compare favorably to their more conventional counterparts.
The present effort’s technical focus is on:
- Developing a new class of cost effective and tunable bifurcation-based mass sensors which are suitable for vapor-phase explosives detection;
- Developing a new inkjet-based functionalization system which is capable of rapidly and precisely depositing functional surface layers on the sensors developed herein;
- Developing new control and signal processing electronics, designed to enable portable functionality, while maintaining performance;
- Validating sensor performance with mock and real energetic materials within both laboratory and operational (wherein the impinging fluid flow becomes important) environments;
- Characterizing pertinent sensor metrics (e.g. false positive/negative rates, sensitivities, power consumption, etc.) and benchmarking these metrics against alternate sensing platforms;
- Overcoming the basic research challenges associated with integrating all of the sensing system’s constituent pieces in a single, portable platform; and
- Modeling the complete sensing system with an eye towards predictive design, performance optimization, and, ultimately, technology transition.