LYNNTECH INC. — Department of Defense SBIR Phase I: CBD222-004

Description

The detection and characterization of airborne aerosol particles is paramount to rapidly sense chemical and biological threats. This is especially true for urban and/or battlespace settings where the aerosol composition can include inorganic, organic, and biological particles with complex morphologies across a range of sizes that spans orders of magnitude (1-100 μm). Because aerosols contain a large majority of innocuous particles, the detection of potential threat materials is limited by their small concentration within a complex ambient matrix containing materials of non-interest as well as interfering compounds. Moreover, aerosol properties can evolve in time through chemical aging processes and mechanical forces. While sensor technology has improved over the last 20 years, threat detection still remains a challenge in operational environments at mission-speed due to the complex and dynamic nature of the surrounding environmental media. To address the critical need, Lynntech is collaborating with Texas A&M University to develop a portable system for rapid onsite detection and morphological classification of aerosol particles based on optical light scattering, holographic imaging, and machine learning algorithms. The contact-free design eliminates the need for consumables, lowering lifetime operating costs and freeing the device from the supply chain. The optical system’s unique arrangement of light source and detectors provide additional data to the machine learning algorithms for improved identification and classification. The improved sensing allows the system to rapidly distinguish between biological and nonbiological as well as threat and nonthreat aerosol. The system’s low SWaP allows for the device to be installed on unmanned vehicles, permitting the autonomous inspection of areas of interest for prolonged periods.