Development of a Custom Aerosol Test Chamber and Stand-Off Chemical Aerosol System for Detection and Classification
STEVEN PULLINS, Meredith Melendez, David Alburty, Darren Radke, Adam Luxon, Jonathan Mueller, Garrett Wendell, Miles Egan, Seth Henshaw, Deborah Hunka, Leidos
Abstract Number: 679
Working Group: Instrumentation and Methods
Abstract
Many toxic chemical threats are delivered as aerosols. Rapidly identifying these types of particles is imperative to human health and protection as well as national security. Aerosol detection and classification, particularly in complex environments, presents a challenging technical problem that cannot be understated and requires developing enhanced detection methods.
The iCATS standoff detection system is being developed under the IARPA PICARD program. The PICARD program intends to develop fieldable sensing platforms for the rapid chemical classification of aerosol particles in plumes. The development of the iCATS focuses on the following challenges: distance from the plume, low concentrations, arbitrarily shaped particles, and challenging environments.
A custom aerosol test chamber has been constructed to facilitate the iCATS sensor. The chamber has multiple aerosol particle generation technologies (both liquid and solid), particle size distribution capabilities from 1 nm–10 um, aerosol recirculation capability, and a modular interrogation path length (1–10 m).
The iCATS sensor performs active longwave infrared (LWIR) spectral measurements of chemical aerosol cloud optical extinction using a scattering surface at up to 10 meters standoff. The sensor design extends laser spectroscopy used for gases to chemical aerosols while removing the need for a retroreflector. This approach takes advantage of the method’s high sensitivity and specificity. We use quantum cascade lasers (QCLs) covering the LWIR (800–1250 cm-1) spectral range. The laser is operated in pulsed mode to achieve the power level needed to measure a return signal from a diffuse surface, and detection is AC coupled to suppress environmental thermal infrared signals.
Collecting the reflected signal from a diffuse surface allows for the use of “surfaces of opportunity” enabling collection in real-world scenarios. Advanced AI/ML algorithms are trained to extract the aerosol signatures from the raw data that may include added spectral features from the reflected surface.