(261a) Bacterial Aerosol Neutralization by Aerodynamic Shocks in a Novel Impactor: An Integrated Computational and Experimental Study
AIChE Annual Meeting
2009
2009 Annual Meeting
Computing and Systems Technology Division
Mathematical and Computational Biosystems Engineering
Tuesday, November 10, 2009 - 12:30pm to 12:50pm
Neutralization of bacterial aerosol releases is critical in countering bioterrorism. As a possible bacterial aerosol neutralization method that avoids the use of chemicals, we investigate the mechanical instabilities of the bacterial cell envelope in air as the bacteria pass through aerodynamic shocks. To carry out this fundamental investigation, an experimental impactor system is designed and built to simultaneously create a controlled and measured shock and to collect the bacteria after they pass through the shock. In the impactor system the aerosol flows through a converging nozzle, perpendicular to a collection surface that has an orifice through which the shocked bacteria enter the deceleration tube. Both experimental measurements of the pressure in the impactor system at multiple points and computational fluid dynamics simulations are used to quantitatively characterize the shocks created in the impactor. Specifically, the developed computational model describes the evolution of both the gas and the particle velocity and temperature in the impactor system to determine the forces exerted on the bacterial aerosol as they pass through the shock. The results indicate that the developed computational model predictions compare well with the experimental pressure measurements. Our models predict that the bacterial accelerations achieved in the impactor system are on the order of 10^9-10^10m/s^2 which is sufficient to neutralize vegetative E. coli. Experimental measurements indicate 0.25% survival of E. coli after they pass through the shock with accelerations of 5x10^9m/s^2 in the impactor compared to 3.5% survival under non-shock conditions with accelerations of 4x10^6m/s^2. These results confirm the computational model predictions.