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A Novel, Low Power, High Throughput, High Efficiency, Scalable Electrostatic Bioaerosol Sampler

Award Information
Agency: Department of Homeland Security
Branch: N/A
Contract: NBCHC040110
Agency Tracking Number: 04110360
Amount: $99,948.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: H-SB04.1-003
Solicitation Number: N/A
Timeline
Solicitation Year: 2004
Award Year: 2004
Award Start Date (Proposal Award Date): 2004-04-02
Award End Date (Contract End Date): 2004-10-19
Small Business Information
215 Wynn Dr.
Huntsville, AL 35805
United States
DUNS: N/A
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Kapil Pant
 Senior Engineer
 (256) 726-4800
 jls@cfdrc.com
Business Contact
 Mark Andrews
Title: Contracts Manager
Phone: (256) 726-4884
Email: mva@cfdrc.com
Research Institution
N/A
Abstract

Current bioaerosol monitoring strategies rely on inertia-based sampling and suffer from high power consumption and low efficiency limitations. In contrast, our overall objective is to design and demonstrate a novel electrostatic sampler that provides dramatic improvements including low power requirement (<100W), high throughput (500-1000L/min), high collection efficiency (>90% for 1-10mm), high viability (>80%), scalability for varying threat scenarios, low noise, and concentration of particles in liquid buffer. In an additional unique feature, our device will be able to discriminate desired size range, e.g., respirable fraction, from atmospheric background. The proposed technology will leverage on ongoing efforts at CFDRC to develop a personal-sized biosensor-sampler. Our main challenge is to increase throughput to 1000L/min while maintaining high efficiencies and minimizing losses. We will extend our existing technology by using a judicious combination of multiphysics analysis and experimentation. In Phase I, we will design, fabricate and test an electrostatic collector with innovative electrode design, along with charge modulation to maximize efficiency. Phase II will involve scale-up, system integration, and testing with class-based simulants and agents.

* Information listed above is at the time of submission. *

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