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Portable Holographic Imager for Aerosols (PHIA)

Award Information
Agency: Department of Defense
Branch: Office for Chemical and Biological Defense
Contract: W911NF-23-P-0002
Agency Tracking Number: C222-004-0068
Amount: $182,955.51
Phase: Phase I
Program: SBIR
Solicitation Topic Code: CBD222-004
Solicitation Number: 22.2
Timeline
Solicitation Year: 2022
Award Year: 2023
Award Start Date (Proposal Award Date): 2023-02-06
Award End Date (Contract End Date): 2023-08-05
Small Business Information
907 Columbia Rd
Fort Collins, CO 80525-1838
United States
DUNS: N/A
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Matt Freer
 (608) 220-0844
 mfreer@gmail.com
Business Contact
 Matt Freer
Phone: (608) 220-0844
Email: mfreer@gmail.com
Research Institution
N/A
Abstract

The rapid identification of aerosolized particles that pose an immediate chemical or biological threat remains a significant challenge. Existing contact-free methods to measure airborne particles have employed various light-scattering techniques to obtain particle information, however, these techniques are severely limited in their accuracy due to their reliance on assumptions about particle shape, source, and refractive index. Perhaps the most promising technique to overcome these issues is digital in-line holography (DIH) which has many advantages over traditional light-scattering techniques. In this proposal, we build on previous DIH work by the team by incorporating lasers of multiple wavelengths to obtain color holographic information for each particle, providing an additional measurement parameter based on the particle’s chromatic absorption properties. Machine learning algorithms can then be applied to the color holograms to determine particle type. In Phase I, we will perform Discrete Dipole Approximation (DDA) modeling to evaluate the system’s theoretical response to various biological and non-biological particle types and determine the system's optimal configuration. Simulated holograms from the DDA model runs will be used to train, test, and evaluate machine learning algorithms. In Phase II, a prototype instrument will be built using the findings of Phase I and will undergo lab and field testing to further establish the measurement technique.

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

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