OPTOWARES INC — Department of Transportation SBIR Phase II: 21-FH1
OPTOWARES INC — SBIR Phase II award from Department of Transportation.
Phase II SBIR prototype / development signal
- Phase II is where Department of Transportation funds deeper R&D after feasibility. Incumbents with Phase II history are serious competitors on adjacent topics.
- Use this award as past-performance context and to map customer organizations for STRATFI/TACFI-style transition planning.
- Obligated amount $999,915 is consistent with substantial Phase II-scale effort; compare to related awards from the same agency.
- Topic code 21-FH1 links this award to a solicitation family — search the same topic stem for incumbents and recompete timing.
- Amount
- $999,915
- Agency
- Department of Transportation
- Program / Phase
- SBIR · Phase II
- Topic
- 21-FH1
- Solicitation
- 6913G621QSBIR1
- NAICS
- —
- Place of performance
- MA
- Period
- 2022-05-26 → 2024-05-27
Description
In-place resurfacing of asphalt-based wear layers is an increasingly common method of roadway rehabilitation. With the addition of waste microplastics to bitumen binders as performance improvers, the grinding of asphalt during in-place resurfacing potentially brings microplastic particulate pollution to every community in the US. In addition, the wear of roadway surfaces is a known major source of particulate runoff, with theinclusion of microplastics potentially adding a new dimension of ecotoxicity to the pavement life cycle. We propose an in-situ, compact, continuous monitor of airborne fugitive and first-flush stormwater-suspended particles, using proven, molecularly-specific vibrational analysis techniques, to quantitate and identify road wear particles by composition, plastic content, and particle size.It may be implemented to monitor water or air without modification, such that aerosol monitoring and aqueous runoff monitoring can be performed with a single instrument. The proposed technology retains a continuous record of the sample in the form of particles bound on a roll filter archive, and is supported by both prior results on the topic, and Optowares’ experience in bringing advanced machine-learning enabled spectroscopy instruments to difficult-to-solve problems.