SBIR-STTR Award

Rapid Solution to COVID-19 and Future Viral Pandemics
Award last edited on: 12/16/2021

Sponsored Program
SBIR
Awarding Agency
NSF
Total Award Amount
$255,973
Award Phase
1
Solicitation Topic Code
W
Principal Investigator
Timothy Childs

Company Information

TLC Millimeter Wave Products Inc

1415 West River Road N
Minneapolis, MN 55411
   (952) 356-9277
   N/A
   www.tlcprecision.com
Location: Single
Congr. District: 05
County: Hennepin

Phase I

Contract Number: 2051411
Start Date: 9/1/2021    Completed: 2/28/2022
Phase I year
2021
Phase I Amount
$255,973
The broader impact/commercial potential of this Small Business Research Innovation (SBIR) Phase I project is a demonstration of a millimeter wave Rapid Pathogen Detector System (or “RPDS”) to autonomously detect and instantly identify pathogens at tiny concentrations as even these samples may cause health threats to individuals or to a community. The “in the field” demonstration utilizes a database of captured and learned the characteristics (i.e., electronic signature) of the pathogen using a unique machine learning/artificial intelligence model. Once the RPDS is “trained” to recognize the pathogen, the RPDS may detect and identify pathogens in human and non-human samples in the field with high accuracy. The model target pathogen of this project is the SARS-CoV-2 virus. This target is to be followed by a selection of other viruses, proteins, biomatter, defective cells (cancer), and small inanimate objects as well. The RPDS system capabilities seek to increase the testing rate by an orders of magnitude compared to the current state of the art detection and diagnostic systems. Also, the instant results can be streamed to authorized individuals and officials in seconds using the local WiFi/ ethernet systems. This small Business Innovation Research (SBIR) Phase I project is to develop and demonstrate an autonomous and remotely operated millimeter wave (MMW) device that identifies pathogens in seconds in the field. The chemical-free, mobile mini table top systems is to help expand the number of pathogen test stations throughout the USA. This effort utilizes transmitted MMW signals reflected from nanosize pathogens in a manner that captures their unique characteristic via digital signal processing assisted artificial intelligence (AI) structures and stores a reliable unique model of the target pathogen. Utilizing machine learning (ML), the detector autonomously recognizes the pathogen identity within each of the million scans (reflected response packets) of the unidentified samples, each second.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Phase II

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Start Date: 00/00/00    Completed: 00/00/00
Phase II year
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