SBIR-STTR Award

Tomographic Nanoscopy for Pathogen Identification
Award last edited on: 9/27/2018

Sponsored Program
SBIR
Awarding Agency
DOD : CBD
Total Award Amount
$656,318
Award Phase
2
Solicitation Topic Code
CBD171-003
Principal Investigator
James E Murguia

Company Information

Solid State Scientific Corporation

27-2 Wright Road
Hollis, NH 03049
   (603) 598-1194
   N/A
   www.solidstatescientific.com
Location: Multiple
Congr. District: 02
County: Hillsborough

Phase I

Contract Number: W911SR-18-C-0024
Start Date: 4/19/2018    Completed: 10/18/2018
Phase I year
2018
Phase I Amount
$149,978
This effort will augment the capabilities of an existing all reflecting, confocal FTIR microscope to allow for digital holographic imaging as wellspectral content determination at video frame rates. The use of a dual beam frequency comb, with a beam path coincident with the pathrequired for the digital holographic imaging, will allow spectral determination of any organism being holographically imaged in themicroscopes field of view over the spectral range 7m to 20m at greater than video frame rates. Although single point spectra of the entireimage field is the product of this work, the availability of high speed IR focal plane arrays and the inherent brightness of the dual comb FTIRtechnique suggest pixel level spectral content of the field of view is a possibility. Significantly, this work will not alter the ability of thecommercial microscope/FTIR system to acquire FTIR spectrographic data using traditional methods.

Phase II

Contract Number: W911-SR-19-C-0035
Start Date: 9/25/2019    Completed: 9/22/2021
Phase II year
2019
Phase II Amount
$506,340
This Phase II effort prototypes a minimal volume microscope designed to allow for a 2-D digital holographic imaging concurrent with field of view spectral content at video frame rates. The use of a laser driven IR dual beam frequency comb, coincident with the optical beam path required for a digital holographic image, will allow rapid acquisition of both the spatial and the spectral content associated with cellular activity. This spatial/spectral content will then be used to develop a deep learning driven library of spectral signatures for the rapid, label free identification and characterization of pathogens for use in remote nanoscopic applications.