SBIR/STTR Research Award

SBIR Phase I: Low cost spectrometer for methane sensing

Award identifier 1c16a7ca91771617bb18cb7f2ad0cc42186e0d3fcd5b006765817e7093f052a0Awarding agency National Science Foundation

Award amount

$221,993

Program and phase

SBIR · Phase I

Award year

2020

Explore This Research Award in CaptureIQ

Research the technology, evaluate the recipient and agency, identify related opportunities, and explore potential follow-on work with CaptureIQ.

Award details

Awarding agency
National Science Foundation
Branch
Not reported
Recipient company
Troposphere Monitoring Inc.
Proposal award date
2020-05-15
Contract end date
2021-01-31
UEI
QTLGLMYGYNG5
Solicitation number
Not reported
Topic code
ET

Research abstract

The broader impact of this Small Business Innovation Research (SBIR) Phase I project is to protect American workers and the environment from leaks of methane, the primary chemical in natural gas and landfill gas. At high concentrations, methane gas is explosive and can be harmful or fatal if inhaled. Methane leak detection is currently conducted by skilled technicians using handheld sensors, an expensive and labor-intensive process that can be unsafe. This project aims to build low-cost monitoring stations that can automate the methane leak detection process and allow remote real-time monitoring. This project will develop algorithms for low-cost monitoring hardware used in outdoor settings typical of active oil and gas fields and landfills. The research will enhance the precision and accuracy of outdoor methane leak detection while making it affordable for industry and thereby reducing methane leaks. The proposed SBIR Phase I project will advance algorithms to improve the performance of a low-cost and field-deployable spectrophotometer for methane emissions monitoring. This project will develop a field spectrometer that can operate autonomously in outdoor environments without calibration. Specifically, this project focuses on employing instrument-specific methods for accuracy and precision enhancement and for rejecting confounding effects. Classical multivariate modeling methods are improved upon with instrument-specific wavelet methods to tolerate changes in the instrument condition induced by environmental conditions. Additionally, an optimization of the speciation method is proposed to reduce confounding effects due to the presence of unknown gases and to generate a speciation quality indicator. 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.

Technology keywords

Not reported

Agency intelligence

National Science Foundation

Back to SBIR/STTR Research Awards

Don't take our word for it. Ask AI about RFPCapture.

See what leading AI assistants have to say about RFPCapture and CaptureIQ.

Gemini opens without a prefilled question. The question is copied to your clipboard when the button works. You can also copy it here:

What is RFPCapture.com and its CaptureIQ platform from Smart Logic AI? How does it help federal contractors discover opportunities, evaluate bid/no-bid decisions, research government contracts, and prepare proposals? How does it compare with other federal capture platforms? Use independent sources where available.