SBIR/STTR Research Award
Physics-informed Machine Learning approach for a selective, sensitive, and rapid sensor for detecting unsafe levels of carcinogenic/toxic VOCs
Award amount
$275,554Program and phase
STTR · Phase IAward year
2023Award details
- Awarding agency
- Department of Health and Human Services
- Branch
- National Institutes of Health
- Recipient company
- Prometheus Technologies, LLC
- Proposal award date
- 2023-01-01
- Contract end date
- 2023-12-31
- UEI
- MW67XHEXPRA6
- Solicitation number
- PA21-262
- Topic code
- NIEHS
Research abstract
Project Summary
Each year, between 340,000 and 900,000 premature deaths can be linked to air pollution caused by releasing
Volatile Organic Compounds (VOCs), i.e., an estimated 1.8 billion tons of VOCs are emitted to the global
environment each year. Also, some VOCs cause serious adverse health effects even at the trace level
concentration, e.g., cancer, damage to the central nervous and immune system. For example, the EPA has
identified 188 toxic air pollutants known or suspected to cause cancer or other serious health effects, such as
reproductive effects, congenital disabilities, or adverse environmental effects. Existing commercial sensors for
detecting VOCs, such as photoionization detectors, are non-selective. So, they are unsuitable for detecting
unsafe levels of multiple carcinogenic/toxic VOCs simultaneously, e.g., Benzene and Toluene. Also, the current
selective detecting technologies such as gas chromatography-mass spectrometry (global chromatography
market size rt$15B by 2030) are bulky (~5 lbs.), expensive (~$25K - $100K), sluggish (~ 2 minutes), and requires
a skilled/trained operator. Therefore, Prometheus Technologies is developing a patented sensor platform with
features such as selectivity, low-cost, fast, small form factor monitoring solution that does not require
skilled/trained operators to detect unsafe levels of carcinogenic/toxic VOCs. A significant technological hurdle to
developing a selective VOC sensor is interference from a small subset of background confounders when a
feature-limited single wavelength desorption curve is used for quantification. The goals of this application are 1)
to perform a series of verified physics-based models to generate a sizeable optical sensor dataset at a low cost
that is essential considering the scarcity of data in this field, and 2) to develop a machine learning model based
on the dataset in step (1) for detecting unsafe levels of target compounds with background confounders. This
work is necessary to advance our patented selective and miniaturized VOC optical sensor.
Technology keywords
Not reported
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