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 identifier 86cb7cc7cf7b60a6876a0e49c603ab7f4b6590ddcecbe8d3a075a1229ec8cde4Awarding agency Department of Health and Human Services · National Institutes of Health

Award amount

$275,554

Program and phase

STTR · Phase I

Award year

2023

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Award 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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Agency intelligence

Department of Health and Human Services

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