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(http://www.niehs.nih.gov//portfolio/index.cfm?do=portfolio.grantdetail&&grant_number=R41ES034936&format=word)
| Principal Investigator: Attariani, Hamed | |
|---|---|
| Institute Receiving Award | Prometheus Technologies, Llc |
| Location | Cincinnati, OH |
| Grant Number | R41ES034936 |
| Funding Organization | National Institute of Environmental Health Sciences |
| Award Funding Period | 01 Jan 2023 to 31 Dec 2024 |
| DESCRIPTION (provided by applicant): | 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 >$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. |
| Science Code(s)/Area of Science(s) |
Primary: 75 - Computational Biology/Computational Methods for Exposure Assessment Secondary: 03 - Carcinogenesis/Cell Transformation |
| Publications | No publications associated with this grant |
| Program Officer | Lingamanaidu Ravichandran |