SPECTREE INC. — Department of Health and Human Services STTR Phase I: 143

SPECTREE INC. — STTR Phase I award from Department of Health and Human Services.

Amount
$259,573
Agency
Department of Health and Human Services · National Institutes of Health
Program / Phase
STTR · Phase I
Topic
143
Solicitation
PA21-262
NAICS
Place of performance
WA
Period
2022-09-13 → 2023-08-31

Description

Project SummaryWe propose developing and validating a novel platform technology that combines the collection and chemical analysis of ultrafine particles using an in-situ multispectral technique. The sample, collected directly onto the analysis substrate, is analyzed via excitation-emission matrix (EEM) spectroscopy. This approach will be validated against laboratory combustion-generated aerosols, such as diesel exhaust, wood smoke, tobacco smoke, and against a mixture of environmental pollutants. Within the respiratory tract, particle size determines the region of deposition and tissue uptake; the chemistry of the particle also affects solubility and determines the potential for biochemical reaction with tissues and cells. There is a growing awareness that exposure scenarios are very complex, consisting of time-varying concentrations and chemical composition over a broad range of particle sizes. Long-term exposure to air pollution has also been linked to increased mortality rates for infectious diseases, including COVID-19. The proposed research addresses the need for improved personal exposure assessment and characterization of ultrafine particles in the environment. Low-cost, miniaturized exposure monitoring devices can shed insight into the relationships between exposure to pollutants and health impact. Source apportioned measurements of PM concentration with high temporal and spatial resolution can facilitate the implementation of optimal air pollution mitigation strategies. The anticipated outcome of this project is the development of a miniaturized spectroscopic sensor that provides an analysis of the chemical composition of combustion-generated ultrafine particles, which both reflects the particle sources and determines their toxic potential. The machine-learning algorithms will enable the deconvolution of the complex spectra and identification of the PM source from the EEM analysis. The broader applications of the technology are environmental and regulatory monitoring, personal exposure assessment for the consumer market, and epidemiological studies.Project NarrativeExposure to complex environmental agents, such as ultrafine particulate matter derived from combustion sources and engineered nanomaterials, is not well understood. The toxic potential of inhaled particles is dependent on their size and chemical composition, and current solutions for personal exposure monitoring are expensive. This proposed effort addresses the need for a better individual exposure assessment, specifically, quantification of the health impact of ultrafine particles, and will facilitate optimal selection and implementation of air pollution control strategies.