Developing a rapid screening method for direct visualization of nanoparticles captured on filter media during occupational exposure assessments

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As engineered nanomaterials (ENMs) are increasingly incorporated into manufacturing processes and consumer goods, the nanotechnology workforce is also growing, with 6 million workers anticipated by 2020, of which 2 million are projected to work in the U.S.1 Risk assessment for nanotechnology workers is still in its infancy since occupational exposure assessment strategies and physiologic and health outcomes of occupational exposure to ENMs have not yet been well characterized. Best-known methods for ENM exposure assessment are based on using air sampling tools to characterize and quantify ENMs; these include real-time direct-reading instruments (DRIs) to measure airborne particle counts and filter-based methods collected in tandem for off-line direct visualization by transmission electron microscopy (TEM) and for elemental analysis.2 Current methods used for direct visualization of ENMs are based on those developed two decades ago for micron-sized asbestos,3,4 and are not appropriate for real-world ENM exposures. Further, TEM is low-throughput, expensive, and time- and resource-intensive. Therefore, the overarching goal of this project is to rapidly advance exposure assessment for the nanotechnology workforce by developing and testing a new protocol for filter-based sample analysis. Enhanced darkfield microscopy (EDFM), coupled with hyperspectral imaging (HSI) and mapping, has been used to identify ENMs in a variety of matrices and shows great promise as a rapid screening tool for direct visualization of filter media from occupational exposure assessments. The CytoViva EDFM-HSI system utilizes oblique angle illumination to enhance signal-to-noise of nanomaterials5 and combines imaging and spectrophotometry using advanced optics and algorithms to capture a spectrum from 400nm-1000nm for each pixel in a hyperspectral image.5–11 Using HSI software, it is possible to identify, locate, and map ENMs within a sample based on its unique spectral response. Spectral profiles for known ENMs are collected into reference spectral libraries (RSLs), which are used to identify those ENMs in other samples through a mapping algorithm.7,8 Based on mapping results, an estimation of ENM concentration and size may be obtained. Preliminary work has explored the use of EDFM-HSI for the rapid visualization and identification of silicon dioxide (SiO2; silica) nanoparticles (NPs) captured on mixed cellulose ester (MCE) filter media (3.0mg loading concentration). A silica RSL was created7 and was used to identify silica NPs on filter media using the spectral angle mapper (SAM) algorithm. Pixels with positive spectral matches to the RSL were indicated by a false coloration overlaid on the hyperspectral image. Results indicate that silica NPs can be easily visualized by EDFM and, moreover, can be specifically mapped using hyperspectral data. Sample preparation has been refined, with an improved, faster method presented here. Future directions include expanding the EDFM-HSI protocol to other filter media types (e.g., polycarbonate) and to other industrially relevant ENMs, including mixed material exposures from field sampling. EDFM-HSI is poised to expedite analysis of NPs captured on filter media and will allow for timely implementation of worker health and safety recommendations, if needed, as well as facilitate compliance with potential future occupational exposure limits (OELs) for nanomaterials.

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Journal: TechConnect Briefs
Volume: 1, Advanced Materials: TechConnect Briefs 2017
Published: May 14, 2017
Pages: 333 - 336
Industry sector: Advanced Materials & Manufacturing
Topic: Environmental Health & Safety of Nanomaterials
ISBN: 978-0-9975117-8-9