Evaluation of enhanced darkfield microscopy and hyperspectral imaging for rapid screening of TiO2 and SiO2 nanoscale particles captured on filter media.

Here we report on initial efforts to evaluate enhanced darkfield microscopy (EDFM) and light scattering Vis-NIR hyperspectral imaging (HSI) as a rapid screening tool for the offline analysis of mixed cellulose ester (MCE) filter media used to collect airborne nanoparticulate from work environments. For this study, the materials of interest were nanoscale titanium dioxide (TiO2 ) and silicon dioxide (SiO2 ; silica), chosen for their frequent use in consumer products. TiO2 and SiO2 nanoscale particles (NPs) were collected on MCE filter media and were imaged and analyzed via EDFM-HSI. When visualized by EDFM, TiO2 and SiO2 NPs were readily apparent as bright spherical structures against a dark background. Moreover, TiO2 and SiO2 NPs were identified in hyperspectral images. EDFM-HSI images and data were compared to scanning transmission electron microscopy (STEM), a NIST-traceable technique for particle size analysis, and the current gold standard for offline analysis of filter media. As expected, STEM provided more accurate sizing and morphology data when compared to EDFM-HSI, but is not ideal for rapid screening of the presence of NPs of interest since it is a costly, low-throughput technique. In this study, we demonstrate the utility of EDFM-HSI in rapidly visualizing and identifying TiO2 and SiO2 NPs on MCE filters. This screening method may prove useful in expediting time-to-knowledge compared to electron microscopy. Future work will expand this evaluation to other industrially relevant NPs, other filter media types, and real-world filter samples from occupational exposure assessments.

Evaluation of classification methods for identifying multiwalled carbon nanotubes collected on mixed cellulose ester filter media.

Enhanced darkfield microscopy (EDFM) and hyperspectral imaging (HSI) are being evaluated as a potential rapid screening modality to reduce the time-to-knowledge for direct visualisation and analysis of filter media used to sample nanoparticulate from work environments, as compared to the current analytical gold standard of transmission electron microscopy (TEM). Here, we compare accuracy, specificity, and sensitivity of several hyperspectral classification models and data preprocessing techniques to determine how to most effectively identify multiwalled carbon nanotubes (MWCNTs) in hyperspectral images. Several classification schemes were identified that are capable of classifying pixels as MWCNT(+) or MWCNT(-) in hyperspectral images with specificity and sensitivity over 99% on the test dataset. Functional principal component analysis (FPCA) was identified as an appropriate data preprocessing technique, testing optimally when coupled with a quadratic discriminant analysis (QDA) model with forward stepwise variable selection and with a support vector machines (SVM) model. The success of these methods suggests that EDFM-HSI may be reliably employed to assess filter media exposed to MWCNTs. Future work will evaluate the ability of EDFM-HSI to quantify MWCNTs collected on filter media using this classification algorithm framework using the best-performing model identified here - quadratic discriminant analysis with forward stepwise selection on functional principal component data - on an expanded sample set.

Sample preparation method for visualization of nanoparticulate captured on mixed cellulose ester filter media by enhanced darkfield microscopy and hyperspectral imaging.

A significant hurdle in conducting effective health and safety hazard analysis and risk assessment for the nanotechnology workforce is the lack of a rapid method for the direct visualization and analysis of filter media used to sample nanomaterials from work environments that represent potential worker exposure. Current best-known methods include transmission electron microscopy (TEM) coupled with energy dispersive x-ray spectroscopy (EDS) for elemental identification. TEM-EDS is considerably time-, cost-, and resource-intensive, which may prevent timely health and safety recommendations and corrective actions. A rapid screening method is currently being explored using enhanced darkfield microscopy with hyperspectral imaging (EDFM-HSI). For this approach to be effective, rapid, and easy, sample preparation that is amenable to the analytical technique is needed. Here, we compare the sample preparation steps for mixed cellulose ester (MCE) filter media specified in NIOSH Method 7400-Asbestos and Other Fibers by Phase Contrast Microscopy (PCM)-against a new method, which involves saturation of the filter media with acetone. NIOSH Method 7400 was chosen as a starting point since it is an established technique for preparing transparent MCE filters for optical microscopy. Limitations in this method led to the development and comparison of a new method. The new method was faster, easier, and rendered filters more transparent, resulting in improved visualization and analysis of nanomaterials via EDFM-HSI. This new method is suitable for a rapid screening protocol due to its speed, ease of use, and the improvement in image acquisition and analysis.

Control Banding Tools for Engineered Nanoparticles: What the Practitioner Needs to Know.

Control banding (CB) has been widely recommended for the selection of exposure controls for engineered nanomaterials (ENMs) in the absence of ENM-specific occupational exposure limits (OELs). Several ENM-specific CB strategies have been developed but have not been systematically evaluated. In this article, we identify the data inputs and compare the guidance provided by eight CB tools, evaluated on six ENMs, and assuming a constant handling/use scenario. The ENMs evaluated include nanoscale silica, titanium dioxide, silver, carbon nanotubes, graphene, and cellulose. Several of the tools recommended the highest level of exposure control for each of the ENMs in the evaluation, which was driven largely by the hazard banding. Dustiness was a factor in determining the exposure band in many tools, although most tools did not provide explicit guidance on how to classify the dustiness (high, medium, low), and published data are limited on this topic. The CB tools that recommended more diverse control options based on ENM hazard and dustiness data appear to be better equipped to utilize the available information, although further validation is needed by comparison to exposure measurements and OELs for a variety of ENMs. In all CB tools, local exhaust ventilation was recommended at a minimum to control exposures to ENMs in the workplace. Generally, the same or more stringent control levels were recommended by these tools compared with the OELs proposed for these ENMs, suggesting that these CB tools would generally provide prudent exposure control guidance, including when data are limited.

NIOSH field studies team assessment: Worker exposure to aerosolized metal oxide nanoparticles in a semiconductor fabrication facility.

The ubiquitous use of engineered nanomaterials-particulate materials measuring approximately 1-100 nanometers (nm) on their smallest axis, intentionally engineered to express novel properties-in semiconductor fabrication poses unique issues for protecting worker health and safety. Use of new substances or substances in a new form may present hazards that have yet to be characterized for their acute or chronic health effects. Uncharacterized or emerging occupational health hazards may exist when there is insufficient validated hazard data available to make a decision on potential hazard and risk to exposed workers under condition of use. To advance the knowledge of potential worker exposure to engineered nanomaterials, the National Institute for Occupational Safety and Health Nanotechnology Field Studies Team conducted an on-site field evaluation in collaboration with on-site researchers at a semiconductor research and development facility on April 18-21, 2011. The Nanomaterial Exposure Assessment Technique (2.0) was used to perform a complete exposure assessment. A combination of filter-based sampling and direct-reading instruments was used to identify, characterize, and quantify the potential for worker inhalation exposure to airborne alumina and amorphous silica nanoparticles associated with th e chemical mechanical planarization wafer polishing process. Engineering controls and work practices were evaluated to characterize tasks that might contribute to potential exposures and to assess existing engineering controls. Metal oxide structures were identified in all sampling areas, as individual nanoparticles and agglomerates ranging in size from 60 nm to >1,000 nm, with varying structure morphology, from long and narrow to compact. Filter-based samples indicated very little aerosolized material in task areas or worker breathing zone. Direct-reading instrument data indicated increased particle counts relative to background in the wastewater treatment area; however, particle counts were very low overall, indicating a well-controlled working environment. Recommendations for employees handling or potentially exposed to engineered nanomaterials include hazard communication, standard operating procedures, conservative ventilation systems, and prevention through design in locations where engineered nanomaterials are used or stored, and routine air sampling for occupational exposure assessment and analysis.

Refinement of the Nanoparticle Emission Assessment Technique into the Nanomaterial Exposure Assessment Technique (NEAT 2.0).

Engineered nanomaterial emission and exposure characterization studies have been completed at more than 60 different facilities by the National Institute for Occupational Safety and Health (NIOSH). These experiences have provided NIOSH the opportunity to refine an earlier published technique, the Nanoparticle Emission Assessment Technique (NEAT 1.0), into a more comprehensive technique for assessing worker and workplace exposures to engineered nanomaterials. This change is reflected in the new name Nanomaterial Exposure Assessment Technique (NEAT 2.0) which distinguishes it from NEAT 1.0. NEAT 2.0 places a stronger emphasis on time-integrated, filter-based sampling (i.e., elemental mass analysis and particle morphology) in the worker's breathing zone (full shift and task specific) and area samples to develop job exposure matrices. NEAT 2.0 includes a comprehensive assessment of emissions at processes and job tasks, using direct-reading instruments (i.e., particle counters) in data-logging mode to better understand peak emission periods. Evaluation of worker practices, ventilation efficacy, and other engineering exposure control systems and risk management strategies serve to allow for a comprehensive exposure assessment.

Lifestyle and safety practices of firefighters and their relation to cardiovascular risk factors.

BACKGROUND: In the United States, over 50% of the deaths of on-duty firefighters are classified as sudden cardiac deaths. A holistic view of the multiple risk factors and their relation to the prevalence of cardiovascular disease (CVD) is necessary to determine a baseline for prevention. METHODS: This study surveyed 154 firefighters in a large Midwestern county about their individual exposure to particulates, noise, heat stress, skin contamination, and physical stress; lifestyle factors such as exercise, diet, smoking, and alcohol consumption; health status; and demographic factors. RESULTS: Consumption of whole grains and alcohol were associated with a reduction of the risk of heart disease, while higher Body Mass Index (BMI) scores and increasing age were associated with increased risk of heart disease. CONCLUSIONS: Although firefighters are exposed to substantial occupational risks, only lifestyle factors were found to significantly predict CVD and related health issues. BMI is a modifiable risk factor, which, if controlled, could appreciably improve health outcomes.