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Original Articles

Use of Raman spectroscopy to identify carbon nanotube contamination at an analytical balance workstation

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ABSTRACT

Carbon nanotubes (CNTs) are cylindrical molecules of carbon with diverse commercial applications. CNTs are also lightweight, easily airborne, and have been shown to be released during various phases of production and use. Therefore, as global CNT production increases, so do concerns that CNTs could pose a safety threat to those who are exposed to them. This makes it imperative to fully understand CNT release scenarios to make accurate risk assessments and to implement effective control measures. However, the current suite of direct-reading and off-line instrumentation used to monitor the release of CNTs in workplaces lack high chemical specificity, which complicates risk assessments when the sampling and/or measurements are performed at a single site where multiple CNT types are handled in the presence of naturally occurring background particles, or dust. Herein, we demonstrate the utility of Raman spectroscopy to unequivocally identify whether particulate matter collected from a multi-user analytical balance workstation comprised CNTs, as well as, whether the contamination included CNTs that were synthesized by a Ni/Y-catalyzed electric-arc method or a Co/Mo-catalyzed chemical vapor deposition method. Identifying the exact CNT type generated a more accurate risk assessment by knowing the metallic impurities involved, and it also led to the identification of the users who handled these CNTs, a review of their handling techniques, and an improved protocol for safely weighing CNTs.

Acknowledgments

The authors thank Winston Layne, Charles L. Golding, Zachary D. Hiscocks, Rebeca Muller Barrera, and Nancy S. Jacobsen for their scientific contributions to this work.

Funding

The authors thank the National Institute for Environmental Health Sciences (Grant R15-ES023666), the Semiconductor Research Corporation Engineering Research Center for Environmentally Benign Semiconductor Manufacturing (Grants 425-042 and 425-048), and the Dean of the UT Dallas School of Natural Sciences and Mathematics for support of this work.

Disclaimer

The content of this work is solely the responsibility of the authors and does not necessarily represent the official views of the Semiconductor Research Corporation or the National Institutes of Health.

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