Quantification of Carbon Nanotubes by Raman Analysis
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2018/06/01
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Description:The increasing prevalence of carbon nanotubes (CNTs) in manufacturing and research environments, together with the potential exposure risks, necessitates development of reliable and accurate monitoring methods for these materials. We examined quantification of CNTs by two distinct methods based on Raman spectroscopy. First, as measured by the Raman peak intensity of aqueous CNT suspensions, and second, by Raman mapping of air filter surfaces onto which CNTs were collected as aerosols or applied as small-area (0.05 cm2) deposits. Correlation (R2 = 0.97) between CNT concentration and Raman scattering intensity for suspensions in cuvettes was found over a concentration range from about 2 to 10 ug/ml, but measurement variance precludes practical determination of a calibration curve. Raman mapping of aerosol sample filter surfaces shows correlation with CNT mass when the surface density is relatively high (R2 = 0.83 and 0.95 above about 5 ug total mass on filter), while heterogeneity of CNT deposition makes obtaining representative maps of lower density samples difficult. This difficulty can be mitigated by increasing the area mapped relative to the total sample area, improving both precision and the limit of detection (LOD). For small-area deposits, detection of low masses relevant to occupational monitoring can be achieved, with an estimated LOD of about 50 ng. [Description provided by NIOSH]
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ISSN:2398-7308
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Volume:62
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Issue:5
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NIOSHTIC Number:nn:20051422
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Citation:Ann Work Expo Health 2018 Jun; 62(5):604-612
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Contact Point Address:M. Eileen Birch, Division of Applied Research and Technology, Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health, MS C-18, 1150 Tusculum Ave, Cincinnati, OH 45226
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Federal Fiscal Year:2018
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Peer Reviewed:True
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Source Full Name:Annals of Work Exposures and Health
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Main Document Checksum:urn:sha-512:6e67dbad005a7f46c74bb0547b67d513e4a02d6d323a3daa11dde50a8d2a68020f5fdc89613502f1b642a69abb80e7ee0185ab1c3672a4aa868c2edba4cc40be
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