Hostname: page-component-78c5997874-8bhkd Total loading time: 0 Render date: 2024-11-05T11:40:35.564Z Has data issue: false hasContentIssue false
  • English
  • Français

Interlaboratory Variability of Radiocarbon Results Obtained from Blind AMS Analyses on Several Modern Carbon Samples

Published online by Cambridge University Press:  18 July 2016

Glenn A Norton
Glenn A Norton*
Affiliation:
Center for Industrial Research and Service, Iowa State University, Ames, Iowa 50011, USA. Email: [email protected].
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

Three samples of modern-day vegetation collected in 2009–2010 and a sample of bioethanol produced in 2010 were analyzed for radiocarbon by 5 different accelerator mass spectrometry (AMS) laboratories in a blind analysis study. The magnitude of any variability in the reported results for percent modern carbon (pMC) was observed. Results indicated that the interlaboratory repeatability on the samples of vegetation was generally very good, varying by no more than ~1 pMC for 2 of the 3 samples. Results for the bioethanol were less consistent, and varied by 5.5 pMC (ranging from 101.9 to 107.4 pMC). Variations in the δ13C values used to correct for isotopic fractionation did not account for the variability observed in the pMC values for this sample. In view of the homogeneity of the bioethanol and its inherent simplicity in composition, this suggests that volatile liquid fuels may be more difficult to prepare for analysis without incurring significant sample processing errors. When viewing all of the results as a whole, the analytical errors (incorporating both instrumental and sample processing errors) appeared to be more random than systematic in nature. Because of analytical uncertainties in pMC measurements, as well as inherent local and regional variations in 14C activity levels known to occur in modern-day biomass, there is not a precise (accurate to 2 decimal places) correction factor for negating the bomb carbon effect that is applicable to all biofuels or other biobased products being analyzed in accordance with ASTM Method D6866. Therefore, a reasonable correction factor (currently set at 0.95) needs to be consistently applied in order to make comparisons of biobased content data from different laboratories more valid. Results from this study indicate that, for samples containing predominantly modern carbon, reporting results to the nearest 0.1 pMC is not warranted.

Type
Technical Note
Information
Radiocarbon , Volume 53 , Issue 3 , 2011 , pp. 551 - 556
Copyright
Copyright © 2011 The Arizona Board of Regents on behalf of the University of Arizona 

References

ASTM International. 2010. Method D 6866-10: Standard test methods for determining the biobased content of solid, liquid, and gaseous samples using radiocarbon analysis. ASTM International, 100 Bar Harbor Drive, West Conshohocken, Pennsylvania, USA.Google Scholar
Dijs, IJ, van der Windt, E, Kaihola, L, Van der Borg, K. 2006. Quantitative determination by 14C analysis of the biological component in fuels. Radiocarbon 48(3):315–23.CrossRefGoogle Scholar
Hua, Q, Barbetti, M. 2004. Review of tropospheric bomb radiocarbon data for carbon cycle modeling and age calibration purposes. Radiocarbon 46(3):1273–98.Google Scholar
Noakes, J, Norton, G, Culp, R, Nigam, M, Dvoracek, D. 2006. A comparison of analytical methods for the certification of biobased products. In: Chatupnik, S, Schönhofer, F, Noakes, J, editors. LSC 2005, Advances in Liquid Scintillation Spectrometry. Tucson: Radiocarbon. p 259–71.Google Scholar
Norton, GA. 2009. Direct analysis of automotive fuels for bioethanol content using radiocarbon analysis. Radiocarbon 51(3):995–1003.CrossRefGoogle Scholar
Stuiver, M, Polach, HA. 1977. Discussion: reporting of 14C data. Radiocarbon 19(3):355–63.Google Scholar
Tans, PP, de Jong, AFM, Mook, WG. 1979. Natural atmospheric 14C variation and the Suess effect. Nature 280(5725):826–8.Google Scholar