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Correction for the 17O interference in δ13C measurements when analyzing CO2 with stable isotope mass spectrometry (IUPAC Technical Report)

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Brand,  Willi A.
Service Facility Stable Isotope, Dr. W. A. Brand, Max Planck Institute for Biogeochemistry, Max Planck Society;

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Zitation

Brand, W. A., Assonov, S., & Coplen, T. B. (2010). Correction for the 17O interference in δ13C measurements when analyzing CO2 with stable isotope mass spectrometry (IUPAC Technical Report). Pure and Applied Chemistry, 82(8), 1719-1733. doi:10.1351/PAC-REP-09-01-05.


Zitierlink: https://hdl.handle.net/11858/00-001M-0000-000E-D964-7
Zusammenfassung
Measurements of δ(13C) determined on CO2 with an isotope-ratio mass spectrometer (IRMS) must be corrected for the amount of 17O in the CO2. For data consistency, this must be done using identical methods by different laboratories. This report aims at unifying data treatment for CO2 IRMS by proposing (i) a unified set of numerical values, and (ii) a unified correction algorithm, based on a simple, linear approximation formula. Because the oxygen of natural CO2 is derived mostly from the global water pool, it is recommended that a value of 0.528 be employed for the factor λ, which relates differences in 17O and 18O abundances. With the currently accepted N(13C)/N(12C) of 0.011 180(28) in VPDB (Vienna Peedee belemnite) reevaluation of data yields a value of 0.000 393(1) for the oxygen isotope ratio N(17O)/N(16O) of the evolved CO2. The ratio of these quantities, a ratio of isotope ratios, is essential for the 17O abundance correction: [N(17O)/N(16O)]/[N(13C)/N(12C)] = 0.035 16(8). The equation [δ(13C) ≈ 45δVPDB-CO2 + 2 17R/13R (45δVPDB-CO2 – λ46δVPDB-CO2)] closely approximates δ(13C) values with less than 0.010 ‰ deviation for normal oxygen-bearing materials and no more than 0.026 ‰ in extreme cases. Other materials containing oxygen of non-mass-dependent isotope composition require a more specific data treatment. A similar linear approximation is also suggested for δ(18O). The linear approximations are easy to implement in a data spreadsheet, and also help in generating a simplified uncertainty budget.