five

Improved Standard Addition Method for Measuring Stable Isotopic Compositions and Its Application to Sulfur Isotope Composition

收藏
Figshare2026-04-28 收录
下载链接:
https://figshare.com/articles/dataset/Improved_Standard_Addition_Method_for_Measuring_Stable_Isotopic_Compositions_and_Its_Application_to_Sulfur_Isotope_Composition/27269785
下载链接
链接失效反馈
官方服务:
资源简介:
The standard addition method (SAM) is widely used to measure the isotopic compositions of natural samples, particularly those with a complex matrix. However, traditional SAM has limitations for isotope systems with significant variations in isotope composition due to its reliance on approximation in calculation and the requirement for a priori estimates of analyte isotopic compositions and accurate concentrations. To overcome the issues, our work proposes an improved SAM that explicitly calculates isotope ratio R (i.e., XE/YE, 34S/32S for example) instead of approximating R* (mass number of isotope X divided by total mass number of all isotopes of an element) with R in SAM. Additionally, the sample fraction within standard-sample mixture in improved SAM is determined using the isotope compositions of standards, sample–standard mixtures, and the mixtures of both standards, rather than relying on sample concentrations and volumes. Both improvements not only overcome the shortcomings of traditional SAM but also empowered the approach’s ability to accurately determine sample concentrations. To validate its effectiveness, we applied the improved SAM to natural samples with substantial sulfur (S) isotope variation (1.94 to 27.19‰) and low S concentration (0.81 to 3.47 μg g–1). The calculated δ34S values and concentrations of these samples are consistent with direct measurements within the error ranges while reducing sample sizes to 20% of those required for direct measurement. Moreover, our method achieves higher accuracy in δ34S values compared with traditional SAM. Both comparisons affirm the reliability and superiority of improved SAM.
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作