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Study of Long-Term Determination Accuracy for REEs in Geological Samples by Inductively Coupled Plasma Quadrupole Mass Spectrometry
This work presents the long-term determination accuracy study of ICP-QMS for rare earth elements (REEs) in geological matrices. Following high-pressure closed acidic decomposition, REEs are measured repetitively across seven months by ICP-QMS. Under optimum experimental conditions (including spray c...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7827012/ https://www.ncbi.nlm.nih.gov/pubmed/33430078 http://dx.doi.org/10.3390/molecules26020290 |
Sumario: | This work presents the long-term determination accuracy study of ICP-QMS for rare earth elements (REEs) in geological matrices. Following high-pressure closed acidic decomposition, REEs are measured repetitively across seven months by ICP-QMS. Under optimum experimental conditions (including spray chamber temperature, gas flow rate, sampling depth, etc.), the REE contents in geological standard materials from basic (basalt BCR-2 and BE-N) to intermediate (andesite AGV-2) and up to acidic (granite GSR-1) show good agreement with the certified values, giving relative errors below 10%. Here, the influence of two storage materials (perfluoroalkoxy PFA and polypropylene PP) on the long-term determination accuracy of REEs has also been monitored. It is found that the relative errors of REEs using a PFA container range from −6.6 to 6.3% (RSDs < 6.0%), while that using a PP container are within −4.0 to 3.9% (RSDs < 4.6%). By using PP material as a solution storage container, the accuracy of REEs quantification in a series of real geological samples are checked, showing the RSDs of less than 5.0%. This work first clarifies the long-term stability of REEs quantification by ICP-QMS covering two types of storage materials, confirming the reasonability of PP material as a daily storage container in terms of higher data precision and lower cost. |
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