仅基于 LA-ICP-MS 的铁陨石元素表征,ACS Earth and Space Chemistry

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仅基于 LA-ICP-MS 的铁陨石元素表征
ACS Earth and Space Chemistry ( IF 2.9 ) Pub Date : 2024-01-23 , DOI: 10.1021/acsearthspacechem.3c00283
Margareth S. Navarro ₁ , Jacinta Enzweiler ₁ , Álvaro P. Crósta ₁ , André L. R. Moutinho ₁ , Gabriel G. Silva ₂ , Christopher D. K. Herd ₃ , Patrick J. A. Hill ₃

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化学成分是铁陨石分类的一个重要方面。在这项研究中，我们开发并验证了一种使用激光烧蚀电感耦合等离子体质谱 (LA-ICP-MS) 来表征铁陨石化学特征的独立程序。分析序列采用的创新方法包括数据简化软件的新功能，例如使用多个测量标准的外部校准、产量校正和矩阵元素归一化。在九种已知铁陨石中获得的 As、Au、Co、Cr、Cu、Fe、Ga、Ge、Ir、Ni、Os、Pd、Pt、Re、Rh、Ru 和 W 的结果与已发表的值一致，公认的化学分类以及标准化球粒陨石图上的分布。此外，所获得的结果与最近被纳入《陨石公报》的三颗巴西铁陨石（Conceicao do Tocantins─H，IIAB；Nova Olinda─Ogg，IIAB；和 Augusto Pestana─Og，IIIE）的分类结果密切相关数据库。此外，我们还说明，在具有奥古斯托佩斯塔纳陨石不同阶段的区域内进行的 LA-ICP-MS 元素测绘，即使在没有有关自然结构变化的先验知识的情况下，也可以作为化学分类的有效工具。

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Elemental Characterization of Iron Meteorites Solely Based on LA-ICP-MS

Chemical composition is an essential aspect of classifying iron meteorites. In this study, we developed and validated a stand-alone procedure for the chemical characterization of iron meteorites using laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS). The adopted innovative approach to the analytical sequence included new features in data reduction software, such as external calibration with more than one measurement standard, yield corrections, and normalization for matrix elements. The obtained results of As, Au, Co, Cr, Cu, Fe, Ga, Ge, Ir, Ni, Os, Pd, Pt, Re, Rh, Ru, and W in nine known iron meteorites agreed with the published values, their recognized chemical classifications, and distribution on normalized chondrite diagrams. Moreover, the results obtained closely correlate with those employed to classify three Brazilian iron meteorites (Conceicao do Tocantins─H, IIAB; Nova Olinda─Ogg, IIAB; and Augusto Pestana─Og, IIIE), which have recently been included in the Meteoritical Bulletin Database. Additionally, we illustrate that LA-ICP-MS elemental mapping, conducted within regions featuring diverse phases of the Augusto Pestana meteorite, serves as a potent tool for chemical classification even without prior knowledge regarding natural structural variations.

更新日期：2024-01-23

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