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ESI/MS 响应研究 30 年:趋势、矛盾与应用。

30 Years of research on ESI/MS response: Trends, contradictions and applications.

机构信息

Institute of Chemistry, Faculty of Science and Technology, University of Tartu, Ravila 14A, 50411, Tartu, Estonia.

Institute of Chemistry, Faculty of Science and Technology, University of Tartu, Ravila 14A, 50411, Tartu, Estonia; Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada.

出版信息

Anal Chim Acta. 2021 Apr 1;1152:238117. doi: 10.1016/j.aca.2020.11.049. Epub 2020 Dec 1.

Abstract

The variation of ionization efficiency for different compounds has puzzled researchers since the invention of the electrospray mass spectrometry (ESI/MS). Ionization depends on the properties of the compound, eluent, matrix, and instrument. Despite significant research, some aspects have remained unclear. For example, research groups have reached contradicting conclusions regarding the ionization processes. One of the best-known is the significance of the logP value for predicting the ionization efficiency. In this tutorial review, we analyse the methodology used for ionization efficiency measurements as well as the most important trends observed in the data. Additionally, we give suggestions regarding the measurement methodology and modelling strategies to yield meaningful and consistent ionization efficiency data. Finally, we have collected a wide range of ionization efficiency values from the literature and evaluated the consistency of these data. We also make this collection available for everyone for downloading as well as for uploading additional and new ionization efficiency data. We hope this GitHub based ionization efficiency repository will allow a joined community effort to collect and unify the current knowledge about the ionization processes.

摘要

自电喷雾质谱 (ESI/MS) 发明以来,不同化合物的离化效率变化一直困扰着研究人员。离化效率取决于化合物、洗脱液、基质和仪器的性质。尽管进行了大量研究,但仍有一些方面尚不清楚。例如,研究小组在关于离化过程的结论上存在矛盾。其中最著名的是 logP 值对预测离化效率的重要性。在本教程综述中,我们分析了离化效率测量的方法学,以及在数据中观察到的最重要的趋势。此外,我们还针对测量方法和建模策略提出了建议,以获得有意义且一致的离化效率数据。最后,我们从文献中收集了广泛的离化效率值,并评估了这些数据的一致性。我们还将这个数据集放在 GitHub 上,供大家下载,也可以上传额外的和新的离化效率数据。我们希望这个基于 GitHub 的离化效率存储库能够使整个社区共同努力,收集和统一当前关于离化过程的知识。

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