Time-of-flight secondary ion mass spectrometry (TOF-SIMS) is a quasi-non-destructive technique capable of analyzing the outer monolayers of a solid sample and detecting all elements of the periodic table and their isotopes. Its ability to analyze the outer monolayers resides in sputtering the sample surface with a low-dose primary ion gun, which, in turn, imposes the use of a detector capable of counting a single ion at a time. Consequently, the detector saturates when more than one ion arrives at the same time hindering the use of TOF-SIMS for quantification purposes such as isotope ratio estimation. Even though a simple Poisson-based correction is usually implemented in TOF-SIMS acquisition software to compensate the detector saturation effects, this correction is only valid up to a certain extent and can be unnoticed by the inexperienced user. This tutorial describes a methodology based on different practices reported in the literature for dealing with the detector saturation effects and assessing the validity limits of Poisson-based correction when attempting to use TOF-SIMS data for quantification purposes. As a practical example, a dried lithium hydroxide solution was analyzed by TOF-SIMS with the aim of estimating the 6Li/7Li isotope ratio. The approach presented here can be used by new TOF-SIMS users on their own data for understanding the effects of detector saturation, determine the validity limits of Poisson-based correction, and take into account important considerations when treating the data for quantification purposes.

中文翻译：

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尝试通过飞行时间二次离子质谱法进行同位素比定量时评估死区时间效应。

飞行时间二次离子质谱 (TOF-SIMS) 是一种准无损技术，能够分析固体样品的外部单层并检测周期表中的所有元素及其同位素。它分析外部单层的能力在于用低剂量初级离子枪溅射样品表面，这反过来又要求使用能够一次对单个离子进行计数的检测器。因此，当超过一种离子同时到达时，检测器就会饱和，从而阻碍使用 TOF-SIMS 进行同位素比估计等量化目的。尽管通常在 TOF-SIMS 采集软件中实施简单的基于泊松的校正来补偿探测器饱和效应，但这种校正仅在一定程度上有效，并且可能会被没有经验的用户注意到。本教程描述了一种基于文献中报道的不同实践的方法，用于处理探测器饱和效应，并在尝试使用 TOF-SIMS 数据进行量化时评估基于泊松的校正的有效性限制。作为一个实际示例，通过 TOF-SIMS 分析干燥的氢氧化锂溶液，目的是估算 6Li/7Li 同位素比。新的 TOF-SIMS 用户可以使用这里介绍的方法来处理自己的数据，以了解探测器饱和的影响，确定基于泊松的校正的有效性限制，并在出于量化目的处理数据时考虑重要因素。