4.5 Article

ToF-SIMS in battery research: Advantages, limitations, and best practices

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A V S AMER INST PHYSICS
DOI: 10.1116/6.0002850

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This article aims to encourage battery researchers to use ToF-SIMS as a powerful analytical technique and to promote collaboration between ToF-SIMS experts and battery researchers. The article introduces the analysis technique and discusses its suitability and method-specific characteristics for battery research. It also provides guidance on common pitfalls and suggestions for improving data quality in ToF-SIMS analysis of batteries.
Time-of-flight secondary ion mass spectrometry (ToF-SIMS) is a powerful analytical technique whose application has great potential for battery research and that today is not used at its full potential. The goal of this article is to encourage battery researchers to add ToF-SIMS to their research toolbox and to incite ToF-SIMS experts to collaborate more strongly with battery researchers. It is, therefore, addressed to both new and experienced ToF-SIMS operators. First, an introduction to the analysis technique is given, in which the fundamental operating principle and the most common measurement modes are briefly explained. Additionally, we provide information on different machines commercially available. Based on this knowledge, we discuss the suitability of ToF-SIMS for battery research and highlight its method-specific characteristics for corresponding analytical tasks. We show that the high sensitivity of this analytical method (fractions < 10 ppm are detectable) combined with high flexibility for all analyzable materials (organic, inorganic, and hybrid) and sample formats (powders, thin films, electrodes, etc.) make ToF-SIMS particularly relevant for battery research, where the chemical nature of interfaces/interphases and traces of reaction products are of paramount importance. As practical guidance, we introduce and discuss the most common pitfalls when using ToF-SIMS for battery research and give hints on how they could be avoided or minimized. A major goal of this article is to review best practices, focusing on improving data quality, avoiding artifacts, and improving reproducibility.

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