Surface Analysis of Pristine and Cycled NMC/Graphite Lithium-Ion Battery Electrodes: Addressing the Measurement Challenges

Lithium-ion batteries are the most ubiquitous energy storage devices in our everyday lives. However, their energy storage capacity fades over time due to chemical and structural changes in their components, via different degradation mechanisms. Understanding and mitigating these degradation mechanisms is key to reducing capacity fade, thereby enabling improvement in the performance and lifetime of Li-ion batteries, supporting the energy transition to renewables and electrification. In this endeavor, surface analysis techniques are commonly employed to characterize the chemistry and structure at reactive interfaces, where most changes are observed as batteries age. However, battery electrodes are complex systems containing unstable compounds, with large heterogeneities in material properties. Moreover, different degradation mechanisms can affect multiple material properties and occur simultaneously, meaning that a range of complementary techniques must be utilized to obtain a complete picture of electrode degradation. The combination of these issues and the lack of standard measurement protocols and guidelines for data interpretation can lead to a lack of trust in data. Herein, we discuss measurement challenges that affect several key surface analysis techniques being used for Li-ion battery degradation studies: focused ion beam scanning electron microscopy, X-ray photoelectron spectroscopy, Raman spectroscopy, and time-of-flight secondary ion mass spectrometry. We provide recommendations for each technique to improve reproducibility and reduce uncertainty in the analysis of NMC/graphite Li-ion battery electrodes. We also highlight some key measurement issues that should be addressed in future investigations.

Automated summary

This article discusses the measurement challenges that affect surface analysis techniques used for Li-ion battery degradation studies and provides recommendations for improving reproducibility and reducing uncertainty. The article highlights key measurement issues that need to be addressed.