Battery Materials: In-Situ Analysis with the Scanning Electron Microscope

Abstract

The concept of decarbonisation has been gradually gaining ground. As a result, emphasis has been given to green energy technology in an attempt to offset the need for fossil fuels. The alternative to fossil fuels could be batteries. Not only can batteries be used for stationary energy storage but also for powering electric vehicles. However, more is increasingly required from batteries: they have to store more energy, perform reliably and function for longer, often under extreme conditions depending on the latitude. Among the different battery technologies that have been developed over the years, lithium ion batteries stand out. The main reason is their superior energy and power density, which can support demanding applications such as electromobility. The drive to make batteries more efficient and able to hold more energy, and eventually deliver more power requires in-depth analysis of materials; in order to understand what promotes or undermines materials performance, what leads to failure, and ultimately, the knowledge to design materials for the next generation of batteries.The scanning electron microscope is a powerful tool supporting in situ analysis of materials. In the short-term, as far as cathode precursor powders are concerned, costs are reduced by early detection of impurities and contamination. In the long-term, the battery quality and performance are safeguarded as failure caused by the presence of contaminants is minimised (contamination particles cause short-circuits by penetrating through the battery components). Scanning electron microscope(SEM)-based quality assurance/quality control of battery powders is routinely done with energy dispersive spectroscopy (EDS) and comes with the advantage of high throughput and minimum instrument down time.Battery materials analysis often focuses on formulation testing. That can be either at the industrial level (enquiring if the result is the intended) or R&D level, that is, experimenting with different recipes to single out the optimal one(s). One of the aspects that can be checked using SEM-based analytical techniques is the composition and distribution of coatings and dopants on the powder particle surface. The addition of coatings (Figure 1A) and dopants (Figure 1B) is crucial to ensuring battery performance. They act as performance enhancers (coatings) and dendrite formation suppressants (dopants). These can be detected and mapped in high resolution with SEM-EDS and they can be analysed more accurately with SEM-WDS. At a later stage, the same techniques can be applied to analyse black mass, the material coming from shredded, spent batteries.Given that the atomic numbers (Z) of the elements comprising the battery powders are often close, there is a limit to the information that an electron image can provide due to the closeness of the grey levels. However, there is need to characterise the internal structure of precursor cathode powders as well as other properties such as shape, size, grain boundaries and texture. SEM-EBSD can bypass this issue and can provide information about the particle internal structure by utilising backscattered electrons. The internal structure information of the particles can be used to study cracking development and propagation. A useful tool for battery components post-mortem analysis. The incorporation the Raman-in-SEM, RISE system provides an additional dimension to battery analysis, meaning we can access a wealth of molecular information. Raman spectroscopy can be employed to aid with the analysis of lithium-containing cathode materials in addition to investigating graphitic carbon anode materials.The primary benefit of the SEM-based techniques briefly discussed here is that they can all be applied in the same SEM chamber, in situ, allowing for data to be obtained under the same conditions as part of the same experiment/workflow, promoting, thus, efficiency and throughput – even more so for beam and/or air sensitive samples, saving time required for sample (re)preparation. Another practical benefit is straightforward data correlation which is facilitated when the exact same sample locations have been analysed. Figure 1