Assessing Graphite Particle Size and Morphology in Lithium-Ion Battery Slurries with Dynamic Image Analysis

Graphite is the dominant active material used in lithium-ion battery anodes, and its physical properties play a decisive role in slurry preparation, electrode coating, drying, calendering, and final cell performance. Besides chemical purity and crystallinity, the particle size and particle shape distributions of graphite strongly influence how particles disperse in aqueous binder systems, how they interact under shear, and how efficiently they pack during electrode formation. Differences in morphology can affect slurry viscosity, sedimentation behavior, coating homogeneity, electrode porosity, tortuosity, and ultimately the electrochemical response of the anode.
Natural and artificial graphites are commonly used in battery applications, either individually or as blends, because they offer different advantages in terms of cost, morphology, packing behavior, and electrochemical performance. Natural graphite typically consists of more irregular, platelet-like particles, while artificial graphite often exhibits a more engineered particle morphology. These differences are clearly visible in scanning electron micrographs and are expected to influence slurry behavior. However, microscopy alone is usually qualitative and limited in statistical relevance. For process development and quality control, a quantitative method is required to describe particle size and shape distributions directly in the slurry state.
Dynamic image analysis offers a suitable approach for this purpose. By measuring a large number of individual particles, it provides statistically meaningful information about particle size and morphology, including parameters such as particle diameter, aspect ratio, circularity, elongation, and convexity. Compared with techniques that report only equivalent spherical size, dynamic image analysis can distinguish between materials with similar particle size distributions but different particle shapes. This is particularly relevant for graphite, where morphology is often as important as size.
The aim of this study is to demonstrate the potential of dynamic image analysis as a quantitative method for characterizing graphite particle size and shape directly in aqueous battery slurries. By combining statistically relevant particle morphology data with qualitative microscopy observations, the study highlights how differences in graphite morphology can be identified, compared, and related to expected slurry-processing behavior.
The work focuses on showing that dynamic image analysis can detect both broad differences between graphite types and more subtle morphology variations within similar material classes, including formulation-driven changes in mixed systems. This establishes the method as a useful standalone tool for slurry material screening and quality control, while providing the basis for future correlation with rheological, impedance, and density-related measurements.