Characterization of Stacking Faults in Many Battery Materials Using FAULTS Software

Defects significantly influence the physicochemical properties of materials, but characterizing faulted structures can be challenging.¹ Stacking faults are detectable using HR-STEM images; however, as this is a local technique, extrapolating the findings to the bulk material may not be straightforward. Conversely, XRD provides an average overview of the structure, making it possible to extract information that offers a more comprehensive description. Nevertheless, most characterization models are based on ideal structures. Refinement results may be poor if the actual structure deviates significantly from the ideal one due to numerous stacking faults. As a result, stacking faults are often overlooked, leading to potential misunderstandings of structure-property correlations.<br/>In this study, we present the structural characterization of a series of faulted materials which are commonly used in batteries (e.g., Li-rich layered oxides, graphite, etc.). HR-STEM images revealed stacking faults in all samples, affecting their XRD patterns. The FAULTS software^2,3 was used to extract information from the XRD patterns to describe the structure accurately. This software constructs the structure with layers, stacking vectors, and probabilities, allowing for the inclusion and refinement of stacking faults. The refined data were then used to correlate structural details with electrochemical performance.<br/><br/>1- M. Reynaud, et al., <i>Chem. Mater</i>., 2023, 35, <b>9</b>, 3345–3363.<br/>2- M. Casas-Cabanas, <i>et al</i>., <i>Zeitschrift fur Krist. Suppl.</i>, 2006, <b>1</b>, 243–248.<br/>3- M. Casas-Cabanas, <i>et al</i>. <i>Appl. Crystallogr.</i>, 2016, <b>49</b>, 2259–2269.