A Large Scale Multi-Modal Workflow for Battery Characterization

Abstract

The development of material acceleration platforms in battery research requires integrating complementary techniques and correlating heterogeneous experimental datasets. Here, this challenge is addressed through a large-scale multimodal program involving fifteen European laboratories and facilities. Coordinated experiments on graphite/ (Formula presented.) Li-ion full cells address two scientific questions: is the electrolyte composition impacting electrode properties, and how do electrode materials evolve when cells are cycled to their end-of-life? A fully standardized workflow is demonstrated, from sample production and delivery, to metadata and data handling, generating seventy-five concatenated datasets shared among partners. Their integrated analysis reveals that scientific conclusions strongly depend on both the observable chosen to describe electrode properties, and the characterization technique employed. Beyond providing detailed insights into specific aspects as crystal structures, redox activity, surface processes or morphology, individual experiments can also act as binary diagnostic tool. Two-dimensional observable-technique patterns are introduced, where each pixel encodes a yes, no or uncertain answer to a scientific question. These multi-property metaviews enable the classification of material behavior and technique suitability according to user demand and criteria, highlighting the interdependence between measurement, extracted parameters, and scientific interpretation. This work establishes a proof-of-concept toward integrated and holistic battery research workflows.