A large scale multi-modal workflow for battery characterization: from concept to implementation

The development of material acceleration platforms in battery research requires integrating complementary techniques and correlating heterogeneous experimental datasets. Here, this challenge is tackled in a large-scale multimodal program involving fifteen laboratories and facilities across Europe. Coordinated multi-site experiments are performed on state-of-the-art graphite / LiNiO2 Li-ion full cells to address two archetypal 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 and centralized workflow is demonstrated, from sample production and delivery, to metadata and data handling, generating seventy-five concatenated datasets shared among all partners. Their integrated analysis shows that scientific conclusions depend critically on both the observable chosen to describe electrode properties, and the measurement technique employed. Individual experiments provide detailed information into specific aspects, such as crystal structures, redox activity, surface processes, morphology, etc., but can also function as binary diagnostic tool. Two-dimensional observable-technique patterns are introduced, in which each pixel encodes a yes, no or uncertain answer to a given scientific question. These patterns serve as multi-property metaviews, e.g. visual genotypes, enabling to classify material behavior and technique suitability according to predefined user demand and criteria, highlighting the interdependencies between measurement choices, extracted parameters and scientific interpretation. This multimodal workflow establishes a proof-of-concept for correlative analysis and underscores challenges toward fully integrated, automated and holistic approaches in energy material science.

💡 Research Summary

This paper presents a pan‑European, large‑scale multimodal workflow for the comprehensive characterization of lithium‑ion batteries, specifically graphite/LiNiO₂ full cells, and demonstrates its implementation from concept through to data integration. Fifteen research institutions and facilities across Europe coordinated their efforts under the BIG‑MAP project (Battery 2030+), focusing on two central scientific questions: (1) how does electrolyte composition affect electrode properties, and (2) how do electrode materials evolve when cells reach end‑of‑life.

The workflow is organized into six sequential steps. First, a clear scientific case is defined, followed by the design of an experimental program that specifies the cell chemistry, the two carbonate‑based electrolytes (standard and modified), and the suite of analytical techniques to be employed. Third, standardized sample production, handling, and cycling protocols are established to ensure that every partner works on identical material batches. Fourth, the experimental campaign is executed in parallel across multiple sites, leveraging high‑resolution synchrotron X‑ray diffraction and absorption, X‑ray computed tomography (including hierarchical XCT), electron microscopy (including FIB‑CT), electrochemical impedance spectroscopy (EIS), on‑line electrochemical mass spectrometry (OEMS), neutron radiography/tomography, and a range of spectroscopic methods.

A critical component of the workflow is data management. All raw and processed data, together with rich metadata, are stored in a central repository following FAIR principles. A common metadata schema and version‑controlled data upload procedures guarantee reproducibility and traceability. In total, 75 concatenated datasets were generated and shared among the partners.

To make sense of this heterogeneous information, the authors introduce a two‑dimensional observable‑technique matrix, where each pixel encodes a “yes”, “no”, or “uncertain” answer to a given scientific question for a specific measurement technique. This visual “genotype” or meta‑view enables rapid assessment of which techniques provide definitive answers, which are ambiguous, and where complementary methods are needed. For example, electrolyte effects on interfacial resistance are clearly captured by EIS and X‑ray absorption spectroscopy, whereas crystal‑structure changes are only discernible via high‑resolution diffraction. Aging‑related micro‑cracking and particle fracture are revealed by FIB‑CT and electron microscopy, while gas evolution pathways are elucidated by combining OEMS with neutron imaging.

The integrated analysis shows that scientific conclusions are highly dependent on both the observable selected and the measurement technique employed. Individual techniques deliver deep insight into specific aspects (e.g., redox activity, surface chemistry, morphology) but also serve as binary diagnostic tools within the broader matrix. By correlating subsets of techniques, the workflow uncovers interdependencies between degradation mechanisms, electrolyte composition, and structural evolution that would remain hidden in single‑technique studies.

The paper also discusses practical challenges encountered during the campaign: sample logistics (temperature and humidity control during transport), heterogeneity of data formats across instruments, consistent metadata capture, and handling of uncertain or missing data. To address these, the consortium developed standard operating procedures, automated data‑ingestion scripts, and statistical outlier detection routines.

In conclusion, this work establishes a proof‑of‑concept for a fully standardized, centralized, and multimodal battery characterization platform. It demonstrates that large‑scale, coordinated experimental workflows, combined with rigorous data management and visual meta‑analysis, can accelerate material discovery, electrolyte optimization, and aging studies. The authors anticipate that extending the workflow with automated pipelines and AI‑driven analysis will further reduce the time‑to‑insight for next‑generation energy storage technologies.