BIG-MAP has established a versatile material characterization and performance evaluation module focusing on liquid electrolyte formulations and compatible electrode materials with proven capabilities for lithium-based battery systems. The current state of achievements comprises autonomous high-throughput (HT) formulation, characterization and analysis systems complemented by a traditionally performed characterization approach to obtain reliable and transferable data sets that include both the common experimental targets and the partner-specific designs. The strong interaction and complementarity facilitate a new level of integration in the HT formulation-characterization-performance-analysis-evaluation chain, leading to accelerated identification of lead electrolyte candidates for given cell chemistries and applications.
The well-established high-throughput (HT) framework enables accelerated identification of affordable, electrochemically and thermally outperforming electrolyte candidates exemplified on four defined chemistry tiers. This identification process is based on customized preselection of electrolyte components: conducting salts, solvents/co-solvents, (multi)-functional additives and resulting formulations. The systematic evaluation on electrolyte, electrode and cell level as well as the characterization of concomitant electrolyte│electrode interfaces is carried out over the entire materials lifecycle, including relevant physicochemical, electrochemical and analytical properties and electrolyte/cell performance analysis.
The results are complemented by the results obtained in all other experimental work packages. Acquired data sets, stored on the BIG-MAP Archive, with metadata added to the BIG-MAP Notebook and linked to the BattINFO ontology, are furthermore used for AI-based analysis to recommend novel electrolyte formulations in terms of optimum concentrations and/or different components. Novel holistic and open data formats bundle results with metadata to minimize human error in data handling and maximize utilization of FAIR data principles. Automated data processing frees up human resources and increases the reliability of generated datasets. Apart from breaking new ground on the methodology, the abundance of in-depth data are used to build the BIG.
Through a multi-stage screening pipeline, this HT experimental workflow facilitates identification of hit/lead electrolyte formulations for targeted cell chemistry applications, accompanied by the generation of abundant pertinent data across the entire lifetime of the battery. Having verified the ability to exceed throughput and integrate into existing high-throughput material discovery pipelines for liquid electrolyte formulation studies, the module aims to demonstrate its extensibility by adapting sub-modules to other electrolyte classes and opening up the established methodology to other research categories of interest.