Authors: Guilherme Vieira da Motta Missaka | Jeffrey Lopez
Source: ECS Meeting Abstracts (article, 2024-11-22)
Field: Materials Science — Materials Chemistry

Integrated Workflow for High-Throughput Formulation and Characterization of Battery Electrolytes Developing novel electrolyte formulations is a major challenge to the adoption of new electrode chemistries in electrochemical energy storage devices. Electrolyte engineering is a massive field, with hundreds of possible solvents, salts, and additives to choose from. The many building blocks lead to an immense combinatorial problem and enormous parameter space for researchers to search within. Computational tools are a promising solution to the issue, with models that can predict electrolyte properties using from machine learning, 1 thermodynamic calculations, 2 and molecular dynamics. 3 Though incredibly powerful, these calculations can be very computationally expensive, highly specific to a certain subset of electrolytes and often require experimental training data or validation. The availability of electrolyte datasets is currently limited to computational or small experimental datasets, and the absence of standardization in the field makes it challenging to extract a cohesive dataset from the literature. Therefore, there is a need for experimental tools to generate new electrolyte datasets for powerful data analysis techniques to enable novel battery chemistries. In this presentation, we will discuss an automated system for electrolyte formulation and high throughput characterization of electrochemical stability, Coulombic efficiency, and ionic conductivity. Existing automated systems lack solid dispensing and are thus limited in the formulation space that is accessible by automation. 4,5 Our design utilizes a robotic arm to handle and dispense solid and liquid precursors to formulate electrolyte samples and heating and stirring capabilities for electrolyte mixing. Post formulation, the robotic platform utilizes custom-made characterization cells to facilitate high-throughput analysis. Specifically, we have designed a microplate-style Coulombic efficiency testing cell and a series of flow-through sensors to collect ionic conductivity and electrochemical stability information that only utilizes a small electrolyte volume. Initial studies with the platform target aqueous electrolytes to enable ease of troubleshooting outside of a glovebox. Here we screen a variety of salts including LiOAc, LiNO 3 , LiSO 4 , LiTFSI and LiFSI. The chosen salts contain anions spanning a wide range of the Hofmeister series, which classifies them between chaotropic (structure breaking) and kosmotropic (structure making). Chaotropic anions, such as TFSI - and FSI - , affect the water bonding structure, disrupting it, leading to an increase in the electrochemical stability window of the electrolyte. 6 We find that we can explore and expand this phenomenon in combinations of the lithium salts in our high-throughput system. We aim to leverage this high-throughput electrolyte characterization platform to produce high quality, consistent, open-source databases of electrolyte properties that we envision will assist and accelerate the entire field’s research. Our long-term goal is to use the comprehensive data generated to make fundamental advances in developing new electrolyte models that will allow researchers to quickly predict optimal formulations and device performance. 1. S. C. Kim et al., Proceedings of the National Academy of Sciences, 120, e2214357120 (2023). 2. A. Dave, K. L. Gering, J. M. Mitchell, J. Whitacre, and V. Viswanathan, J. Electrochem. Soc., 167, 013514 (2019). 3. B. Ravikumar, M. Mynam, and B. Rai, J. Phys. Chem. C, 122, 8173–8181 (2018). 4. A. Dave et al., arXiv:2111.14786 [cs] (2021) http://arxiv.org/abs/2111.14786. 5. S. Matsuda, K. Nishioka, and S. Nakanishi, Sci Rep, 9, 6211 (2019). 6. D. Reber, R. Grissa, M. Becker, R.-S. Kühnel, and C. Battaglia, Advanced Energy Materials, 11, 2002913 (2021). Workflow ; Throughput ; Battery (electricity) ; Characterization (materials science) ; Computer science ; Electrolyte ; Materials science ; Nanotechnology ; Chemistry ; Operating system ; Database ; Physics ; Electrode ; Wireless Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Fuel Cells and Related Materials