Abstract
Data-driven material discovery has recently become popular in the field of next-generation secondary batteries. However, it is important to obtain large, high quality data sets to apply data-driven methods such as evolutionary algorithms or Bayesian optimization. Combinatorial high-throughput techniques are an effective approach to obtaining large data sets together with reliable quality. In the present study, we developed a combinatorial high-throughput system (HTS) with a throughput of 400 samples/day. The aim was to identify suitable combinations of additives to improve the performance of lithium metal electrodes for use in lithium batteries. Based on the high-throughput screening of 2002 samples, a specific combination of five additives was selected that drastically improved the coulombic efficiency (CE) of a lithium metal electrode. Importantly, the CE was remarkably decreased merely by removing one of these components, highlighting the synergistic basis of this mixture. The results of this study show that the HTS presented herein is a viable means of accelerating the discovery of ideal yet complex electrolytes with multiple components that are very difficult to identify via conventional bottom-up approach.
Highlights
There are currently increasing demands for safe electrical energy storage devices having high energy density and long lifespans for use in electric vehicles and smart grid systems, so as to make efficient use of renewable energy sources
We developed an high-throughput screening system (HTS) method with the aim of identifying optimal combinations of additives to improve the performance of lithium metal electrodes
We developed multi-channel electrochemical cells for the HTS approach based on a microplate technique that has often been used in the field of biochemical research
Summary
There are currently increasing demands for safe electrical energy storage devices having high energy density and long lifespans for use in electric vehicles and smart grid systems, so as to make efficient use of renewable energy sources. System[13,14,15,16] These studies successfully demonstrated the effectiveness of HTS approaches with regard to selecting solid-state battery materials, this technique would seem to have limited usefulness in the screening of liquid electrolytes, unless these electrolytes are complex multi-component mixtures, such as combinations of lithium salts, solvents and additives. The identification of the most appropriate combination could be performed using an HTS-based approach Such methods have not yet been applied for this purpose, due to difficulties in simultaneously performing automated sequential electrochemical operations based on robotics engineering together with big-data processing and highly sensitive analyses. We developed an HTS method with the aim of identifying optimal combinations of additives to improve the performance of lithium metal electrodes
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