Combinatorial methods in advanced battery materials design

Abstract

In the search for better performing battery materials, researchers have increasingly ventured into complex composition spaces, including numerous pseudo-quaternaries, with further substitutions being either explored experimentally or proposed based on computation. Given the vast composition spaces that need exploring, experimental combinatorial science can play an important role in accelerating the development of advanced battery materials and is arguably the best means to obtain a sufficiently large data set to truly bring a high degree of precision to advanced computational techniques such as machine-learning. Herein, we present a robust high-throughput synthesis platform that is currently being used in the McCalla lab at McGill University to study Li-ion cathodes, anodes, and solid electrolytes, as well as Na-ion cathodes. The synthesis methods used are presented in detail, as are the high-throughput characterization techniques we utilize regularly (X-ray diffraction, electrochemical testing, and electrochemical impedance spectroscopy). We quantitatively determine the high precision and reproducibility achieved by this combinatorial system and also demonstrate its versatility by presenting for the first time combinatorial data for two high-power anodes for Li-ion batteries (TiNb2O7 and W3Nb14O44), as well as solid state electrolyte Li7La3Zr2O12. Our methods reproduce accurately the results from the literature for bulk samples, indicating that the high-throughput methodology utilizing small milligram-scale samples scales up extremely well to the larger sample sizes typically used in both the literature and industry. The throughput of this combinatorial infrastructure has a current limit of 896 XRD patterns and 896 EIS patterns a week and 448 cyclic voltammograms running simultaneously.