Mesoscale phase transformations in beyond-intercalation-type batteries
Christian Prehal
Department of Information Technology and Electrical Engineering, ETH Zürich, Switzerland, cprehal@ethz.ch
16:15 - 17:15 Tuesday 13 June 2023 Realizing beyond intercalation-type batteries, such as Metal-sulfur (Me-S) batteries, could be game-changing due to a theoretical specific capacity amongst the highest of all batteries paired with the low cost and sustainability of sulfur. However, the insufficient understanding of the mechanism that re-versibly converts sulfur into soluble polysulfides and solid metal sulfides hampers the realization of high-performance Me-S cells.
In this seminar talk, I will present the results of employing operando small and wide angle X-ray scat-tering (SAXS/WAXS) and operando small angle neutron scattering (SANS) to track the growth and dissolution of solid deposits from atomic to sub-micron scales during operating a Li-S battery cell [1]. Machine-learning-assisted stochastic modelling based on the SANS (and SAXS) data allows quantifi-cation of the chemical phase evolution during discharge and charge. Combined with complementary data from transmission electron microscopy and Raman spectroscopy, we show that the deposit is comprised of nanocrystalline Li2S and smaller, solid short-chain polysulfide particles. Knowing this has important implications for influencing the reaction mechanism. As an outlook, I will illustrate how structuring at mesoscopic length scales (1- 1000 nm) could lead to high-rate electrochemical sulfur conversion in the bulk solid state. This may boost the stored energy and solve the cycle life issue of Me-S batteries.
The example on Li-S batteries shows that structural information on mesoscopic length scales is key to understanding complex transformations in energy materials. Operando SAXS/SANS, (cryo-) electron microscopy, and machine-learning-assisted stochastic modelling combine the advantages of integral time-resolved structural information, local element-specific microscopy, and quantitative data analysis.
References:
[1] C. Prehal, V. Wood et al. Nature Communications 13, 6326 (2022)
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