Speaker
Description
Operando synchrotron and neutron techniques have become indispensable tools for resolving the structural and chemical complexity of electrochemical phase transformation in battery materials. In this talk, I present how operando SAXS/WAXS and SANS, combined with machine learning-assisted stochastic modelling, can quantify conversion mechanisms in post lithium-ion batteries at length scales difficult to access with other techniques.
The central focus is lithium-sulfur (Li-S) batteries, where the transformation between solid sulfur and solid lithium sulfide largely defines battery performance and can proceed through solid-liquid-solid, quasi-solid-state, or solid-state pathways depending on electrolyte and cathode composition. I present results tracking the growth and dissolution of solid deposits at nanometer scales across these different regimes [1-3]. Combined with cryo-transmission electron microscopy, the data show that the deposit consists of nanocrystalline Li₂S alongside smaller solid short-chain polysulfide particles. These findings inform strategies for controlling the reaction pathway in next-generation Li-S cells.
As a second example, I will discuss how related approaches apply to sodium-ion batteries, where hard carbon anode sodiation and desodiation mechanisms are resolved at the nanoscale. I will present preliminary work on active Bayesian learning for scattering-based quality control of hard carbon synthesis, with relevance to process optimisation in practical materials manufacturing.
References:
[1] C. Prehal, V. Wood et al., Nature Communications 2022, 6326
[2] J.-M. von Mentlen, C. Prehal et al., ACS Nano 2025 19, 16626
[3] P. Dutta, C. Prehal et al., ACS Energy Letters 2025, 10, 5722