ROSBEEF

Overview

Spatially offset Raman spectroscopy coupled with modeling to monitor the evolution of the battery, electrodes, and electrolyte.

Dr. Mouna BEN YAHIA (ICGM, Univ Montpellier)

The ROSBEEF project develops an integrated approach combining Spatially Offset Raman Spectroscopy (SORS) and theoretical modelling to monitor in situ the chemical and structural transformations of batteries, electrodes, electrolytes, and interfaces. By probing matter beneath the surface, SORS allows distinct analysis of electrodes, electrolyte, and interfaces. Coupled with DFT and Machine Learning, this method offers a robust interpretation of vibrational signatures, contributing to the optimization of electrochemical performance and durability of energy storage systems.

Keywords

Raman Spectroscopy, SORS, batteries, in situ, Na-ion, Li-ion, electrode materials, ASSB modeling, DFT, ML

Tasks

Research activities

Selection, acquisition, and setup of the experimental system

In the first phase (Months 1–6), this involves selecting, purchasing, and receiving the SORS spectrometer and the required instrumental modules for the project.

Experimental development and Raman data analysis

Over months 6–48, this task aims to validate the operation of the SORS device on model materials, and calibrate the relation between spatial offset and probed depth — a major milestone of the project. Tests on positive and negative electrodes will establish a reference Raman database. Ultimately, automation of the setup is intended to enhance reliability, precision, and reproducibility of measurements.

Electrochemical validation and transfer to solid-state batteries

During months 24–48, this task will apply the SORS method to Li-ion cells (ex situ first and then in situ) to validate its efficiency for studying electrochemical phenomena. Special attention will be paid to detecting the signature of the solid electrolyte interphase (SEI). Finally, the methodology will be extended to all-solid batteries, resulting in a robust SORS protocol for analyzing next-generation battery systems.

Multiscale modelling and theoretical validation

Also over months 6–48, this task aims to simulate Raman spectra for model systems to validate the SORS technology and ensure consistency between experimental and theoretical results. Deviations observed will refine DFT models and improve the reliability of spectral assignments. Machine Learning approaches will then be implemented to identify Raman signatures depending on composition, oxidation state, and structure, as well as to interpret ex situ and in situ experimental data.