Periodic Reporting for period 1 - OPINCHARGE (OPerando analyses and modelling of INterface dynamics and CHARGE transport in lithium-ion batteries)
Reporting period: 2023-06-01 to 2024-11-30
Battery technology has been crucial in developing new energy and transportation solutions, helping to create a cleaner, more affordable, and secure energy system. However, progress is slow because of a lack of detailed understanding of atomic level processes occurring at battery interfaces. To tackle this, the OPINCHARGE consortium—made up of 10 organizations from 7 countries—is working together to develop advanced techniques to study battery processes in greater detail than ever before. Our approach focuses on three main types of innovative analyses: chemical-based, isotope-based, and physics-based techniques. The key methods we improve include X-ray scattering, enhanced Raman spectroscopy, Scanning Transmission Electron Microscopy-Electron Energy Loss Spectroscopy & Energy Dispersive X-ray Spectroscopy, Focused Ion Beam-Secondary Ion Mass Spectroscopy, Neutron imaging, Online Electrochemical Mass Spectrometry, and Nuclear Magnetic Resonance spectroscopy. Additionally, the team uses Artificial Intelligence and Machine Learning to make data collection and analysis faster and more effective. We also emphasize open data sharing and collaboration to align with the Europe wide initiatives of the research community. The project will last for 36 months and is divided into seven work packages. Luxembourg Institute of Science and Technology leads the consortium while also ensuring that findings are widely shared to maximize the project's impact and benefit the broader scientific community.
The work performed can be described under these three groups: chemical-based, isotope-based, and physics-based analysis of battery-relevant interfaces. Consequently, main techniques that are being used in operando mode are: X-ray scattering, enhanced Raman, STEM-EELS & EDX, FIB-SIMS, Neutron imaging, OEMS and NMR. Parallelly, the consortium integrates AI/Machine Learning support, in order to improve data acquisition and analysis, making the data crunching processes more efficient and meaningful. Likewise, data treatment and sharing are cornerstones of the project, as Open Science practices and scientific collaboration with the community are recognized by the consortium as key aspects of the BIG-MAP objectives of the Batteries partnership and 2030+ programmes.
The project is progressing very well and on track with the main goals. However, the work is still in progress. We expect to present more specific details of the results and how they go beyond the state of the art in our next report.