NSF-DFG MISSION: Surface analysis & Correlative imaging using Operando Secondary Ion Mass Spectrometry (SCOPE-SIMS)

This project was in response to a NSF-DFG German Research Foundation partnership. The partners are Rice University and Justus Liebig University Giessen in Germany. This project aims to advance fundamental understanding of the chemical processes occurring at the interfaces within next-generation all-solid-state batteries.

These promising battery technologies have the potential for higher energy densities and enhanced safety compared to conventional lithium-ion batteries with organic liquid electrolytes. However, their performance is critically dependent on the properties of the solid-state electrolyte and its interfaces with the electrodes. By developing cutting-edge surface analysis methods to study these interfacial regions during battery cycling, this research project will provide unprecedented molecular-level insights into the degradation mechanisms that currently limit solid-state battery lifetimes.

These scientific advances will help guide the rational design of optimized materials and architectures to enable widespread commercialization for applications ranging from electric vehicles to grid storage. The project fosters international research collaboration, provides interdisciplinary training for the next generation of scientists/engineers, and engages underrepresented groups in energy research through targeted outreach efforts.

This project will develop and apply advanced time-of-flight secondary ion mass spectrometry (ToF-SIMS) methods to characterize solid-solid interfaces in all solid-state battery materials during operando electrochemical cycling. The focus is on a NMC (nickel, manganese, cobalt) cathode, sulfide-based electrolyte, and a silicon anode. Integrated electrochemical cells will be designed to enable operando ToF-SIMS mapping of the evolving solid-electrolyte interphase chemistry.

Labeling studies will probe lithium transport pathways. Benchmarking will be performed against post-mortem analysis and molecular dynamics simulations. Machine learning algorithms will be applied to correlate the ToF-SIMS spectral features to degradation processes and capacity fade.

This combined experimental and computational approach provides a multi-scale view spanning from molecular mechanisms to bulk performance losses. Expected outcomes include elucidating the impact of surface coatings and binders, particle sizes, current densities and other factors on interfacial stability. This foundational knowledge will guide strategies like interfacial engineering to mitigate issues like dendrite growth, mechanical cracking, and resistive film formation.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.