Optimizing ionic transport in polymer(PEO)-Garnet(LLZO) composite solid-state electrolytes
Year: 2020
Type: preprint
Abstract: Polymer composite electrolytes (PCEs) are viable candidates to replace the flammable organic liquid electrolytes in current lithium-ion batteries. The addition of fillers in PCEs has been reported to suppress the crystalline fraction in the polymer phase and increments of their ionic conductivity are ascribed to enhanced degrees of segmental motion of the amorphous phase that facilitate ion transport. A comprehensive mechanism for ion transport in PCEs has yet to be developed, and it is not fully understood why the weight or volume load of filler particles added to PCEs, varies widely to achieve the maximum ionic conductivity in different material systems. This work investigates ionic transport in PCEs comprising Li6La3ZrBiO12 (Bi-LLZO) and Li6.25La3Zr1.25Bi0.75O12 (0.75Bi-LLZO) mesoparticles as fillers embedded in PEO: LiTFSI matrixes. The role of the Bi content in LLZO garnet particles on the PCE ionic conductivity, the polymer phase crystallinity and the PCE microstructure are investigated. We find that the Bi content of the garnet mesoparticles determines the weight load required to achieve the highest ionic conductivity in the PCEs studied in this work. The optimum weight loads for Bi-LLZO and 0.75Bi-LLZO particle additions were found to be 5% and 10% respectively. Polarized light microscopy observations of the polymer morphology reveal significant changes in spherulite size and density as a function of garnet particle weight load. The maximum IC is observed for morphologies exhibiting large interconnected spherulites. We conclude that the density of Li vacancies, controlled by the Bi content, in the garnet crystalline structure, dominates spherulite nucleation density and growth in the polymer matrix. The optimum particle weight load results in the formation of an interconnected network of lamellar amorphous regions which is responsible for the optimum ionic conductivity.
Source: arXiv (Cornell University)
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3080438189