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Optimization of Fluorinated Ether-Based Quasi-Solid Electrolyte Systems for Lithium-Sulfur Batteries
Ishani Senevirathna1, Changlong Chen1,2, Junquan Ou2
1Department of Physics, Illinois Institute of Technology, Chicago, Illinois 60616, United States.
ACS Applied Energy Materials
|March 27, 2026
Summary
Researchers optimized quasi-solid-state lithium-sulfur battery electrolytes using statistical modeling. The new formulation significantly improved cycling stability and discharge capacity, paving the way for safer, high-energy storage solutions.
Area of Science:
- Materials Science
- Electrochemistry
- Chemical Engineering
Background:
- Quasi-solid-state lithium-sulfur (Li-S) batteries offer high energy density but face challenges in electrolyte optimization.
- Complex interactions between ion transport, stability, and sulfur utilization hinder performance.
- Developing stable and efficient electrolytes is crucial for advancing Li-S battery technology.
Purpose of the Study:
- To systematically investigate quasi-solid electrolyte formulations for Li-S batteries.
- To utilize statistical modeling to map composition-performance relationships.
- To identify an optimized electrolyte composition for enhanced battery performance.
Main Methods:
- Investigated seven quasi-solid electrolyte formulations based on 1,3-dioxolane (DOL), octafluoropentyl tetrafluoroethyl ether (OTE), and dimethoxyethane (DME).
- Employed a modified mixture design based on design of experiments (DoE) principles.
- Utilized Gaussian process regression (GPR) for data-driven modeling of composition-performance trends.
- Formed electrolytes via in situ polymerization for mechanical stability and electrode contact.
Main Results:
- A Gaussian process regression model successfully predicted performance based on electrolyte composition.
- An optimized electrolyte composition (DOL:OTE:DME = 0.273:0.505:0.222) was identified.
- The optimized electrolyte achieved a high initial discharge capacity of 861 mAh g⁻¹ at 0.3 C.
- Demonstrated improved cycling stability with only 9.2% capacity loss over 100 cycles compared to the baseline.
Conclusions:
- Statistical modeling, specifically GPR, provides a powerful framework for optimizing multicomponent electrolyte systems.
- The identified electrolyte composition significantly enhances the performance and stability of quasi-solid-state Li-S batteries.
- This approach offers valuable insights into composition-performance relationships for future electrolyte development.
