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Using data-science approaches to unravel insights for enhanced transport of lithium ions in single-ion conducting polymer electrolyte

  • Zhu, Qinyu | Oak Ridge National Laboratory
  • Liu, Yifan | Oak Ridge National Laboratory
  • Shepard, Lauren B. | The Pennsylvania State University
  • Bhattacharya, Debjyoti | The Pennsylvania State University
  • Sinnott, Susan B. | The Pennsylvania State University
  • Reinhart, Wesley F. | The Pennsylvania State University
  • Cooper, Valentino R. | Oak Ridge National Laboratory
  • Kumar, Rajeev | Oak Ridge National Laboratory
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Overview

Description

Solid polymer electrolytes have yet to achieve the an ionic conductivity > 1 mS/cm at room temperature for realistic applications. This target implies the need to reduce the effective energy barriers of ion transport in polymer electrolytes to around 20 kJ/mol. In this work, we combine information extracted from existing experimental results with theoretical calculations to provide insights into ion transport in single-ion conductors (SICs) with a focus on lithium ion SICs. Through the analysis of temperature-dependent ionic conductivity data obtained from the literature, we evaluate different methods of extracting energy barriers for lithium transport. The traditional Arrhenius fit to the temperature-dependent ionic conductivity data indicates that the Meyer-Neldel rule holds for SICs. However, the values of the fitting parameters remain unphysical. Our modified approach based on recent work (Macromolecules, 56, 15, 6051(2023)), which incorporates a fixed pre-exponential factor, reveals that the energy barriers exhibit temperature dependence over a wide range of temperatures. Using this approach, we identify a series of anions leading to the energy barriers less than 30 kJ/mol, which include trifluoromethane sulfonimide (TFSI), fluoromethane sulfonimide (FSI), and boron-based organic anions. In our efforts to design the next generation of anions, which can exhibit the energy barriers less than 20 kJ/mol, we focused on boron-containing SICs, and performed density functional theory (DFT) based calculations to connect the chemical structures via the binding energy of cation (lithium)-anion pairs with the experimentally derived effective energy barriers for ion transport. Not only have we identified a correlation between the binding energy and the energy barriers, but we also propose a strategy to design new boron-based anions by using the correlation. This combined approach involving experiments and theoretical calculations is capable of facilitating the identification of promising new anions, which can exhibit ionic conductivity $> 1$ mS/cm near room temperature, thereby expediting the development of novel superionic single-ion conducting polymer electrolytes. The published datasets include all the temperature-dependent ionic conductivity collected from the literature with literature DOIs, DFT calculated binding energies, and python scripts to analyze data, construct statistical models, and generate plots.

Funding resources

DOE contract number

DE-AC05-00OR22725

Originating research organization

Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring organization

Office of Science (SC), Basic Energy Sciences (BES) (SC-22)

Details

DOI

10.13139/OLCF/2441479

Release date

October 30, 2024

Dataset

Dataset type

SM Specialized Mix

Software

Python

Other contract number(s)

National Energy Research Scientific Computing Center (NERSC) award BES-ERCAPm4305

Acknowledgements

Users should acknowledge the OLCF in all publications and presentations that speak to work performed on OLCF resources:

This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725.

Category

  • 36 MATERIALS SCIENCE,
  • 74 ATOMIC AND MOLECULAR PHYSICS,
  • 97 MATHEMATICS AND COMPUTING

Keywords

  • Single Ion Conducting Polymer,
  • Energy Barrier,
  • DFT Calculation,
  • Statistical Model