U.S. Department of EnergyOffice of Scientific and Technical Information
Using data-science approaches to unravel insights for enhanced transport of lithium ions in single-ion conducting polymer electrolyte
Abstract
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 lessmore »
- Authors:
-
- ORNL-OLCF
- Publication Date:
- DOE Contract Number:
- AC05-00OR22725
- Research Org.:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Org.:
- Office of Science (SC), Basic Energy Sciences (BES) (SC-22)
- Subject:
- 36 MATERIALS SCIENCE; 74 ATOMIC AND MOLECULAR PHYSICS; 97 MATHEMATICS AND COMPUTING; Single Ion Conducting Polymer, Energy Barrier, DFT Calculation, Statistical Model
- OSTI Identifier:
- 2441479
- DOI:
- https://doi.org/10.13139/OLCF/2441479
Citation Formats
Zhu, Qinyu, Liu, Yifan, Shepard, Lauren B., Bhattacharya, Debjyoti, Sinnott, Susan B., Reinhart, Wesley F., Cooper, Valentino R., and Kumar, Rajeev. Using data-science approaches to unravel insights for enhanced transport of lithium ions in single-ion conducting polymer electrolyte. United States: N. p., 2024.
Web. doi:10.13139/OLCF/2441479.
Zhu, Qinyu, Liu, Yifan, Shepard, Lauren B., Bhattacharya, Debjyoti, Sinnott, Susan B., Reinhart, Wesley F., Cooper, Valentino R., & Kumar, Rajeev. Using data-science approaches to unravel insights for enhanced transport of lithium ions in single-ion conducting polymer electrolyte. United States. doi:https://doi.org/10.13139/OLCF/2441479
Zhu, Qinyu, Liu, Yifan, Shepard, Lauren B., Bhattacharya, Debjyoti, Sinnott, Susan B., Reinhart, Wesley F., Cooper, Valentino R., and Kumar, Rajeev. 2024.
"Using data-science approaches to unravel insights for enhanced transport of lithium ions in single-ion conducting polymer electrolyte". United States. doi:https://doi.org/10.13139/OLCF/2441479. https://www.osti.gov/servlets/purl/2441479. Pub date:Wed Oct 30 00:00:00 EDT 2024
@article{osti_2441479,
title = {Using data-science approaches to unravel insights for enhanced transport of lithium ions in single-ion conducting polymer electrolyte},
author = {Zhu, Qinyu and Liu, Yifan and Shepard, Lauren B. and Bhattacharya, Debjyoti and Sinnott, Susan B. and Reinhart, Wesley F. and Cooper, Valentino R. and Kumar, Rajeev},
abstractNote = {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.},
doi = {10.13139/OLCF/2441479},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Wed Oct 30 00:00:00 EDT 2024},
month = {Wed Oct 30 00:00:00 EDT 2024}
}