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Structure and dynamics of dynamic covalent cross-linked PEOs and PEO/LiPF 6 electrolytes: a coarse-grained simulation study.

Tongfei WuPing Zhang
Published in: Physical chemistry chemical physics : PCCP (2023)
The incorporation of dynamic covalent bonds has been an attractive strategy to synthesize adaptive solid polymer electrolytes (SPEs). Here, we present molecular dynamics results concerning the relationship between ion transport and segmental dynamics for dynamic covalent cross-linked PEO-Li + SPEs. To dissolve LiPF 6 into PEO, a 1/ r 4 -form approximation of ion-dipole interactions is employed as the solvation potential. Its parameters are estimated with the assistance of the Bayesian optimization algorithm and validated by comparing the resulting behaviors of PEO/LiPF 6 with experimental observations. The dynamic associations of EO with Li + and PF 6 - significantly reduce the segmental mobility of PEO, verifying the coupling of PEO segmental dynamics with ion transport. In order to reproduce the unique behaviors of associative covalent adaptive networks (CANs), the bond-exchange reaction is controlled by the collision probability and the user-defined activation energy ( E a ≥ 0) based on a hybrid of molecular dynamics and Monte Carlo methods. The dynamics of network topology, facilitated by the reshuffling of dynamic covalent bonds, is analyzed using graph theory. The network mesh size varies with time, which can be considered as one of the characteristics for associative CANs. The reshuffling of dynamic bonds releases the constraint from cross-linked structures, and enhances the long-range segmental mobility as well as the mobilities of Li + and PF 6 - . By drawing comparisons with its conventionally cross-linked counterpart, the effect of dynamic-bond reshuffling on ion transport is studied for the dynamic covalent cross-linked PEO 16 -LiPF 6 electrolyte in terms of self-diffusivities, cation transference number, and ionic conductivity.
Keyphrases
  • molecular dynamics
  • ionic liquid
  • solid state
  • density functional theory
  • mass spectrometry
  • convolutional neural network