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Determining the Transport Properties of Electrolyte Solutions By in-Situ NMR Imaging and Inverse Modeling

Wednesday, October 14, 2015
West Hall 1 (Phoenix Convention Center)
I. C. Halalay (General Motors Global Research & Development), A. K. Sethurajan, B. Protas (Mc Master University), S. Krachkovskiy (McMaster University), and G. Goward (McMaster University)
We used NMR imaging (MRI) combined with a data analysis by modeling of the inverse mass transport problem, to determine salt diffusion coefficients D+ and transference numbers t+ in electrolyte solutions of interest for Li-ion batteries. Sensitivity analyses have shown that accurate estimates of these parameters (as a function of concentration) are critical to the reliability of the predictions provided by models of porous electrodes. The inverse modeling (IM) solution was generated with an extension of the Planck-Nernst model for the transport of ionic species in electrolyte solutions. Concentration dependent diffusion coefficients and transference numbers were derived using concentration profiles obtained from in-situ 19F MRI measurements. Material properties were reconstructed with minimal assumptions, using methods of variational optimization to minimize the least-square deviation between experimental and simulated concentration values. Diffusion coefficients obtained by pulsed field gradient NMR (PFG NMR) fall within the 95% confidence bounds for the diffusion coefficient values obtained by the MRI+IM method. This demonstrates that PFG NMR determines chemical (Fickian) diffusion coefficients in concentrated electrolyte solutions and not self-diffusion coefficients. The MRI+IM method also yields the concentration dependence of the Li+transference number in agreement with trends obtained by electrochemical methods for similar systems and with predictions of theoretical models for concentrated electrolyte solutions, in marked contrast to the salt concentration dependence of transport numbers determined from PFG NMR data.

Acknowledgements

The authors acknowledge funding through the NSERC APC program and GM of Canada.