The drying process is a crucial step in electrode manufacture that may lead to spatial inhomogeneities in the distribution of the electrode components resulting in impaired cell performance. Binder migration during the drying process, and the ensuing poor binder coverage in certain regions of the electrode, can lead to capacity fade and mechanical failure (e.g. electrode delamination from the current collector). A mathematical model of electrode drying is presented which tracks the evolution of the binder distribution, and is applicable in the relatively high drying rates encountered in industrial electrode manufacture. The model predicts that constant low drying rates lead to a favourable homogeneous binder profiles, whereas constant high drying rates are unfavourable and result in accumulation of binder near the evaporation surface and depletion near the current collector. These results show strong qualitative agreement with experimental observations and provide a cogent explanation for why fast drying conditions result in poorly performing electrodes. Finally, a scheme is detailed for optimisation of a time-varying drying procedure that allows for short drying times whilst simultaneously ensuring a close to homogeneous binder distribution throughout the electrode.

We used NMR imaging (MRI) combined with data analysis based on inverse modeling of the mass transport problem to determine ionic diffusion coefficients and transference numbers 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 (19)F MRI measurements. Material properties were reconstructed under minimal assumptions using methods of variational optimization to minimize the least-squares deviation between experimental and simulated concentration values with uncertainty of the reconstructions quantified using a Monte Carlo analysis. The 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. 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.

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