Recovery of cathode materials from spent lithium-ion batteries using eutectic system of lithium compounds
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We developed a first-principles machine learning model for the reactive vapor–liquid phase behavior of molten Li 2 CO 3 . The model was trained on ab initio electronic density functional theory data using the Deep Potential (DP) methodology, and its accuracy was evaluated by comparing model predictions of density and viscosity to experimental measurements. Direct coexistence simulations with the DP model over time scales of tens of nanoseconds were used to observe equilibrium dissociation of Li 2 CO 3 into CO 2 residing primarily in the vapor phase and Li 2 O which remains dissolved in the liquid. The simulations covered a range of temperatures, overall system sizes, and vapor-to-liquid volume ratios. Results were analyzed in terms of the observed chemical composition of the liquid and vapor phases, product structure, and CO 2 partial pressures. In addition, we calculated equilibrium constants for the dissociation reaction by assuming ideal-solution behavior for the liquid. As expected on the basis of thermodynamic arguments and prior experiments for this system, the observed partial pressure of CO 2 in the gas phase depends on both the temperature and the ratio of vapor to liquid volumes, while the calculated equilibrium constants only depend on temperature. DP model predictions for the equilibrium constant of the reaction are generally consistent with the available experimental measurements. Furthermore, the present study establishes the validity of the DP methodology for the description of reactive, multiphase equilibria from first principles, with possible applications to many other systems of scientific and technological interest even in the absence of relevant experimental measurements.
Water and ion dynamics in concentrated LiCl solutions were studied using ultrafast 2D IR spectroscopy with the methyl thiocyanate (MeSCN) CN stretch as the vibrational probe. The IR absorption spectrum of MeSCN has two peaks, one peak for water associated with the nitrogen lone pair of MeSCN (W) and the other peak corresponding to Li + associated with the lone pair (L). To extract the spectral diffusion (structural dynamics) of W and L species, we developed a method that isolates the peak of interest by subtracting the 2D Gaussian proxies of multiple interfering peaks. Center line slope data (normalized frequency–frequency correlation function) for 2D bands from the W and L are fit with triexponential functions. The fastest component (1.1–1.6 ps) is assigned to local hydrogen bond length fluctuations. The intermediate timescale (~4.0 ps) corresponds to the hydrogen bond network rearrangement. The slowest component decays in ~40 ps and corresponds to ion pair and ion cluster dynamics. The very similar W and L spectral diffusion indicates that the motions of the water and ions are strongly coupled. Orientational relaxations of the W and L species were extracted using a new method to eliminate the effects of overlapping peaks. The results show that MeSCN bound to water undergoes orientational relaxation significantly faster than MeSCN bound to Li + . The orientational and spectral diffusion results are compared. In conclusion, a Stark coupling model is used to extract the root mean square average electric field caused by the ion clouds along the CN moiety as a function of concentration.
Localized high concentration electrolyte (LHCE) combines a diluent with high concentration electrolyte (HCE), offering promising properties. The ions, solvent, and diluent interact to form a complex heterogenous liquid structure, where high salt concentration clusters are embedded in diluent. Optimizing LHCE for desired electrolyte properties like high ionic conductivity, low viscosity, effective solid electrolyte interphase (SEI) formability, within the vast chemical and compositional design space requires deeper understanding and theoretical guidance. For this work, we investigated the structures and conductivity of LHCEs based on a fluorinated solvent with two different diluents at varying concentrations. The 2,2,3,3-tetrafluoropropyl trifluoroacetate (TFPTFA) enters the solvation cluster due to its stronger Li-ion interactions, whereas 1,1,2,2-tetrafuoroethyl 2,2,2-trifuoroethyl ether (TFETFE) enters only at extremely high diluent concentrations. The ionic conductivity increases with decreasing diluent concentrations, with a slope change during cluster percolation. Overall, TFETFE demonstrates higher effectiveness than TFPTFA, forming higher local salt concentration clusters, and resulting in higher ionic conductivity.
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