In Silico Based Rank-Order Determination and Experiments on Nonaqueous Electrolytes for Sodium Ion Battery Applications
Electrolytes are an important component of electrochemical energy storage systems and their optimization is critical for emerging beyond lithium ion technologies. Here, an integrated computational-experimental approach is used to rank-order and aid the selection of suitable electrolytes for a Na-ion battery. We present an in silico strategy based on both thermodynamic and kinetic descriptors derived from molecular dynamics simulations to rationally arrive at optimal electrolytes for Na-ion batteries. We benchmarked various electrolytes (pure and binary mixtures of cyclic and acyclic carbonates with NaClO4 salt) to identify appropriate formulations with the overarching goal of simultaneously enhancing cell performance while meeting safety norms. Fundamental insights from computationally derived thermodynamic and kinetic data considerations coupled with atomistic-level description of the solvation dynamics is used to rank order the various electrolytes. Thermodynamic considerations based on free energy evaluation indicate EC:PC as a top electrolyte formulation under equilibrium conditions. However, kinetic descriptors which are important factors dictating the rate capability and power performance suggest EC:DMC and EC:EMC to be among the best formulations. Experimental verification of these optimized formulations was carried out by examining the electrochemical performance of various electrolytes in Na/TiO2nanotubes half cells with NaClO4 salt. Our rate capability studies confirm that EC:DMC and EC:EMC to be the best formulations. These optimized formulations have low-rate specific capacities 120–140 mAh/g whereas the lower ranked electrolytes (EC: DEC) have capacities 95 mAh/g. The various electrolytes are also evaluated from a safety perspective. Such results suggest encouraging prospects for this approach in the a priori prediction of optimal sodium ion systems with possible screening implications for novel battery formulations.
Cutler, Richard W.; Parrish, Riley; Huether, Joshua; Butt, Darryl P.; and Xiong, H.. (2014). "In Silico Based Rank-Order Determination and Experiments on Nonaqueous Electrolytes for Sodium Ion Battery Applications". The Journal of Physical Chemistry C, 118(25), 13406-13416. https://doi.org/10.1021/jp502319p