The electrode microstructure plays an integral role in the performance of the non-aqueous Li-air battery. Computational modeling has proven to be an indispensible tool in the analysis of battery systems, but previous macroscale, volume-averaged models that consider the porous electrode as a homogenous medium of uniform geometric properties are insufficient to probe the effect of precise electrode microstructures. Utilizing a pore-scale transport-resolved model of the Li-air battery, the complex electrode and Li 2 O 2 morphologies can be directly incorporated and their effects on the system-level performance can be evaluated. A thickness-dependent electrical conductivity of Li 2 O 2 is considered in the model based on inputs from the density functional theory. Model validation is presented along with a sensitivity study of the applied current density and the reaction rate coefficient. The effect of electrode geometry (e.g., nanostructure spacing and height) on cell performance, including its influence on pore blocking compared against electrical insulation, is investigated. Pore blocking is observed for cathodes with nanostructure spacing less than twice a critical insulating thickness of Li 2 O 2 , suggesting the loss of active surface area as the mechanism for decreased cell performance. While for cathodes with larger nanostructure spacing, the discharge capacity is dictated by the electrical insulation of Developing a battery with the energy density of fossil fuel has been an elusive goal that will likely usher in the widespread implementation of high-energy storage applications, such as electrified transportation 1 . Current Li-ion batteries (100 -480 Wh kg −1 practical 2-4 ) have only 5-28% of the specific energy of gasoline (1700 Wh kg −1 practical 5 ); in order to drive 300 miles, an electric car would need a 500 kg Liion battery.4 Alternatively, the non-aqueous Li-air (or more precisely Li-oxygen) batteries (1800 -2700 Wh kg −1 practical 6-10 ) have up to 5 times the energy density of Li-ion batteries, putting them well within the range of gasoline 1 . Several factors contribute to the high energy density of Li-air battery: (i) there is no heavy transition metal in the cell, 11 (ii) lithium is the lightest and most electronegative anode metal, and (iii) the gaseous reactant O 2 is not contained within the cell but instead can be drawn from the surroundings. However, the relatively new Li-air battery still underperforms in several key factors when compared to the more established Li-ion battery, such as low round trip efficiency (70% 9,12 vs. >95% 13 ), low drawing current density (0.1-1.0 mA cm −28,14 vs. 30 mA cm −214 ) and poor cycle life (∼100 15,16 vs. 5000 17 ). The source of these problems can be traced to a number of key issues, including the electric insulation of the reaction products, [18][19][20][21][22] parasitic irreversible side reactions, 23-26 electrolyte instability, 12,23,24 and electrode degradation. 12,27 These shortcomings must be overcome in order for the Li-air battery to become via...
The lithium-air (Li-air) battery, with its usable energy density close to 1,700Wh/kg1, has captured worldwide attention as a promising battery solution for electric vehicles. However, a major hurdle facing the development of Li-air battery systems is their poor round-trip efficiency owing to the formation of electrically insulating lithium peroxide (Li2O2) at the cathode surface. Computational modeling has proven to be an indispensable tool in the analysis of battery systems, however, all existing simulations thus far treat the electrode/Li2O2 matrix as a homogenous continuum and utilize simply-shaped electrode morphologies, such as spheres and rods, to construct volume-averaged expressions (see Fig. 1a) for porosity and surface area and are insufficient to probe the effect of precise electrode microstructures and Li2O2 growth2,3. Utilizing a pore-scale transport-resolved model of the Li-air battery, the complex electrode and Li2O2morphologies can be directly incorporated and their effects on the system-level performance can be evaluated. In this work, we present a pore-scale transport resolved model (see Fig. 1b) of the Li-air battery that fully accounts for the electrode microstructure and peroxide growth4. This approach requires no empirical correlations regarding the electrode morphology. 3D reconstructed images of real carbon fiber cathode (Fig. 2) are used as geometric inputs to the model. Results obtained from our pore-scale model agree well with experiments and the validated model is then used to predict the galvanostatic discharge behavior of a Li-air cell for a variety of electrode morphologies and design parameters. Fig. 3 shows the cell voltage versus specific capacity curves for nanofiber electrodes of varying mean pore spacing from our 3D pore-scale model. The cell discharge capacity is limited by the spacing between nanostructures, which may lead to pore blocking and hence the reduction of active surface area. To capture the different growth morphologies two potential peroxide forming reactions, based on an intermediate oxygen reduction reaction where O2 reduces to becomes superoxide (O2 -), are incorporated as shown in Fig. 1c. Superoxide may form at the surface of the electrode and then follow one of two possible paths; reaction at the electrode surface to form lithium peroxide or diffusion into the bulk liquid to nucleate lithium peroxide in the electrolyte5. Lithium peroxide created at the electrode/electrolyte surface will produce a thin film growth morphology while peroxide particles generated further away in the electrolyte contributes to the formation of larger particle shaped or toroidal shaped growth. The competition between the different reactions can be explicitly accounted for in the pore-scale modeling framework. A discrete element method is used to simulate the nucleation and movement of lithium peroxide. Through extensive 3D simulations, we systematically examine the effect of drawing current, ORR rate coefficients, oxygen solubility, mean pore size and distribution on Li2O2growth and cell performance as to better understand the underlying physics of capacity fading during cycling. The methodology presented here can be applied to other electrochemical systems that include an insoluble product formation as a result of the reaction process and will be a valuable tool for rational design of electrode microstructures for improved cell performance. References: [1] G. Girishkumar, B. McCloskey, A. C. Luntz, S. Swanson, and W. Wilcke, "Lithium−Air Battery: Promise and Challenges," The Journal of Physical Chemistry Letters, 1, 2193, (2010). [2] P. Andrei, J. P. Zheng, M. Hendrickson, and E. J. Plichta, "Some Possible Approaches for Improving the Energy Density of Li-Air Batteries," Journal of The Electrochemical Society, 157, A1287, (2010). [3] Y. Wang, "Modeling discharge deposit formation and its effect on lithium-air battery performance," Electrochimica Acta, 75, 239, (2012). [4] C. Andersen, H. Hu, G. Qiu, V. Kalra, and Y. Sun, "Pore-Scale Transport Resolved Model Incorporating Cathode Microstructure and Peroxide Growth in Lithium-Air Batteries," Journal of The Electrochemical Society, 162, A1135, (2015). [5] K. H. Xue, E. McTurk, L. Johnson, P. G. Bruce, and A. A. Franco, "A Comprehensive Model for Non-Aqueous Lithium Air Batteries Involving Different Reaction Mechanisms," Journal of The Electrochemical Society, 162, A614-A621 (2015). Figure 1
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