Understanding Zn Electrodeposits Morphology in Secondary Batteries Using Multiscale Computational Approach

Zinc (Zn) aqueous rechargeable batteries (ZBs) have shown a tremendous success in various applications due to their environmental friendliness, multi-electron capacity, high abundance, safety and low cost; however, analogous to their Li-ion battery counterparts, they suffer from the dendrite formation leading to decrease in capacity and eventual failure. Despite many studies reporting a good performance and partial dendrites suppression1, there have been no systematic studies of Zn electrodeposits morphology analysis in ZBs. In this contribution, we present the results of a combined density functional theory (DFT) calculations of Zn diffusion through the ZnO surface layer (i.e., solid electrolyte interface – SEI) and the phase-field modeling (PFM) of Zn electrodeposition study. The DFT calculations are performed using commercial code VASP (Vienna Ab initio Simulation Package)2 with the projector augmented wave (PAW)3 Perdew-Burke-Ernzerhof (PBE)4 pseudo-potentials within generalized gradient approximation (GGA)5. The Zn path and migration barriers are evaluated using the Nudged Elastic Band (NEB) method. The PFM6,7 is developed in the open source code MOOSE and is formulated in continuum theory based upon diffusion-conservation laws using partial-differential equations and then discretized using finite element method8 and solved computationally. The DFT calculations are performed to reveal the dominant diffusion pathways, energetics and the corresponding diffusion coefficients associated with Zn diffusion through the SEI. The SEI surface layer on Zn electrode in ZBs primarily consists of ZnO. Correspondingly, two cases are considered, i.e. the Zn diffusion through the ZnO grain boundary (GB) formed between the different ZnO surface orientations and the Zn diffusion through the ZnO bulk grains. It is found that Zn diffusion occurs through the different nano-channels inside grains as well as through the GB. The path and the activation energies of Zn diffusion vary significantly depending upon the structure of grains and GB, as well as the presence of additional Zn atoms corresponding to the current density dependency. However, the general trend is that Zn diffusion in a grain is faster comparing to the ZnO/ZnO GB. Using the DFT calculations the appropriate elastic properties of SEI components are calculated as required by the PFM. The 2D PFM model predicts the Zn morphologies, the Zn ions concentration, the electrostatic potential, the stress and the equivalent plastic strain. Three regimes are investigated, specifically, low, intermediate and high current densities, where different Zn morphologies, such as boulders, mossy and dendritic shape are observed. It is found that the stress has a major influence on the electrodeposition, and vice versa the Zn electrodeposits growth is found to affect the stress distribution significantly. The plastic yielding occurs preferentially at the node of the boulders and through the filaments and mossy structures. In addition, based upon the present PFM results alternative strategies for the inclusion of the stress field in the mean-field electrochemical modeling will be discussed. Furthermore, both the DFT and the PFM results are supported by scanning electron (SEM) and scanning transmission electron microscopy (STEM) to reveal micro and nano-scopic details of Zn electrodeposits as well as the surface ZnO layer. The combined results also provide a basis for development and design of novel strategies against unwanted Zn dendrites formation. References 1. T. Foroozan, V. Yurkiv, S. Sharifi-Asl, R. Rojaee, F. Mashayek, R. S.-Y. Non-Dendritic Zn Electrodeposition Enabled by Zincophilic Graphene Substrates. ACS Appl. Mater. Interfaces 2019, (doi.org/10.1021/acsami.9b13174) 2. Kresse, G.; Hafner, J. Ab Initio Molecular Dynamics for Liquid Metals. Phys. Rev. B 1993, 47, 558–561. 3. Blöchl, P. E. Projector Augmented-Wave Method. Phys. Rev. B 1994, 50, 17953–17979. 4. Perdew, J. P.; Ruzsinszky, A.; Csonka, G. I.; Vydrov, O. A.; Scuseria, G. E.; Constantin, L. A.; Zhou, X.; Burke, K. Restoring the Density-Gradient Expansion for Exchange in Solids and Surfaces. Phys. Rev. Lett. 2007, 100, 136406. 5. Perdew, J. P.; Burke, K.; Ernzerhof, M. Generalized Gradient Approximation Made Simple. Phys. Rev. Lett. 1996, 77, 3865–3868. 6. Yurkiv, V.; Foroozan, T.; Ramasubramanian, A.; Shahbazian-Yassar, R.; Mashayek, F. Phase-Field Modeling of Solid Electrolyte Interface (SEI) Influence on Li Dendritic Behavior. Electrochim. Acta 2018, 265. 7. Yurkiv, V.; Foroozan, T.; Ramasubramanian, A.; Shahbazian-Yassar, R.; Mashayek, F. The Influence of Stress Field on Li Electrodeposition in Li-Metal Battery. MRS Commun. 2018, 8, 1285–1291. 8. Tonks, M. R.; Gaston, D.; Millett, P. C.; Andrs, D.; Talbot, P. An Object-Oriented Finite Element Framework for Multiphysics Phase Field Simulations. Comput. Mater. Sci. 2012, 51, 20–29.