Controlling Binder Adhesion to Impact Electrode Mesostructures and Transport

Abstract

The complex three-phase composition of lithium-ion battery electrodes-containing an ion-conducting pore phase, a nanoporous electron-conducting carbon binder domain (CBD) phase, and an active material (AM) phase-provides several avenues of mesostructural engineering to enhance battery performance. We demonstrate a promising strategy for engineering electrode mesostructures by controlling the strength of adhesion between the AM and CBD phases. Using high-fidelity, physics-based colloidal and granular dynamics simulations, we predict that this strategy can provide significant control over electrochemical transport-relevant properties such as ionic conductivity, electronic conductivity, and available AM surface area. Importantly, the proposed strategy could be experimentally realized through surface functionalization of the AM and CBD phases and would be compatible with traditional electrode manufacturing methods.

Towards a 3D-resolved model of Si/Graphite composite electrodes from manufacturing simulations

“…For instance, the model has the potential to be improved by fitting FF parameters to reproduce experimental physical properties. Additionally, DEM methods to simulate electrode calendering could be easily included in the workflow [29,32]. Summing up, Figure 1 shows the overall workflow used to generate the graphite/Si composite microstructures.…”

“…In these regards, computational 3D modelling has been proven to be an efficient tool to better understand battery phenomena at the mesoscale [23][24][25][26][27][28][29], where relationships between the electrode microstructure and performance can be elucidated. Concerning the generation of 3D electrode microstructural models, we can distinguish between two main approaches, each with their own advantages and disadvantages: (i) stochastic generation, where particles are randomly placed or piled on a simulation box until a certain target is met (porosity, particle number, etc.)…”

“…Concerning the generation of 3D electrode microstructural models, we can distinguish between two main approaches, each with their own advantages and disadvantages: (i) stochastic generation, where particles are randomly placed or piled on a simulation box until a certain target is met (porosity, particle number, etc.) [30,31] and (ii) physics-based simulations, such as Coarse-Grained Molecular Dynamics (CGMD) [32][33][34] and Discrete Element Method (DEM) [29,32,[35][36][37]. A recently developed third option consists of generating realistic 3D electrode microstructures by generative adversarial networks (GANs) [38,39].…”

Probing the Influence of Multiscale Heterogeneity on Effective Properties of Graphite Electrodes

“…The current state-of-the-art electrochemical models use XCT imaging for their geometry; however, XCT cannot discern the CBD phase due to low atomic numbers. Thus this domain is often reconstructed artificially using synthetic models 26,[39][40][41][42] . The addition of CBD impacts effective properties in cathode materials due to the high volume being added 26,43 .…”

A Minimal Information Set To Enable Verifiable Theoretical Battery Research

“…Patel & Nelson (10.1115/1.4045820) 24 Y NA NA NA Y Sripad et al (10.1063/5.0023771) 25 N NA NA N Y Srivastava et al (10.1021/acsami.0c082513) 26 Y Y NA Y Y Subramanian, Ploehn & White (10.1149/1.1393618) 27 Y NA NA N Y Timms et al (10.1137/20M1336898) 28 Y NA NA Y Y Tong et al (10.1021/acs.est.1c01273) 29 Y NA NA N Y Verma et al (10.1149/2.1701713jes) 30 N NA NA N Y Verma et al ( Remarks: Most equations and boundary conditions (for the SPMe) are given, but some are not (e.g. OCV curve) since they are directly from another paper, the reader is referred to that instead.…”