(Digital Presentation) Three-Dimensional Pore-Scale Modelling of NMC Cathodes Using Multi-Resolution FIB-SEM Images
Bibliographic record
Abstract
Lithium-ion battery (LIB) cathodes are porous electrodes made of active material (AM) that stores lithium, a composite of carbon additives and polymeric binder (CBD) that facilitates electron transport and ensures the mechanical integrity of the electrode, and electrolyte-filled pore space that facilitates lithium-ion transport [1]. The volume fraction and morphology of the different constituents at the microscale necessarily determine transport properties and influence the measurable performance [2]. In this work, the impact of NMC electrode microstructure on the effective transport properties is studied using FIB-SEM-based three-dimensional (3D) particle-resolved microscale simulations. The effect of AM and CBD bulk electronic conductivity on the effective electronic conductivity is first studied and used to highlight that the impact of the AM bulk conductivity is negligible compared to that of the CBD. Next, the impact of CBD volume fraction, in isolation from its morphology, is studied using morphological operations by eroding and dilating the CBD phase in the FIB-SEM images and analyzing its impact on the effective conductivity using multiple 3D reconstructions. Increasing the CBD volume fraction results in a nonlinear increase in the effective electronic conductivity. To study the effect of CBD morphology, two stochastic CBD reconstruction techniques are proposed. The first method places new CBD voxels preferentially next to existing CBD voxels, and the second method deposits the CBD randomly in the pore space. The effective electronic conductivity for microstructures containing stochastic CBD morphologies is calculated and compared to that evaluated for microstructures with eroded and dilated CBD. The CBD generated stochastically results in a predicted higher effective electronic conductivity primarily due to a lower CBD tortuosity when compared to the CBD generated with morphological operations. Finally, the impact of CBD porosity on electrode tortuosity is studied by estimating the pore-phase tortuosity considering a solid and a porous CBD. The diffusivity of the porous CBD is estimated using multi-resolution FIB-SEM images. Results show that not accounting for the CBD porosity increases the electrode tortuosity by a factor of up to three at low electrode porosities. References [1] B. L. Trembacki, A. N. Mistry, D. R. Noble, M. E. Ferraro, P. P. Mukherjee, S. A. Roberts, Mesoscale analysis of conductive binder domain morphology in lithium-ion battery electrodes, Journal of The Electrochemical Society 165 (13) (2018) E725–E736 [2] Xu, Hongyi, et al. ‘Guiding the Design of Heterogeneous Electrode Microstructures for Li‐Ion Batteries: Microscopic Imaging, Predictive Modeling, and Machine Learning’. Advanced Energy Materials, vol. 11, no. 19, May 2021, p. 2003908. DOI.org (Crossref), https://doi.org/10.1002/aenm.202003908. Figure 1
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.104 | 0.010 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".