Probing the Structure-Performance Relationship of Lithium-Ion Battery Cathodes Using Pore-Networks Extracted from Three-Phase Tomograms
Bibliographic record
Abstract
Pore-scale simulations of Li-ion battery electrodes were conducted using both pore-network modeling and direct numerical simulation. Ternary tomographic images of NMC811 cathodes were obtained and used to create the pore-scale computational domains. A novel network extraction method was developed to manage the extraction of N-phase networks which was used to extract all three phases of NMC-811 electrode along with their interconnections Pore network results compared favorably with direct numerical simulations (DNS) in terms of effective transport properties of each phase but were obtained in significantly less time. Simulations were then conducted with combined diffusion-reaction to simulate the limiting current behavior. It was found that when considering only ion and electron transport, the electrode structure could support current densities about 300 times higher than experimentally observed values. Additional case studies were conducted to illustrate the necessity of ternary images which allow separate consideration of carbon binder domain and active material. The results showed a 24.4% decrease in current density when the carbon binder was treated as a separate phase compared to lumping the CBD and active material into a single phase. The impact of nanoporosity in the carbon binder phase was also explored and found to enhance the reaction rate by 16.8% compared to solid binder. In addition, the developed technique used 58 times larger domain volume than DNS which opens up the possibility of modelling much larger tomographic data sets, enabling representative areas of typically inhomogeneous battery electrodes to be modelled accurately, and proposes a solution to the conflicting needs of high-resolution imaging and large volumes for image-based modelling. For the first time, three-phase pore network modelling of battery electrodes has been demonstrated and evaluated, opening the path towards a new modelling framework for lithium ion batteries.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".