Multi-Particle Model for a Commercial Blended Lithium-Ion Electrode
Bibliographic record
Abstract
A mathematical model is presented to describe the electrochemical performance of a LiNi 1/3 Mn 1/3 Co 1/3 O 2 −LiMn 2 O 4 (NMC-LMO) blended cathode obtained from a commercial lithium-ion battery. The model accounts for the multiple particle sizes of the active materials in terms of three distributions: one for LMO particles, one for NMC primary and one for NMC secondary particles which likely are agglomerates of primary particles. The good match between the simulated and experimental galvanostatic discharge and differential-capacity curves supports the assumption that the secondary particles are nonporous under conditions where currents of 2C and below are applied. A thermodynamically consistent equation for diffusive flux is used to describe transport across the active particles. The corresponding thermodynamic factors are estimated from the equilibrium potentials of the active materials present in the electrode, while the particle size distribution and effective electronic conductivities of each component have been directly measured. Since the model is able to accurately describe the utilization of the various particle sizes and determine the contribution of each component at different discharge rates, it can serve as a useful tool for customizing the designs and predicting the discharge profiles of electrode blends made up of different active materials having a range of particle sizes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".