Impact of a Titanium-Based Surface Coating Applied to Li[Ni<sub>0.5</sub>Mn<sub>0.3</sub>Co<sub>0.2</sub>]O<sub>2</sub> on Lithium-Ion Cell Performance
Bibliographic record
Abstract
The effect of a Ti-based surface coating on Li[Ni 0.5 Mn 0.3 Co 0.2 ]O 2 (NMC532) positive electrode material in NMC532/graphite Li-ion pouch cells has been investigated using high temperature storage testing (60 °C), ultrahigh precision coulometry, electrochemical impedance spectroscopy, accelerating rate calorimetry, and long-term cycling. Several superior electrolyte additive combinations were selected for this study. Comparing data from cells containing coated and uncoated NMC532 showed that the surface coating generally contributed to improved cell performance from many perspectives; however, for one electrolyte additive combination, cells containing coated and uncoated NMC532 had virtually identical excellent performance. In an effort to understand why the coating was effective, X-ray photoelectron spectroscopy was used to study the solid electrolyte interphases on both coated and uncoated NMC532. X-ray fluorescence studies of negative electrodes harvested from aged cells showed that the coating helped to prevent transition metal dissolution, although the amounts of metal dissolved were very small from both coated and uncoated NMC532. The “pouch bag” method was also used to study the effect of interactions between delithiated NMC532 (coated or uncoated) and lithiated graphite on gas evolution and impedance growth.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".