Impedance Reducing Additives and Their Effect on Cell Performance
Bibliographic record
Abstract
Wound LiCoO 2 /graphite and Li[Ni 0.42 Mn 0.42 Co 0.16 ]O 2 (NMC)/graphite cells with 1 M LiPF 6 in EC:EMC (3:7 by wt) electrolyte containing 2 wt% vinylene carbonate (VC) and/or 0.3 wt% trimethoxyboroxine (TMOBX) were studied using the High Precision Charger at Dalhousie University, automated cycling/storage, AC impedance and long-term cycling at elevated temperature. The additive, VC, improves lifetime performance by increasing coulombic efficiency and decreasing charge and discharge end point capacity slippage. The impact of TMOBX on cell performance depends on electrolyte and electrode choices within the cell. When TMOBX is added to control electrolyte in LiCoO 2 /graphite cells, the cycle life is improved and the impedance reduced. When added to VC-containing electrolyte the impedance is also reduced with the impact on lifetime not being clear at this time. When TMOBX is added to NMC/graphite cells with control electrolyte the impedance is decreased but when added to VC-containing electrolyte the impedance is unchanged. The impact on cycle life when TMOBX is added to NMC/graphite cells is a decrease in capacity fade but a lower measured coulombic efficiency. Therefore TMOBX appears to be an interesting additive with potential to decrease impedance and/or extend cycle life based on the electrolyte formulation and electrodes used in a Li-ion cell.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".