Using the Charge-Discharge Cycling of Positive Electrode Symmetric Cells to Find Electrolyte/Electrode Combinations with Minimum Reactivity
Bibliographic record
Abstract
The effects of solvents, salts, electrolyte additives and surface coatings on LiNi 0.4 Mn 0.4 Co 0.2 O 2 (NMC442) or LiNi 0.6 Mn 0.2 Co 0.2 O 2 (NMC622) have been probed using positive electrode Li-ion symmetric cells coupled with dV/dQ analysis. A robust symmetric cell design is presented which prevents hardware corrosion for over at least 800 hours of testing to 4.5 V vs. Li/Li + at 40°C. Positive electrode symmetric cells using uncoated positive electrode materials and 1M LiPF 6 EC:EMC 3:7 or 1M LiPF 6 EMC electrolyte rapidly developed high impedance and showed poor capacity retention. However, if 1% pyridine boron trifluoride (PBF) was added to these electrolytes, cell performance was dramatically improved. Replacing LiPF 6 by LiBF 4 in the electrolytes above, with or without PBF, yielded positive electrode symmetric cells with good capacity retention. Two types of surface coatings were explored on NMC622 positive electrodes. Cells using surface-coated positive electrodes demonstrated better capacity retention for all electrolytes compared to cells without surface coatings. This work can be used as a guide by those attempting to find electrolyte/electrode pairs suitable for use in NMC/graphite cells that can operate with long lifetime to 4.5 V.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 0.001 |
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".