Designing Positive/Positive and Negative/Negative Symmetric Cells with Electrodes Operating in the Same Potential Ranges as Electrodes in a Full Li-Ion Cell
Bibliographic record
Abstract
This work shows how to design and build positive/positive (+/+) and negative/negative (−/−) symmetric cells with electrodes operating in the same potential ranges (vs Li/Li + ) as those in a full Li-ion cell. When this is achieved, better understanding of full cell degradation can be obtained. This method uses only coin cells that are ubiquitous in lithium-ion cell research instead of novel electrochemical devices that are more or less unique to specific research groups and are difficult to access. Using this method, the capacity retention and impedance growth of single crystal LiNi 0.5 Mn 0.3 Co 0.2 O 2 (SC532)/artificial graphite full coin cells were shown to lie between those of the +/+ and −/− symmetric cells, regardless of electrolyte additive and surface coating. Among all the cells, the +/+ cells have the worst capacity retention and highest impedance growth. Simultaneously cycled full coin cells and symmetric cells demonstrate the beneficial effect of “cross-talk” between the SC532 and the graphite electrodes to lower full cell impedance growth. Additionally, symmetric cell results show that 2% of vinylene carbonate (VC) increases the negative impedance more than 1% of lithium diflurophosphate (LFO), and that 1%LFO is also a better additive than VC to inhibit positive electrode impedance growth with coating.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".