Cascading Degradations Artificially Improving the Lifetime of Li-ion Full Cells using DMC-based Highly Concentrated Electrolyte
Bibliographic record
Abstract
The deployment of energy dense Ni-rich NMC (LiNi x Mn y Co z O 2 with x > 0.8) in Li-ion batteries is hampered by a poor interfacial stability above 4.2 V. Among the strategies to mitigate this instability, highly concentrated electrolytes (HCE) have shown a promising resilience at high potential. In this work, we demonstrate that although cells using HCE experience low capacity fading compared to conventional carbonate based-electrolyte, HCE does not prevent oxidation of dimethylcarbonate (DMC) at high potential. Even worse, this phenomenon cannot be fully offset by lithium intercalation at the negative electrode and eventually leads to lithium plating that precipitates the cell end of life. To circumvent lithium plating, cycling at high temperature is shown to build a more passivating solid electrolyte interphase (SEI); while promising at first, the lithium losses associated with the SEI formation trigger a jump of graphite staging. Only replacing DMC by ethyl carbonate (EC) solvent reduces efficiently the parasitic oxidation and prevents capacity rollover. This work, by the use of adapted testing protocols and analysis workflows, provides the necessary understanding to open new routes for tackling parasitic reaction at high voltage in Li-ion batteries, which including mastering of SEI formation conditions and the use of appropriate solvent.
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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 | 0.000 |
| Research integrity | 0.000 | 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".