Impact of Salts and Linear Carbonates on the Performance of Layered Oxide/Hard Carbon Sodium-Ion Pouch Cells with Alkyl Carbonate Electrolytes
Bibliographic record
Abstract
This study examines the influence of electrolyte salts and solvents on the performance of O3 layered oxide NaMn0.39Fe0.31Ni0.22Zn0.08O2/hard carbon sodium-ion pouch cells with polyethylene terephthalate (PET) jellyroll tape. A significant enhancement in cell performance between 2.0 and 3.8 V was observed across various temperatures (20, 40, and 55 °C) by substituting NaPF6 with NaFSI, including reduced impedance growth, minimized gas generation, and supressed jellyroll tape decomposition. Ultra-high precision coulometry revealed that the use of NaPF6 resulted in increased unwanted parasitic reactions associated with tape decomposition, e.g., capacity fade and charge endpoint capacity slippage. Teardown of sodium-ion pouch cells after cycling in DMC-based electrolytes revealed a severe decomposition of the PET tape with NaPF6 but not with NaFSI. Gas chromatography shows significantly more electrolyte decomposition products with NaPF6 as opposed to NaFSI. DEC-based electrolyte showed less capacity fade, less electrolyte decomposition products, and less tape decomposition after cycling than DMC-based electrolyte. The electrolyte additive DTD can prevent parasitic reactions in DMC- and NaPF6-based electrolyte. Overall, the choice of salts and linear carbonates in alkyl carbonate electrolytes plays a crucial role in determining the overall cycling performance of the layered oxide/hard carbon sodium-ion cells with PET jellyroll tape.
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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 | 0.000 | 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".