Can Layered Oxide/Hard Carbon Sodium-Ion Pouch Cells with Simple Electrolyte Additives Achieve Better Cycle Life than LFP/Graphite Cells?
Bibliographic record
Abstract
This study explores the impact of simple electrolyte additives on the performance of layered oxide/hard carbon sodium-ion pouch cells. The cycle life of these cells between 2.0 and 3.8 V is assessed at various temperatures (20, 40, and 55 °C) with different solvent systems based on ethylene carbonate, diethyl carbonate, and dimethyl carbonate. A particular challenge in these cells is gas generation at high temperature. Pouch bag experiments which separate the charged electrodes to measure their gas generation from reactions with the electrolyte show that hard carbon generates no gas, but the sodium layered oxide produces large amounts of gas. Isothermal microcalorimetry corroborates these results with parasitic heat flow measurements of pouch bags and full pouch cells. A crosstalk mechanism is revealed which lowers gas generation and reduces parasitic heat flows in full cells. The electrolyte additives prop-1-ene-1,3-sultone, sodium difluorophosphate, and 1,3,2-dioxathiolane-2,2-dioxide (DTD) are effective at reducing gas generation and heat flow from the positive electrode. They also reduce self-discharge in elevated temperature storage tests. Overall, 1 M NaFSI in EC:DMC (15:85) with 2% DTD is the best electrolyte for the sodium-ion pouch cells in this work. Eventually, the performance of these cells is compared to optimized LiFePO4/graphite cells.
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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 | 0.001 | 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".