Impact of Sodium Metal Plating on Cycling Performance of Layered Oxide/Hard Carbon Sodium-ion Pouch Cells with Different Voltage Cut-offs
Bibliographic record
Abstract
This study investigates the cycling performance and failure mechanisms of sodium-ion pouch cells with layered NaCa 0.03 [Mn 0.39 Fe 0.31 Ni 0.22 Zn 0.08 ]O 2 positive electrodes and hard carbon negative electrodes. Charge/discharge cycling between different lower and upper cut-off voltages at C/5 and 40 °C showed better capacity retention, lower voltage polarization, and less gassing when the upper cut-off voltage was limited to 3.80 V. Electrodes harvested from pouch cells after cycling were reassembled in symmetric coin cells to reveal the origin of voltage polarization by electrochemical impedance spectroscopy. The negative electrode charge transfer resistance dominated the full cell impedance and increased considerably after 100 cycles at 40 °C with standard alkyl carbonate electrolyte. The positive electrode impedance was less significant but increased dramatically when the full cell voltage was 4.00 V. Furthermore, ultra-high precision coulometry used for the in situ detection of sodium plating at 40 °C, revealed significant plating at charging rates greater C/2. Based on this failure analysis, long-lived sodium-ion cells with 97% capacity retention after 450 cycles at 40 °C could be realized by selecting appropriate voltage cut-offs, C-rates, and effective electrolyte additives that lowered the cell resistance and suppressed gas generation.
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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".