Designing Fluorine-Free Electrolytes for Practical Sodium Metal Anodes and Seawater Batteries
Bibliographic record
Abstract
Fluorine (F) is regarded as a key element in electrolytes for sodium metal anodes (SMAs) because of the formation of NaF containing solid–electrolyte interphase (SEI) layers; however, the high-cost and HF formation issues experienced by F-based electrolytes should be addressed. Herein, F-free, cost-effective 1 M NaBH4/ether-based electrolytes are proposed, motivated by the recent speculation that NaH is a “good SEI layer.” The time-of-flight secondary ion mass spectrometry (TOF-SIMS) results of sodium metal electrodes after galvanostatic cycling demonstrated that NaH is a major component of the SEI layer. In addition, the native oxide surface of sodium was converted into NaH and NaBO2 after soaking in the electrolytes, implying that “SEI reconstruction” occurred by chemical reduction. Accordingly, significantly longer cyclability was obtained in the Na‖Na symmetric cell (1200 h, 1 mA cm−2, 1 mA h cm−2) than in F-based electrolytes. In seawater batteries (SWBs), 1 M NaBH4/DEGDME (diethylene glycol dimethyl ether) delivers higher power density (2.82 mW cm−2 vs. 2.27 mW cm−2) and cyclability (300 h vs. 50 h) under 1 mA cm−2 than 1 M NaOTf/TEGDME (tetraethylene glycol dimethyl ether), which is commonly used in SWBs. In conclusion, two novel contributions of this study include the demonstration that NaH can work as a “good SEI layer” apart from NaF and the proposal of a cost-effective, F-free electrolyte for practical and large-scale SWBs.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".