Salt‐Assisted Recovery of Sodium Metal Anodes for High‐Rate Capability Sodium Batteries
Bibliographic record
Abstract
Abstract Rechargeable sodium metal batteries are considered to be one of the most promising high energy density and cost‐effective electrochemical energy storage systems. However, their practicality is constrained by the high reactivity of sodium metal anodes that readily brings about excessive accumulation of inactive Na species on the surface, either by chemical reactions with oxygen and moisture during electrode handling or through electrochemical processes with electrolytes during battery operation. Herein, this paper reports on an alkali, salt‐assisted, assembly‐polymerization strategy to recover Na activity and to reinforce the solid‐electrolyte interphase (SEI) of sodium metal anodes. To achieve this, an alkali‐reactive coupling agent 3‐glycidoxypropyltrimethoxysilane (GPTMS) is applied to convert inactive Na species into Si–O–Na coordination with a self‐assembly GPTMS layer that consists of inner O–Si–O networks and outer hydrophobic epoxides. As a result, the electrochemical activity of Na metal anodes can be fully recovered and the robust GPTMS‐derived SEI layer ensures high capacity and long‐term cycling under an ultrahigh rate of 30 C (93.1 mAh g −1 , 94.8% after 3000 cycles). This novel process provides surface engineering clues on designing high power density and cost‐effective alkaline metal batteries.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".