Utilizing the full capacity of carbon black as anode for Na-ion batteries via solvent co-intercalation
Bibliographic record
Abstract
Carbonaceous materials have long been considered promising anode materials for Na-ion batteries. However, the electrochemical performance of conventional carbon anodes is generally poor because the sodium ion storage solely relies on the disordered region of the carbon materials in a carbonate-based electrolyte. The solvent co-intercalation mechanism for Na ions has been recently reported in natural graphite anodes for Na-ion batteries with ether-based electrolytes, but their capacities are still unsatisfactory. We show here for the first time that by combining regular Na ion storage in the disordered carbon layer and solvent co-intercalation mechanism in the graphitized layer of a commercial N330 carbon black as an anode material for Na-ion batteries in ether-based electrolyte, the reversible capacity could be fully realized and doubled in magnitude. This unique sodium intercalation process resulted in a significantly improved electrochemical performance for the N330 electrode with an initial reversible capacity of 234 mAh·g –1 at 50 mA·g –1 and a superior rate capability of 105 mAh·g –1 at 3,200 mA·g –1 . When cycled at 3,200 mA·g –1 over 2,000 cycles, the electrode still exhibited a highly reversible capacity of 72 mAh·g –1 with a negligible capacity loss per cycle (0.0167%). Additionally, surface-sensitive C K-edge X-ray absorption spectroscopy, with the assistance of electrochemical and physicochemical characterizations, helped in identifying the controlled formation and evolution of a thin and robust solid electrolyte interphase film. This film not only reduced the resistance for sodium ion diffusion, but also maintained the structural stability of the electrode for extended cycle reversibility. The superior electrochemical performance of N330 carbon black strongly demonstrated the potential of applying ether-based electrolytes for a wide range of carbon anodes apart from natural graphite.
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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 | 0.001 | 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".