Phosphorus–Hard Carbon Composite Anodes for High-Performance Sodium-Ion Batteries
Bibliographic record
Abstract
Sodium-ion batteries (SIBs) present a promising alternative to lithium-ion batteries for large-scale energy storage, owing to the abundance of sodium resources and lower cost. However, the commonly used anode material, hard carbon (HC), limits volumetric energy density due to its porous structure and low density. Red phosphorus (P), with its high theoretical volumetric capacity (∼6000 mAh cm –3 ), is a compelling alternative but suffers from severe volume expansion and poor electronic conductivity. In this study, we investigated the potential of composite electrodes by blending red P with commercial HC to harness the complementary advantages of both materials. To optimize red P electrodes, SWCNT conductive additives, a PAANa binder, and carbonate-based electrolytes with an FEC additive were initially employed. Following this, a systematic investigation was conducted into different blending methods, specifically mechanical blending and physical vapor deposition (PVD). PVD significantly improved the uniformity and cycling stability of P/HC blends. Blending 5 wt% P via PVD enhanced specific capacity from ∼300 mAh g –1 (pure HC) to ∼420 mAh g –1 , maintaining excellent cycling stability over 100 cycles. These findings provide valuable guidance for developing high-capacity, stable SIB anodes and lay the groundwork for further optimization of P/HC composites.
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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 | 0.001 | 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 | 0.001 |
| 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".