Promoting Ge Alloying Reaction via Heterostructure Engineering for High Efficient and Ultra‐Stable Sodium‐Ion Storage
Bibliographic record
Abstract
Abstract Germanium (Ge)‐based materials have been considered as potential anode materials for sodium‐ion batteries owing to their high theoretical specific capacity. However, the poor conductivity and Na + diffusivity of Ge‐based materials result in retardant ion/electron transportation and insufficient sodium storage efficiency, leading to sluggish reaction kinetics. To intrinsically maximize the sodium storage capability of Ge, the nitrogen doped carbon‐coated Cu 3 Ge/Ge heterostructure material (Cu 3 Ge/Ge@N‐C) is developed for enhanced sodium storage. The pod‐like structure of Cu 3 Ge/Ge@N‐C exposes numerous active surface to shorten ion transportation pathway while the uniform encapsulation of carbon shell improves the electron transportation, leading to enhanced reaction kinetics. Theoretical calculation reveals that Cu 3 Ge/Ge heterostructure can offer decent electron conduction and lower the Na + diffusion barrier, which further promotes Ge alloying reaction and improves its sodium storage capability close to its theoretical value. In addition, the uniform encapsulation of nitrogen‐doped carbon on Cu 3 Ge/Ge heterostructure material efficiently alleviates its volume expansion and prevents its decomposition, further ensuring its structural integrity upon cycling. Attributed to these unique superiorities, the as‐prepared Cu 3 Ge/Ge@N‐C electrode demonstrates admirable discharge capacity, outstanding rate capability and prolonged cycle lifespan (178 mAh g −1 at 4.0 A g −1 after 4000 cycles).
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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) | 0.000 | 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".