3D Nanowire Arrayed Cu Current Collector toward Homogeneous Alloying Anode Deposition for Enhanced Sodium Storage
Bibliographic record
Abstract
Abstract Alloying electrodes are regarded as promising anodes for lithium/sodium storage thanks to their multielectron reaction capacity, moderate voltage plateau, and high electrical conductivity. However, huge volume change upon cycling, especially for sodium storage, usually causes the loss of electrical connection between active components and their delaminations from traditional current collectors, thus leading to rapid capacity decay. Herein, a unique 3D current collector is assembled from 1D nanowire arrays anchored on 3D porous Cu foams for constructing core‐shelled Cu@Sb nanowires as advanced sodium‐ion battery (SIB) anodes. The so‐formed hierarchical 3D anode with interconnected 3D micrometer sized pores and abundant voids between nanowires not only effectively accommodates the structural strains during repeated cycling but also ensures the structural integrity and contributes to a uniform ion/electron scattered distribution throughout the whole surface. When employed as anodes for SIBs, the obtained electrode shows a high capacity of 605.3 mAh g−1 at 330 mA g−1, and demonstrates a high capacity retention of 84.8% even at a high current density of 3300 mA g−1. The 3D nanowire arrayed Cu current collector in this work can offer a promising strategy for designing and building advanced alloy anodes for lithium/sodium storage.
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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".