Template‐Sacrificed Hot Fusion Construction and Nanoseed Modification of 3D Porous Copper Nanoscaffold Host for Stable‐Cycling Lithium Metal Anodes
Bibliographic record
Abstract
Abstract Lithium (Li) metal anodes have been proposed as a promising candidate for high‐energy‐density electrode materials in secondary batteries. However, the dendrite growth and unstable electrode–electrolyte interfaces during Li plating/stripping are fatal to their practical applications. Herein, the construction of 3D porous Au/Cu nanoscaffold prepared via a convenient template‐sacrificed hot fusion construction method and a nanoseed modification process as an effective Li metal hosting material are proposed. The Au/Cu nanoscaffold can spatially guide uniform deposition of Li metal free from the growth of Li dendrites due to the homogenous Li+ ion flux and negligible nucleation overpotential. Moreover, the Cu skeleton can relieve volume change and stabilize local current density during cycling processes. Benefiting from these advantages, the symmetric cells based on self‐supported Li‐filled Au/Cu (Li‐Au/Cu) nanoscaffold electrodes present highly stable Li plating/stripping for more than 1000 h with a low voltage hysteresis less than 90 mV and a long lifespan over 1300 h at 1.0 mA cm–2 in carbonate‐based electrolytes. Impressively, the Li‐Au/Cu nanoscaffold||LiFePO4 full cells also exhibit exceptional cycling stability and rate performance. This work provides a promising strategy to construct dendrite‐free lithium metal anodes toward high‐performance lithium 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) | 0.000 | 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".