Synergistic Lithium Alloying and Plating in 3D Cu/CNT/Sn Electrodes for Stable Lithium Metal Batteries
Bibliographic record
Abstract
Anode-less Li-ion batteries, in which Li is reversibly plated onto and stripped from a metal current collector during charge and discharge, theoretically offer the highest possible anode energy density. However, such systems suffer from rapid self-discharge, excessive solid electrolyte interphase (SEI) formation, and dendritic lithium growth, resulting in severe performance degradation and safety concerns. Here, these challenges are addressed by introducing a novel 3D current collector that enables energy storage via a hybrid mechanism of alloying and plating. The 3D current collectors are fabricated through two scalable electroplating processes involving a porous Cu plating process followed by a Sn surface coating, and are structurally reinforced with carbon nanotubes (CNTs) to form a mechanically robust and conductive scaffold. The relative contributions of the alloying and plating reactions to the cell capacity are modulated by adjusting the thickness of the Sn layer, which governs the extent of lithiation through alloy formation. By optimizing the capacity distribution between Sn alloying and Li plating, the resulting half-cell exhibits stable cycling over 200 cycles with an average Coulombic efficiency of 93.9%, significantly outperforming a control cell with planar Cu foils, which retain only 71.3% efficiency after 110 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".