Regulating Zinc Nucleation Sites and Electric Field Distribution to Achieve High‐Performance Zinc Metal Anode via Surface Texturing
Bibliographic record
Abstract
Abstract Understanding zinc (Zn) deposition behavior and improving Zn stripping and plating reversibility are significant in developing practical aqueous Zn ion batteries (AZIBs). Zn metal is abundant, cost‐effective, and intrinsically safe compared with Li. However, their similar inhomogeneous growth regime harms their practicality. This work reports a facile, easily scalable, but effective method to develop a textured Zn with unidirectional scratches on the surface that electrochemically achieves a high accumulated areal capacity of 5530 mAh cm−2 with homogenized Zn deposition. In symmetric cells, textured Zn presents a stable cycling performance of 1100 hours (vs 250 h of bare Zn) at 0.5 mA cm−2 for 0.5 mAh cm−2 and lower nucleation and plating overpotentials of 120.5 and 41.8 mV. In situ optical microscopy and COMSOL simulation disclose that the textured surface topography can 1) homogenize the electron field distribution on the Zn surface and regulate Zn nucleation and growth, and 2) provides physical space to accommodate Zn deposits, prevent the detachment of “dead” Zn, and improve the structural sufficiency of Zn anode. Moreover, differential electrochemical mass spectrometry analysis find that the textured Zn with regulated interfacial electron activity also presents a higher resistance toward hydrogen evolution and other parasitic reactions.
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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) | 0.001 | 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".