Ultrafast Metal Electrodeposition Revealed by In Situ Optical Imaging and Theoretical Modeling towards Fast‐Charging Zn Battery Chemistry
Bibliographic record
Abstract
Abstract Metallic Zn is a preferred anode material for rechargeable aqueous batteries towards a smart grid and renewable energy storage. Understanding how the metal nucleates and grows at the aqueous Zn anode is a critical and challenging step to achieve full reversibility of Zn battery chemistry, especially under fast‐charging conditions. Here, by combining in situ optical imaging and theoretical modeling, we uncover the critical parameters governing the electrodeposition stability of the metallic Zn electrode, that is, the competition among crystallographic thermodynamics, kinetics, and Zn 2+ ‐ion diffusion. Moreover, steady‐state Zn metal plating/stripping with Coulombic efficiency above 99 % is achieved at 10–100 mA cm −2 in a reasonably high concentration (3 M) ZnSO 4 electrolyte. Significantly, a long‐term cycling‐stable Zn metal electrode is realized with a depth of discharge of 66.7 % under 50 mA cm −2 in both Zn||Zn symmetrical cells and MnO 2 ||Zn full cells.
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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 | 0.001 |
| Open science | 0.001 | 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".