Engineering Chemo-Mechanical Properties of Zn Surfaces via Alucone Coating
Bibliographic record
Abstract
Aqueous zinc (Zn)-ion batteries (AZIB) are promising candidates for the next-generation energy store systems due to their high capacity and low cost. Despite their nominal performance, Zn anodes tend to rapidly develop dendrite and fracture, leading to substantial capacity loss and cycling stability failure. Well-controlled coating using organic–inorganic hybrid molecules is highly promising to substantially improve their chemo-mechanical stability without compromising their performance. We herein present a critical assessment of the chemical and mechanical stability of alucone-coated Zn surfaces using first-principles simulations. Negative adsorption energies indicate strong cohesive strengths between alucone and the selected Zn surfaces. Energetically favorable alucone coatings are further verified by charge transfer at interfaces as seen through Bader charge analysis. Negative surface stress profiles at the alucone-coated interface are mostly responsible for surface reconstruction. The contributions of surface elastic constants are dependent on the selection of slip planes and the thickness of the thin film. By considering plane stress conditions, we calculate the mechanical properties that indicate the ductility of the alucone-coated basal thin film.
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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".