Steering Interfacial Renovation with Highly Electronegative Cl Modulated Trinity Effect for Exceptional Durable Zinc Anode
Bibliographic record
Abstract
Abstract The poor anode/electrolyte interface triggered by abysmal dendritic growth and hydrogen evolution reactions (HER) hinders the development of aqueous zinc‐ion batteries (AZIBs). Here, a highly efficient electrolyte is formulated with sucralose (Sucral) additive to refresh solvated structure and steer interfacial renovation, for building highly electronegative Cl‐modulated trinity effect in the bulk‐interface between electrolyte and electrode. Experiment results and theoretical studies jointly reveal that Sucral with highly zincophilic and hydrophilic hydroxyl groups can remodel primary Zn2+ solvation shell and interrupt strong H‐bond network from H2O molecules, thus boosting fast de‐solvation and restricting undesirable HER. Simultaneously, three highly electronegative chlorides in the adsorbed Sucral possessing hydrophobic features can enable a H2O‐poor electric double‐layer (EDL), thus remodeling the Zn surface against corrosion. Additionally, it realizes preferential exposure of the Zn (002) plane for helping uniform interfacial deposition. The synergy of the above factors achieves a prolonged lifespan of 3000 h (1.0 mA cm−2, 1.0 mAh cm−2), much better than that with Sucrose (Suc) electrolyte. The Zn//V2O5 full cell at 5 A g−1 also maintains enhanced stability of 1500 cycles with 160 mAh g−1.
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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".