Synergistic Modulation of Cationic Preferential Adsorption and Anionic Solvation Structure Reconstruction for Enhanced Stability of Zinc Anode
Bibliographic record
Abstract
Abstract Rechargeable aqueous zinc‐ion batteries (AZIBs) with low cost and high safety are promising for energy storage. However, challenges such as the hydrogen evolution reaction, corrosion, and dendrite growth diminish the stability and reversibility of the zinc anode. Herein, zirconium oxychloride is used as an electrolyte additive to address these issues via synergistic modulation. Experimental and theoretical results reveal that the cations (ZrO 2+ ) preferentially anchor to the Zn anode, forming a water‐poor electrical double layer that alters zinc ion migration pathways and restrains side reactions. Meanwhile, the anions (Cl − ) enter the solvation‐sheath structure of zinc ions, reconstruct the hydrogen‐bond network of the electrolyte, and weaken water reactivity, eliminating dendrite growth and promoting anticorrosion behavior. Consequently, the Zn||Zn symmetric cell confers a lifespan of 1800 h at 3 mA cm −2 for 1 mAh cm −2 . Zn||Cu half‐cells maintain a high coulombic efficiency of 99.8% after 1900 cycles. When matched with NaV 3 O 8 ·1.5H 2 O (NVO) cathode, the Zn||NVO full‐cell achieves a capacity retention of 77% at 5.0 A g −1 after 1000 cycles. This work provides a solution for developing high‐performance AZIBs.
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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".