Grotthuss Mechanism for Stable Zinc Anodes: Time‐Resolved pH Buffering in Aqueous Batteries
Bibliographic record
Abstract
Abstract In zinc ion batteries, the curtailed lifespan and diminished Coulombic efficiency are primarily ascribed to the hydrogen evolution reaction, surface corrosion, and rampant dendrites, all related to unstable interfacial pH at the anode. To tackle these challenges, hydrogen‐bonded organic frameworks (HOFs) are designed possessing outstanding zincophilic and hydrogen storage capabilities on the surface of Zn, thereby creating a dendrite‐free anode (MACA@Zn). By leveraging the innate and reversible proton‐hopping mechanism of MACA, the interfacial pH at the anode is able to be controlled. In situ scanning electrochemical microscopy has demonstrated a time‐resolved local pH buffering effect. Moreover, the presence of MACA induces preferential growth of the (002) plane, resulting in a uniform and dense Zn deposition layer. Consequently, the Zn//Zn cell with MACA@Zn anode delivers an exceptional cycling stability of ≈2000 h at 5 mA cm−2 and 1 mAh cm−2, with a high cumulative plating capacity of 4950 mAh cm−2. When paired with an α‐MnO2 cathode, the cell retains a specific capacity of 70.4 mAh g−1 after 990 cycles, demonstrating a capacity retention of 44.87%. This research emphasizes the multifunctional protective effects of HOFs on the anode surface and offers critical insights for advancing the development and real‐world implementation of ZIBs.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".