Bionic Nanogel Interfaces Unlock Long‐Term Stability in Zn Metal Electrodeposition‐Based Electrochromic Windows
Bibliographic record
Abstract
Aqueous zinc (Zn) metal electrodeposition-based electrochromic windows (AZWs) are a promising dynamic window technology due to their use of low-cost, nonflammable, nontoxic, and highly conductive aqueous electrolytes. However, their development is hindered by issues such as poor reversibility, byproduct formation, and hydrogen evolution, which limit the optical window and cycling lifespan. Herein, a bionic transparent nanogel interlayer (TGI) in triple-layer structure introduced on both Zn electrode and indium tin oxide (ITO) glass electrode is demonstrated to achieve highly reversible electrochemical reaction. In the spontaneously formed triple-layer nanogel architecture, the top hydrophobic protective layer effectively mitigates water corrosion and suppresses hydrogen evolution reactions as well as byproduct formation. The middle layer incorporates internal fluorinated functional groups to promote uniform and rapid Zn ion transport. The bottom colloidal adhesion layer dynamically adapts to the substrate surface, preventing detachment due to morphology changes during cyclic Zn deposition/stripping processes. Consequently, the AZWs incorporating TGI@Zn and TGI@ITO glass electrodes exhibit excellent electrochemical properties and solar heat modulation abilities, which are attributed to their enhanced reversibility and uniform deposition of Zn ions. Compared with the single-layer interlayer, the three-layer structure design significantly improves the electrode's stability and performance, providing new ideas for designing next-generation AZW electrodes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".