Emerging Zn Anode‐Based Electrochromic Devices
Bibliographic record
Abstract
The development of electrochromic materials has opened the door to the development of numerous devices including smart windows, color displays, optical filters, wearable camouflages, among others. Although the current electrochromic devices do not consume energy while maintaining their colored or colorless states, their bistable operation requires external electrical energy to be consumed during switching. To reduce the energy consumption of an electrochromic device, an emerging Zn anode-based electrochromic device concept was recently introduced to partially retrieve the consumed electrical energy. In this Review, key technological developments and scientific challenges are presented for a broad range of Zn anode-based electrochromic device configurations with emphasis on the inherent distinctions between the Zn anode-based and conventional electrochromic devices. Specifically, a comprehensive comparison of the two classes of electrochromic devices in the high-performance device design is provided. For the electrochromic layer, the methods for obtaining high-quality electrochromic materials and thin films are reviewed. For the electrolytes, the effect of the dual ion system on the electrochromic performance is discussed. Also, some critical but unresolved issues in the device design and fabrication are discussed. The perspectives and outlook at the end of this Review provide recommendations to improve performance for future electrochromic studies.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".