Nanostructured inorganic electrochromic materials for light applications
Bibliographic record
Abstract
Abstract Electrochromism, an emerging energy conversion technology, has attracted immense interest due to its various applications including bistable displays, optical filters, variable optical attenuators, optical switches, and energy‐efficient smart windows. Currently, the major drawback for the development of electrochromism is the slow switching speed, especially in inorganic electrochromic materials. The slow switching speed is mainly attributed to slow reaction kinetics of the dense inorganic electrochromic films. As such, an efficient design of nanostructured electrochromic materials is a key strategy to attain a rapid switching speed for their real‐world applications. In this review article, we summarize the classifications of electrochromic materials, including inorganic materials (e.g., transition metal oxides, Prussian blue, and polyoxometalates), organic materials (e.g., polymers, covalent organic frameworks, and viologens), inorganic‐organic hybrids, and plasmonic materials. We also discuss the electrochromic properties and synthesis methods for various nanostructured inorganic electrochromic materials depending on structure/morphology engineering, doping techniques, and crystal phase design. Finally, we outline the major challenges to be solved and discuss the outlooks and our perspectives for the development of high‐performance nanostructured electrochromic materials.
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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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".