Transparent inorganic multicolour displays enabled by zinc-based electrochromic devices
Bibliographic record
Abstract
Electrochromic displays have been the subject of extensive research as a promising colour display technology. The current state-of-the-art inorganic multicolour electrochromic displays utilize nanocavity structures that sacrifice transparency and thus limit their diverse applications. Herein, we demonstrate a transparent inorganic multicolour display platform based on Zn-based electrochromic devices. These devices enable independent operation of top and bottom electrochromic electrodes, thus providing additional configuration flexibility of the devices through the utilization of dual electrochromic layers under the same or different colour states. Zn-sodium vanadium oxide (Zn-SVO) electrochromic displays were assembled by sandwiching Zn between two SVO electrodes, and they could be reversibly switched between multiple colours (orange, amber, yellow, brown, chartreuse and green) while preserving a high optical transparency. These Zn-SVO electrochromic displays represent the most colourful transparent inorganic-based electrochromic displays to date. In addition, the Zn-SVO electrochromic displays possess an open-circuit potential (OCP) of 1.56 V, which enables a self-colouration behaviour and compelling energy retrieval functionality. This study presents a new concept integrating high transparency and high energy efficiency for inorganic multicolour displays.
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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 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".