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Record W3217212107 · doi:10.1002/adfm.202108341

Electrochromic Displays Having Two‐Dimensional CIE Color Space Tunability

2021· article· en· W3217212107 on OpenAlexaff
Wu Zhang, Haizeng Li, A. Y. Elezzabi

Bibliographic record

VenueAdvanced Functional Materials · 2021
Typearticle
Languageen
FieldMaterials Science
TopicTransition Metal Oxide Nanomaterials
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsElectrochromismMaterials scienceGamutHueColor spaceOptoelectronicsElectrochromic devicesOpticsDisplay deviceComputer scienceElectrodePhysicsComputer vision

Abstract

fetched live from OpenAlex

Abstract Electrochromic devices with a wide color gamut distribution have long been sought after for non‐emissive display technologies. The current state‐of‐the‐art multicolor electrochromic displays utilize a single electrochromic layer, which restricts their color tunability within a linear or curved segment scope in International Commission on Illumination (CIE) color space and thus leads to limited color hues. Herein, it is demonstrated vivid electrochromic displays with broadened color hues via fabricating Zn‐based multicolor electrochromic displays having 2D CIE color space tunability. In addition, it is revealed that a Fabry–Perot nanocavity structure can further tune the color hues via altering the coordinate of the 2D CIE color space. It is known that this is the first demonstration of 2D CIE color space tunability realization from a single transparent or reflective electrochromic device. These findings represent a novel strategy for fabricating multicolor electrochromic displays and are expected to advance the development of electrochromic displays.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.010
GPT teacher head0.241
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations145
Published2021
Admission routes1
Has abstractyes

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