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Record W4327556890 · doi:10.1117/12.2655238

Zinc anode-based electrochromic devices for dynamic light modulation

2023· article· en· W4327556890 on OpenAlexaff
Wu Zhang, Haizeng Li, A. Y. Elezzabi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicTransition Metal Oxide Nanomaterials
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsElectrochromismAnodeElectrochromic devicesMaterials scienceOptoelectronicsBistabilityElectrodeComputer scienceChemistry

Abstract

fetched live from OpenAlex

As an emerging energy conversion technology, electrochromism has attracted immense interest due to its various applications including variable optical filters, bistable displays and energy-efficient smart windows. 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. As such, a novel design of Zn anodebased electrochromic devices was recently developed as a key strategy to partially retrieve the consumed electrical energy. Compared to conventional electrochromic devices, Zn anode-based electrochromic devices exhibit a rapid spontaneous switching behavior due to the high value of redox potential difference between a metal anode and an electrochromic cathode, thus providing the most energy-efficient consumption during operation. Moreover, Zn-based electrochromic 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 color states. As such, the color overlay effect can greatly broaden the color palettes via altering the coordinate of the 2D CIE color space. These key issues of Zn anode-based electrochromic devices make them very promising for the future development of electrochromic devices.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.030
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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)0.0010.001

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.

Opus teacher head0.014
GPT teacher head0.269
Teacher spread0.255 · 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 teacher head, not a consensus.

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

Citations0
Published2023
Admission routes1
Has abstractyes

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