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Record W2562184774 · doi:10.5539/jmsr.v6n1p44

Electrochromic Properties of Sputtered Iridium Oxide Thin Films with Various Film Thicknesses

2016· article· en· W2562184774 on OpenAlexvenueno aff
Yoshio Abe, Satoshi Ito, Kyung Ho Kim, Midori Kawamura, Takayuki Kiba

Bibliographic record

VenueJournal of Materials Science Research · 2016
Typearticle
Languageen
FieldMaterials Science
TopicTransition Metal Oxide Nanomaterials
Canadian institutionsnot available
Fundersnot available
KeywordsElectrochromismMaterials scienceTransmittanceAmorphous solidThin filmColoredSputteringIridiumOxideOptoelectronicsAbsorption (acoustics)WavelengthOpticsComposite materialElectrodeNanotechnologyMetallurgy

Abstract

fetched live from OpenAlex

Iridium oxide is an anodic electrochromic material, which takes on a blue-black color through electrochemical oxidation and turns to transparent via reduction. Hydrated amorphous Ir oxide thin films with various thicknesses from 20 to 400 nm were prepared by reactive sputtering in a H2O atmosphere, and their transmittance spectra in both the bleached and colored states as well as their response times were examined in this study. The bleached and colored transmittances decreased with increasing film thickness according to Lambert’s law, and the absorption coefficients in the bleached and colored states were estimated to be 3.2 ´ 103 and 1.1 ´ 105 cm-1, respectively, at a wavelength of 600 nm. The results point to almost all the Ir atoms being electrochemically active and contributing to the color change. A maximum transmittance change of 81% was obtained for the 400 nm-thick film. Further, there was a trade-off between the response speed and the transmittance change. The response speed slowed down with increasing the film thickness, while the coloring and bleaching response time for the thick films was several tens of seconds.

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 integrity0.0000.001
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 head0.049
GPT teacher head0.313
Teacher spread0.263 · 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

Citations13
Published2016
Admission routes1
Has abstractyes

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