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Structures and Optical Properties of Anodic Aluminum Oxide Thin Films

2014· article· en· W1674857276 on OpenAlexvenueno aff
Qi Hua Fan, David Galipeau, Baibhav Ghimire, Rakesh Kumar Gupta, Mukul Dubey, Nitu Mandal

Bibliographic record

VenueJournal of Coating Science and Technology · 2014
Typearticle
Languageen
FieldMaterials Science
TopicAnodic Oxide Films and Nanostructures
Canadian institutionsnot available
FundersOffice of Experimental Program to Stimulate Competitive ResearchSouth Dakota Board of RegentsNational Science Foundation
KeywordsMaterials scienceAnodizingEllipsometryRefractive indexSubstrate (aquarium)DielectricEtching (microfabrication)PorosityIsotropic etchingThin filmAluminiumSurface roughnessOxideCoatingComposite materialOptoelectronicsLayer (electronics)NanotechnologyMetallurgy

Abstract

fetched live from OpenAlex

Low refractive index materials (n<1.3) are not common in nature. However, they are essential for antireflection coatings. In this study porous anodic aluminum oxide (AAO) on glass substrate was fabricated by electrochemical oxidation and subsequent etching. The pore size was modulated from less than 80 nm to more than 250 nm. The pore depth was controlled by electrochemical anodization and/or chemical etching time. It is challenging to effectively quantify the pore structures and the optical properties of such porous materials. Using spectroscopic ellipsometry, the authors showed that the AAO materials had tunable refractive index from 1.25 to 1.40, which is ideal for antireflection coating on glass (n=1.54). In addition, quantitative information on the AAO film porosity, profile structure, film thickness, dielectric constants, and roughness was also derived from the ellipsometry analysis. It was shown that the as-fabricated AAO film included trace amount of residual metal aluminum with an effective thickness ~0.28 nm.

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 score0.003

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 head0.009
GPT teacher head0.226
Teacher spread0.217 · 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

Citations2
Published2014
Admission routes1
Has abstractyes

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