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Record W2317482912 · doi:10.1021/am2000196

Spectral and Physical Properties of Electrochemically Formed Colored Layers on Titanium Covered with Clearcoats

2011· article· en· W2317482912 on OpenAlexafffund
Andrew Munro, Michael F. Cunningham, Gregory Jerkiewicz

Bibliographic record

VenueACS Applied Materials & Interfaces · 2011
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceTitaniumColoredCoatingComposite materialSiliconePolymerSurface finishMetallurgy

Abstract

fetched live from OpenAlex

We present the application and characterization of two commercial polymer clearcoats to electrochemically formed colored passive layers on titanium with the aim of providing physical protection required in many of titanium's applications, while allowing the unique appearance of the colored layers to show through. Thin layers of an acrylic automotive clearcoat (∼3.5 μm thick) and an epoxy marine clearcoat (∼6.5 μm thick) are applied to the colored titanium surfaces using spin coating, and are found to slightly modify their visual properties, while maintaining their bright, well-defined sparkling colors. Both clearcoats are found to significantly reduce the surface roughness, thereby reducing potential wear from friction and the adhesion of fine dirt particles. They are also found to notably decrease the wetting properties of colored titanium, furthering its protection against damage from ambient and aqueous media. The clearcoats show the ability to protect colored titanium from physical and chemical damage, with the automotive clearcoat exhibiting superior adhesion. Our electrochemical coloring technique combined with the application of clearcoats creates a new and unique system that does not rely solely on a polymer coating for its colorful appearance and protection against corrosion.

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 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 score0.003
Threshold uncertainty score0.555

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)0.0000.000

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 head0.023
GPT teacher head0.218
Teacher spread0.195 · 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.

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

Citations4
Published2011
Admission routes2
Has abstractyes

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