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Record W2329576033 · doi:10.1021/am506572m

Characteristics of Colored Passive Layers on Titanium: Morphology, Optical Properties, and Corrosion Resistance

2014· article· en· W2329576033 on OpenAlexafffund
Rebecca J. Holmberg, Diane Beauchemin, Gregory Jerkiewicz

Bibliographic record

VenueACS Applied Materials & Interfaces · 2014
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceTitaniumCorrosionVisible spectrumMetalPolarization (electrochemistry)UltravioletOptical microscopeInfraredComposite materialOpticsOptoelectronicsAnalytical Chemistry (journal)Scanning electron microscopeMetallurgy

Abstract

fetched live from OpenAlex

Electrochemically formed colored passive layers on titanium and their optical, surface morphology, and corrosion properties are presented and discussed. With the application of progressively higher AC voltages (VAC) during preparation of these passive layers, they are found to become more protective of the underlying metal, as determined from corrosion resistance measurements employing electrochemical polarization curve and inductively coupled plasma mass spectrometry experiments. The passive layers on titanium were found to be uniform in their surface morphology with no apparent cracks or pits. Surface morphology, and its relation to optical properties, was also investigated using visible light microscopy, profilometry, and near-infrared ultraviolet visible reflectance spectroscopy measurements. A correlation between the light reflected from the entire sample surface and the coloration of surface grains was also observed through these measurements. The reflectance spectra showed a red-shift of wavelength maxima (λmax) values as AC voltages and, therefore, thicknesses were increased. Overall, these passive layers are protective of an already remarkable metal, and with greater knowledge of the properties of colored protective layers, their potential may be employed in a wide range of applications.

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 score0.714

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 head0.018
GPT teacher head0.225
Teacher spread0.207 · 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

Citations18
Published2014
Admission routes2
Has abstractyes

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