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Record W2330030188 · doi:10.1021/am405938n

Structure–Property Correlation in EEMAO Fabricated TiO<sub>2</sub>–Al<sub>2</sub>O<sub>3</sub> Nanocomposite Coatings

2014· article· en· W2330030188 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueACS Applied Materials & Interfaces · 2014
Typearticle
Languageen
FieldEngineering
TopicElectrophoretic Deposition in Materials Science
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMaterials scienceAnataseNanocompositeCorrosionRutileMicrostructureTitaniumChemical engineeringElectrochemistrySulfuric acidOxideMetallurgyComposite materialElectrodeCatalysisPhotocatalysis

Abstract

fetched live from OpenAlex

We grew TiO2-Al2O3 nanocomposite coatings on titanium substrates by electrophoretic enhanced microarc oxidation (EEMAO) technique under several voltages and established a correlation between microstructure, surface hardness, and corrosion resistance of the coatings in sulfuric acid and sodium chloride solutions. Structural analysis revealed that the coatings contained anatase, rutile, alumina, and tialite phases. Formation kinetics of tialite phase was studied. It was found that increasing the voltage gives rise to a coarser morphology, i.e., larger pore size, and incorporation of more alumina nanoparticles into the layers. It is shown that surface hardness of the titanium substrates increased by a factor of 4 following EEMAO treatment. Corrosion resistance of titanium was enhanced significantly. Resistance against pitting corrosion was improved as well. We proposed a formation mechanism for the TiO2-Al2O3 composite coatings at different voltages based on the chemical and electrochemical foundations.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.007
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.004
GPT teacher head0.185
Teacher spread0.181 · 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