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Record W2078650835 · doi:10.1155/2013/648246

A New Approach in Modifying Polymeric Coatings to Increase Corrosion Resistance Properties

2013· article· en· W2078650835 on OpenAlexaff
Alisina Toloei, Sanam Atashin, Mohammad Ebrahim Bahrololoom

Bibliographic record

VenueISRN Materials Science · 2013
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Nanocomposites and Properties
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsEpoxyDielectric spectroscopyMaterials scienceCorrosionScanning electron microscopeCoatingComposite materialMontmorilloniteCarbon steelElectrochemistryPolymerPolarization (electrochemistry)Chemical engineeringChemistryElectrode

Abstract

fetched live from OpenAlex

In this study, carbon steel sheets were coated with a modified epoxy coating. The urea-modified montmorillonite clay nanoparticles were added to a DGEBA epoxy resin in different contents and then applied to the surfaces. The corrosion resistance of the coated samples was determined by electrochemical techniques (open circuit potential and linear polarization) in 3.5 wt% NaCl solutions at room temperature and 80°C. Electrochemical impedance spectroscopy (EIS) evaluated the properties of polymer-coated metals and their changes during the exposure to corrosive environments. Scanning electron microscopy (SEM) was used to characterize the coatings. An improvement of protective properties of epoxy coatings with an optimal percentage of the modified clay in comparison with pure epoxy was achieved.

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 score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.023
GPT teacher head0.233
Teacher spread0.210 · 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

Citations6
Published2013
Admission routes1
Has abstractyes

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