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Record W2619250075 · doi:10.20319/mijst.2015.12.5772

CORROSION PROTECTION OF STAINLESS STEEL TYPE 304 USING GRAPHENE COMPOSITES

2017· article· en· W2619250075 on OpenAlexaff
Hesham Alhumade, Hiba Nauman, Erij Elkamel, Aiping Yu, Ali Elkamel

Bibliographic record

VenueMATTER International Journal of Science and Technology · 2017
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMaterials scienceGrapheneDielectric spectroscopyComposite materialCorrosionPolyetherimideScanning electron microscopeCuring (chemistry)ElectrochemistryComposite numberElectrodeNanotechnologyPolymer

Abstract

fetched live from OpenAlex

Polyetherimide-Graphene (PEI/G) composites were prepared using in situ polymerization approach and thermally cured under vacuum on Stainless Steel 304 (SS304) substrates in order to be evaluated as corrosion protection coatings. Several steps curing were performed to ensure complete imidization of PEI/G composites. Dispersion of the graphene fillers in the PEI matrices was captured using Scanning electron microscopy (SEM) and Transmission electron microscopy (TEM). The study examines PEI/G composites as corrosion protection coatings using electrochemical techniques such as Cyclic Voltammetry (CV) and Electrochemical Impedance Spectroscopy (EIS). Furthermore, the influences of the load of graphene on the electrochemical behavior as well as the interface adhesion of the PEI/G composites are illustrated. Adhesion tests were conducted and evaluated according to ASTM D3359 standard and the long term performances of the prepared PEI/G coatings were confirmed by conducting the adhesion tests after 30 days of exposure to the corrosive medium. The study revealed that PEI may slow down the corrosion process on SS304 substrates and this protection property of PEI can be excelled by the incorporation of graphene in the PEI matrix.

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 score0.008
Threshold uncertainty score0.445

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 studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.029
GPT teacher head0.307
Teacher spread0.278 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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