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Record W4234152390 · doi:10.5383/ijtee.11.01.007

Nature of Steel Effect on Intermetallic Compounds Obtained by Galvanization

2016· article· en· W4234152390 on OpenAlexvenueno aff
Younès Benarioua

Bibliographic record

VenueInternational Journal of Thermal and Environmental Engineering · 2016
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsnot available
Fundersnot available
KeywordsGalvanizationIntermetallicMetallurgyMaterials scienceCoatingMicrostructureZincSubstrate (aquarium)CorrosionVickers hardness testSurface finishOptical microscopeCathodic protectionGalvannealedLayer (electronics)Composite materialScanning electron microscopeAlloyElectrochemistryChemistry

Abstract

fetched live from OpenAlex

Zinc and some of its alloys have a number of characteristics that make it well suited for use as a protective coating against the corrosion of steel substrates under severe atmospheric conditions. The metal of zinc, which represents the main galvanization element offer then a cathodic protection to the ferrous materials. Because of these excellent characteristics, galvanization coatings are expected to be used for different protective applications fields. The objective of this work is to study the influence of the nature of steel substrate on the microstructure and the hardness of the intermetallic compounds. The steels used as the substrate are employed in agriculture field as tubes and irrigation elements in pivot. After an optimal preparation of the surface of the substrate by an appropriate roughness process, the steels specimen were immersed in a molten zinc bath maintained at 450°C. The chemical reactions which take place between the steel and the liquid zinc give rise to the formation of the  and  intermetallic compounds and the -Fe/Zn solid solution. The structure of coating was identified by X ray diffraction. The morphology and thickness of phases formed the coatings at different parameters took place with optical microscope. Finally the hardness of coatings was measured with a Vickers hardness tester.

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 score0.006
Threshold uncertainty score0.230

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 head0.003
GPT teacher head0.193
Teacher spread0.190 · 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

Citations2
Published2016
Admission routes1
Has abstractyes

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