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Record W3135148355 · doi:10.22453/lsj-020.2.262-271

Development of novel corrosion resistant electroless NI-P composite coatings for pipeline steel

2019· article· en· W3135148355 on OpenAlexafffund
Omar Chaar, George Jarjoura, Zoheir Farhat, Rami Nader

Bibliographic record

VenueLebanese Science Journal · 2019
Typearticle
Languageen
FieldEngineering
TopicElectrodeposition and Electroless Coatings
Canadian institutionsDalhousie University
FundersCentre National de la Recherche ScientifiqueDalhousie University
KeywordsMaterials scienceCorrosionComposite numberCoatingScanning electron microscopeMetallurgyCarbon steelBurnishing (metal)Indentation hardnessIndentationHardnessTitaniumComposite materialPolarization (electrochemistry)MicrostructurePolishing

Abstract

fetched live from OpenAlex

In this research, coatings with various particles contents will be co-deposited within Ni–P deposits on AISI 1012 steel samples by electroless coating process. As a result, structural changes were evaluated, surface and cross-section morphology of composite deposits have been investigated by scanning electron microscopy (SEM), volumetric percentage of co-deposited particles have been determined using EDS analysis system, corrosion resistance of various EN coatings and current density were studied through potentiodynamic polarization, and mechanical properties were evaluated using wear and nano-indentation tests. The results showed that Alumina can give the best compromise between surface smoothness and deposition rate, Titanium can reach the highest levels of volumetric incorporation whereas Carbon can improve further lubrication effect on wear action, finally hardness and corrosion behavior had the best results with the existence of Alumina.

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)0.001
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 score0.074
Threshold uncertainty score0.645

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.010
GPT teacher head0.238
Teacher spread0.228 · 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

Citations3
Published2019
Admission routes2
Has abstractyes

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