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Effect of amino acid on the passivation, corrosion and inhibition behavior of aluminum alloy in alkaline medium

2017· article· en· W2793329939 on OpenAlexaff
S. Kharacha, A. Batah, M. Belkhaouda, L. Bazzi, L. Bammou, R. Salghi, O. Jbara, A. Tara

Bibliographic record

VenueMoroccan Journal of chemistry · 2017
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsThe Audio Recording Academy
Fundersnot available
KeywordsPassivationDielectric spectroscopyCorrosionAlloyAdsorptionMaterials scienceLangmuir adsorption modelPolarization (electrochemistry)Cyclic voltammetryElectrochemistryScanning electron microscopeInorganic chemistryAluminiumNuclear chemistryChemistryMetallurgyElectrodePhysical chemistryComposite material

Abstract

fetched live from OpenAlex

The effect of L-methionine (L-Met) on the passivation and corrosion inhibition of aluminum alloy was investigated in carbonate medium in presence of chloride ions. This inhibitive action against the corrosion of aluminum in corrosive solution was investigated at 298 K using potentiodynamic polarization curves (PDP), cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). The results obtained from the polarization curves reveal that the aluminum in alkaline medium exhibits a phenomenon of passivation with breakdown of passivity. Temperature effect on the corrosion behavior with the addition of (L-Met) studied in the range of temperature from 298 to 328 K. The inhibition efficiency decreases slightly with the increase in the temperature. Results show that L-Met is a good inhibitor and inhibition efficiency reach 87,23% at 10−3M. The electrochemical results are confirmed by scanning electron microscopy (SEM). The adsorption of this compound on aluminum surface obeys Langmuir’s adsorption isotherm. The kinetic and thermodynamic parameters were determined and discussed.

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 score0.005
Threshold uncertainty score0.314

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.012
GPT teacher head0.264
Teacher spread0.252 · 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

Citations7
Published2017
Admission routes1
Has abstractyes

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