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Record W4406907940 · doi:10.3390/ma18030595

Studies of Corrosion Inhibition Performance of Inorganic Inhibitors for Aluminum Alloy

2025· article· en· W4406907940 on OpenAlexafffund
Redouane Farid, D.K. Sarkar, Santanu Das

Bibliographic record

VenueMaterials · 2025
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsUniversité du Québec à Chicoutimi
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCorrosionMaterials scienceScanning electron microscopeAlloySodium silicateAmmonium metavanadateSubstrate (aquarium)AmmoniumAqueous solutionAluminiumNuclear chemistryInorganic chemistryMetallurgyVanadiumChemistryComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

In this study, the behavior of sodium silicate (Na2SiO3), manganese sulfate monohydrate (MnSO4·H2O), and ammonium metavanadate (NH4VO3) as corrosion inhibitors for AA6061 aluminum alloy (Al) was investigated. The polarization resistance (Rp) of the Al substrate immersed in 0.1 M NaCl solution was found to be 13 kΩ·cm2. In comparison, the Rp of the Al substrate immersed in 0.1 M NaCl in the presence of Na2SiO3, Na2SiO3/MnSO4·H2O, and Na2SiO3/NH4VO3 inhibitors was found to be 100, 133, and 679 kΩ·cm2, respectively. The best inhibition result was obtained when the mixture of the inhibitors was used with Rp of 722 kΩ·cm2. The maximum percentage of the corroded area calculated from the scanning electron microscopy (SEM) images was found to be 5.7% for Al substrate immersed in 0.1 M NaCl, which decreased to 0.06% when the mixture of the inhibitors was used. The synergetic effects between the three inhibitors were studied, and the results illustrated that the combination of Na2SiO3, MnSO4·H2O, and NH4VO3 provided the best corrosion inhibition properties for Al in aqueous NaCl environments.

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 score0.002
Threshold uncertainty score0.499

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 head0.026
GPT teacher head0.293
Teacher spread0.268 · 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

Citations8
Published2025
Admission routes2
Has abstractyes

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