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Record W2509745963 · doi:10.1155/2016/1025261

Electrochemical and Microstructural Analysis of FeS Films from Acidic Chemical Bath at Varying Temperatures, pH, and Immersion Time

2016· article· en· W2509745963 on OpenAlexafffund
Ladan Khaksar, Gary Whelan, J. Shirokoff

Bibliographic record

VenueInternational Journal of Corrosion · 2016
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsMemorial University of Newfoundland
FundersMemorial University of NewfoundlandSuncor Energy Incorporated
KeywordsCorrosionMaterials scienceSulfideScanning electron microscopeIron sulfideImmersion (mathematics)MetallurgyAlloyPolarization (electrochemistry)ElectrochemistryEnergy-dispersive X-ray spectroscopyNuclear chemistryChemical engineeringChemistryComposite materialElectrodeSulfur

Abstract

fetched live from OpenAlex

The corrosion resistance and corrosion products of 4130 alloy steel have been investigated by depositing thin films of iron sulfide synthesized from an acidic chemical bath. Tests were conducted at varying temperatures (25°C–75°C), pH levels (2–4), and immersion time (24–72 hours). The corrosion behavior was monitored by linear polarization resistance (LPR) method. X-ray Diffraction (XRD), Energy Dispersive X-ray (EDX) spectroscopy, and Scanning Electron Microscopy (SEM) have been applied to characterize the corrosion products. The results show that, along with the formation of an iron sulfide protective film on the alloy surface, increasing temperature, increasing immersion time, and decreasing pH all directly increase the corrosion rate of steel in the tested experimental conditions. It was also concluded that increasing temperature causes an initial increase of the corrosion rate followed by a large decrease due to transformation of the iron sulfide crystalline structure.

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 categoriesInsufficient payload (model declined to judge)
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 score0.001
Threshold uncertainty score1.000

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)0.0010.000

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 head0.006
GPT teacher head0.240
Teacher spread0.234 · 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.

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

Citations16
Published2016
Admission routes2
Has abstractyes

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