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On the Capability of In-Situ Exposure in Scanning and Auger Electron Spectroscopy for Investigating Corrosion Property of Engineering Alloys

2016· article· en· W2611063019 on OpenAlexaff
Aezeden Mohamed

Bibliographic record

VenueInnovations in Corrosion and Materials Science (Formerly Recent Patents on Corrosion Science) · 2016
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsAuger electron spectroscopyScanning electron microscopeMaterials scienceCorrosionPitting corrosionOxideMetallurgyAlloyPolarization (electrochemistry)Hydrochloric acidComposite materialChemistry

Abstract

fetched live from OpenAlex

Background: Potentiodynamic polarization experiments were performed on Alloys IN601 and C22 to evaluate their susceptibility to localized corrosion in hydrochloric acid (HCl) at pH2. The tested specimens were evaluated by auger electron spectroscopy (AES) and scanning microscopy (SEM). Nickel chromium alloys consist of surface oxide layers and corrosion products with metallic materials beneath. Methods: Electrochemical techniques are used to examine the pitting tendencies of specimens of IN601 and C22 immersed in a concentrated hydrochloric acid solution (pH2). Techniques include potentiodynamic anodic polarization to determine the active-passive characteristics of alloys and scanning, X-ray, and Auger electron spectroscopy to characterize specimen surfaces in terms of pitting morphologies and for surface analysis of oxide layers. Results: This paper examines the effect of HCl solution on the pitting behaviour of these alloys. Results show that IN601 alloy was characterized by more pitting than C22. Conclusion: There are deeper oxide layers and corrosion products formed on sample surfaces of alloys C22 than in IN601. Furthermore, C22 exhibits a smaller hysteresis loop than IN601, thereby indicating that it has no pitting corrosion in concentrated HCl than IN601. Keywords: Analysis, corrosion, C22, IN601, morphology, oxide layer, pits.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 head0.038
GPT teacher head0.291
Teacher spread0.253 · 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 source (direct Gemma or distilled Codex), 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".

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Citations1
Published2016
Admission routes1
Has abstractyes

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Same venueInnovations in Corrosion and Materials Science (Formerly Recent Patents on Corrosion Science)Same topicCorrosion Behavior and InhibitionFrench-language works237,207