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Record W2912627123 · doi:10.3390/ma12030503

Corrosion Characteristics of Copper-Added Austempered Gray Cast Iron (AGCI)

2019· article· en· W2912627123 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMaterials · 2019
Typearticle
Languageen
FieldEngineering
TopicNon-Destructive Testing Techniques
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMetallurgyCast ironMaterials scienceCopperCorrosionGray ironAustemperingMicrostructureBainiteMartensite

Abstract

fetched live from OpenAlex

The aim of this investigation was to assess the corrosion behavior of gray cast iron (GCI) alloyed with copper. Alloyed GCI specimens were austempered isothermally at varying temperatures. After austenitizing at 927 °C, the samples were austempered at different temperatures ranging from 260 to 385 °C with an interval of 25 °C for 60 minutes. As a result, these samples developed an ausferrite matrix with different percentages of austenite. The resulting microstructures were evaluated and characterized by optical microscope (OM), scanning electron microscope (SEM), and X-ray diffraction (XRD). The corrosion characteristics were determined using potentiodynamic polarization tests and electrochemical impedance spectroscopy (EIS) of these samples. These tests were carried out in a medium of 0.5 M H2SO4 and 3.5% NaCl solution. It was observed from the potentiodynamic polarization results that with increasing austempering temperature, the corrosion rate decreased. All results of the EIS were in accordance with a constant phase element (CPE) model. It was found that with an increase in austempering temperature, the polarization resistance (Rp) increased. The austenite content was also found to influence the corrosion behavior of the austempered gray cast iron (AGCI).

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.

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 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 score0.021
Threshold uncertainty score0.706

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)0.0000.000

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 head0.011
GPT teacher head0.221
Teacher spread0.210 · 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