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Record W2736803559 · doi:10.5006/c2013-02315

Investigation of Corrosion Behavior of Wrought Co-Cr-W Super Alloys

2013· article· en· W2736803559 on OpenAlexaff
X.Z. Zhang, K. Y. Chen, Rong Liu, Matthew Yao

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicMetal Alloys Wear and Properties
Canadian institutionsNational Research Council CanadaCarleton University
Fundersnot available
KeywordsMaterials scienceCorrosionMetallurgy

Abstract

fetched live from OpenAlex

Abstract The corrosion behavior of two wrought Co-Cr-W superalloys is studied under both polarization immersion tests. The corrosive media, Green Death solution, is used in both tests. Potentiodynamic polarization and cyclic polarization testes are performed to investigate general and localized corrosion resistance of these alloys. Immersion tests of the two alloys are conducted in Green Death solution to determine Critical Pitting Temperature (CPT), mass loss, thickness change and the Extreme Value (minimum thickness) using Extreme Value Analysis (EVA) model derived from the Gumbel Distribution. A Scanning Electron Microscope (SEM) with Energy Dispersive X-ray (EDX) spectrum is utilized to analyze the chemical composition of the corrosion products (pits). The presence of carbides generates potential in the electrochemical reaction, causing corrosion of the alloys in the solution. The larger the carbide volume fraction, the more the pits are formed in the alloy. Carbide size also affects maximum pit depths; the larger the carbide size, the bigger and deeper the pits. The EDX analysis results of pits show large amount of oxygen in the carbide phase and a small amount of oxygen in the solid solution phase. The Cr-rich carbides react with oxygen forming Cr-rich carbonates which are easily brittle, loose and broken.

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 score0.010
Threshold uncertainty score0.993

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)0.0080.001

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 head0.039
GPT teacher head0.249
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

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

Citations3
Published2013
Admission routes1
Has abstractyes

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