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Record W2093707718 · doi:10.2495/mc130171

The effect of different surface topographies on the corrosion behaviour of nickel

2013· article· en· W2093707718 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

VenueWIT transactions on engineering sciences · 2013
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsCorrosionMaterials scienceSurface roughnessMetallurgySurface finishPolarization (electrochemistry)NickelSilicon carbideProfilometerElectrochemistryCarbideComposite materialElectrode

Abstract

fetched live from OpenAlex

The electrochemical and corrosion behaviour of a surface is extremely complicated and depends on various chemical, physical and mechanical factors. In this study the effect of different surface roughnesses on the corrosion resistance of nickel in 0.5 M sulphuric acid was investigated. Open circuit potential, corrosion current density, polarization resistance and corrosion rate were measured for surfaces polished with different grits (120, 240, 400, 600 and 1200) of silicon carbide papers. The surface roughness was measured using a profilometry method both before and after corrosion testing. SEM images were taken and compared for all of the samples before and after corrosion tests. The results showed that surface roughness and surface morphology can considerably change corrosion and corrosion rate. A higher corrosion resistance is obtained for surfaces with lower roughnesses. Finally, the results were compared with specimens where a specific surface patterning was obtained using a laser ablation method.

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 score0.015
Threshold uncertainty score0.279

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 head0.008
GPT teacher head0.215
Teacher spread0.206 · 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