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Record W2014841405 · doi:10.1155/tsm.34.119

The Microstructural Characterization of ElectrogalvanizedZinc–Iron and Zinc–Nickel Coatings

2000· article· en· W2014841405 on OpenAlexaff
H. Park, Jerzy A. Szpunar

Bibliographic record

VenueTexture Stress and Microstructure · 2000
Typearticle
Languageen
FieldEngineering
TopicTribology and Wear Analysis
Canadian institutionsMcGill University
Fundersnot available
KeywordsZincNickelMaterials scienceCharacterization (materials science)MetallurgyNanotechnology

Abstract

fetched live from OpenAlex

In this study, the development of the microstructure of zinc–iron and zinc–nickel coatings on steel sheet electrodeposited at various deposition conditions, is analyzed. The evolution of the coating microstructure – texture, surface morphology, crystallographic lattice, microstress, alloy composition and phase of coatings – is investigated at different current densities and deposition times. The microstructure of zinc alloy coatings varies significantly with the change of deposition parameters. The present study proposes that the transformarion of texture during the alloy codeposition is closely related to the morphological characteristics of the coating surface and the change of alloy and phase composition of the deposit. Anomalous codeposition behavior, is considered responsible for the change or transformation of the microstructure of coatings. The texture of zinc–iron coatings changes from the combined texture of the basal fiber and non‐fiber pyramidal component to the pyramidal fiber texture with the current density increase. In the case of zinc–nickel coatings, the intensity of the {101} texture of γ phase increases with increasing nickel content, while the intensity of the {100} texture decreases. A correlation between the texture, morphology and alloy content in the deposits is discussed.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.551
Threshold uncertainty score0.487

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 head0.001
GPT teacher head0.176
Teacher spread0.175 · 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.

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".

Quick stats

Citations2
Published2000
Admission routes1
Has abstractyes

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