The Microstructural Characterization of ElectrogalvanizedZinc–Iron and Zinc–Nickel Coatings
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".