Modified Salt Spray Test to Evaluate Zinc Electroplating Coating with Co-deposited Natural Particles
Bibliographic record
Abstract
Extended Abstract Among the electroplating techniques, the co-deposition of non-metallic particles has become a prominent alternative due their specific properties. The use of natural compounds from agro-industrial residues, such as avocado seed powder (ASP) and garlic peel powder (GPP), in the electroplating bath can improve corrosion resistance, change roughness profile, and modify the coating wettability. This work aims to evaluate the influence of different concentration of agro-industrial residues co-deposited in the zinc coating employing salt spray test (SST), adapting ASTM B117. Scanning electron microscope (SEM) images were used to characterize the morphology of deposits and it was correlated with salt spray test (SST) results. The coated samples have been monitored every hour until the 8th hour, finishing after 24 h. Different concentrations of ASP and GPP (0.060 g/L, 0.330 g/L, and 0.600 g/L) were evaluated and compared to the coating without natural particles. In the first hours of exposure, the samples began to show corrosion points characteristic of zinc coating. ASP samples did not resist as much as GPP samples after 4 h of exposure. The GPP samples were almost intact after 8 h of exposure. After 24 h, all samples showed generalized corrosion. The GPP samples presented the most homogeneous, compact, brighter, and refined grain deposits by SEM images analysis. This procedure was sensitive enough to indicate that the 0.330 g/L GPP sample showed greater corrosion resistance.
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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) | 0.002 | 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".