Simultaneous effect of surface roughness and passivity on corrosion resistance of metals
Why this work is in the frame
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Bibliographic record
Abstract
Unidirectional surface roughness of varying magnitudes were created on both nickel and mild steel by grinding on SiC papers with grit sizes from G60 (roughest) to G1200 (smoothest) and the corrosion resistance in 0.5M H 2 SO 4 solution was determined using a potentiodynamic polarization technique. A different trend of corrosion rate versus roughness was seen for the active-passive metal (nickel) and non-active-passive metal (mild steel). For nickel there was an increase in corrosion rate with increasing roughness, whereas for mild steel the corrosion rate decreased with increasing surface roughness. Furthermore, through a detailed examination of the surface before and after corrosion using techniques including profilometry, scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS), it was established that different corrosion mechanisms were operative for nickel and mild steel. For both metals, the smaller grit sizes produced a rougher surface with wider and deeper grooves. In the case of nickel, the higher roughness provided a greater contact area between the corrosive medium and metal and there was trapping of the corrosive ions in the deep grooves. Both of these factors would lead to an increase in corrosion rate. Also, for the smoother nickel surfaces, it is easier to form a stable passive film. For mild steel, which does not form a passive film, corrosion rates are generally much higher than for nickel. For the rougher surfaces with the deeper grooves, the corrosion product, FeSO4, can fill the grooves thereby acting as a barrier to further ingress of the corrosive ions to the un-corroded metal.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 it