Improving corrosion performance by surface patterning
Bibliographic record
Abstract
Based on the idea that hydrophobic (low or non-wettable) surfaces can decrease the contact area between a corrosive solution and a surface, thereby potentially rendering the material more corrosion resistant, the effect of surface patterning on the corrosion behaviour of nickel was investigated.The surface patterning consisted of an array of holes of various diameters (D) and inter-hole spacings (L) that were produced by a laser ablation process.The corrosion behaviour of the patterned surfaces was studied using a potentiodynamic polarization method in a 0.5M H 2 SO 4 electrolyte and compared with that of a polished reference sample.Following the potentiodynamic polarization corrosion test cycle, the corroded surfaces were examined using scanning electron microscopy (SEM) for morphological features and white light interferometry (WLI) to determine the surface roughness.The changes in surface morphology were related to the corrosion behaviour.A relationship was found between D, L, and the corrosion current density (I corr ), whereby the higher the (D/L ) 2 ratio, the higher the I corr value.The corrosion potential (E corr ) of all surface patterned samples was lower (less noble) than that of the reference sample in all tests.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
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".