The Relationship Between Surface Roughness and Corrosion
Bibliographic record
Abstract
There are different parameters which can affect electrochemical reactions such as type of electrolyte, velocity, temperature, oxidizing agents, impurities, anode material type and surface treatment. It has been shown that pre-treatment of working electrode (anode) through abrasion techniques is one of the most important parameters affecting on Tafel slopes and consequently corrosion rate. Surface roughness of the metal surface is a major influence on general corrosion, nucleation of metastable pitting and pitting potential as well. In this study different surface roughnesses were created on nickel surface by SiC papers and corrosion properties were compared. Electrochemical impedance spectroscopy (EIS) and profilometry tests were carried out on all the samples and the results were compared with another sample prepared through laser ablation method. Corrosion rate values were calculated and were compared with EIS results for all the samples and a trend in the effect of roughness on corrosion protection of nickel was introduced. SEM and 3D roughness images were taken and compared for all of the samples before and after corrosion tests. Different mechanisms were distinguished for samples created through different methods. The lower the roughness values, the more the corrosion resistance. Sample with patterns created through laser ablation method showed the best protection properties compared to other samples.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".