Chemical modifications for water-repellent \ncoatings on engineering metals
Bibliographic record
Abstract
Hydrophobic surfaces have drawn lots of attention for use in applications such as selfcleaning \nsurfaces, anti-icing in harsh environments and corrosion resistance. Generally \nspeaking, the prerequisite for hydrophobic surface synthesis is the combination of \nmicro-scale and nano-scale surface structures along with a low surface energy coating. \nIn my research work, chemical methods were investigated to produce hydrophobic \ncarbon steel and stainless steel, which are important engineering metals. \nSimple chemical etching and organic coatings were applied to carbon steel and \nstainless steel. Although the hydrophobicity of the modified surface increased, the \ndegree of water repellency didn’t reach our expectation. Furthermore, the heterogeneous \netching and coating caused large uncertainty in terms of wettability. The most \npromising system is a zinc electrodeposit with a stearic acid coating. We showed that \nmildly alkaline electrolytes can be used for the fabrication of zinc coatings that give \nrise to remarkably low adhesion surfaces. Various parameters (pH, applied potential, \nelectrolyte composition) during zinc electrodeposition influenced the homogeneity of \nzinc coverage and the topography of zinc crystalites, which consequently impacted \nthe hydrophobicity of the surface. Moreover, the two important roles of stearic acid, \npreventing the oxidation of zinc surface and decreasing the surface energy, were also \nstudied. In conclusion, the zinc layer not only increases the roughness of the surface, \nbut also provide excellent adhesion to the organic coating.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".