Advanced superhydrophobic and wear-resistant coatings on carbon steel
Bibliographic record
Abstract
Purpose This study aims to develop a dual-action coating combining superhydrophobicity and wear-resistance on carbon steel. Design/methodology/approach Superhydrophobic coatings were fabricated by blending perfluorodecyltrimethoxysilane (FAS)-modified nanoparticles (SiO2, TiO2, ZrO2) with silane coupling agent-modified epoxy resin (Ep-51). Key parameters – epoxy content, nanoparticle type and silane agent (KH-540, KH-550, KH-560) – were optimized. Coatings were characterized via scanning electron microscopy, atomic force microscopy, FT-IR, contact angle measurements, pencil hardness tests and abrasion resistance evaluations. Findings The optimal coating (FAS-SiO2:KH-560-modified Ep-51 = 10:4) exhibited outstanding superhydrophobicity (153°), high hardness (6H) and superior abrasion resistance. Increased Ep-51 content enhanced hardness but reduced hydrophobicity due to nanoparticle encapsulation. SiO2 outperformed TiO2/ZrO2 in hydrophobicity, while KH-560 provided optimal mechanical–hydrophobic balance. The coating maintained functionality after abrasion. Originality/value This work presents a novel sprayable nanocomposite coating integrating superhydrophobicity and robust mechanical durability. The dual-layer design offers a scalable solution for long-term protection in industrial applications.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".