Eco-friendly Superhydrophobic MWCNTs/PTFE/PEEK Powder Coating for Stable Superhydrophobicity in Extreme Environments
Bibliographic record
Abstract
Superhydrophobic coatings hold broad application potential but face persistent challenges in thermal protection and mechanical robustness. Conventional fabrication methods further suffer from environmental and economic inefficiency associated with the use of organic solvents and complex equipment. Herein, a ternary nano/microintegrated composite of polyether–ether–ketone, polytetrafluoroethylene, and multiwalled carbon nanotubes was rationally designed and prepared via pressure-bonding technology. And thus, an eco-friendly superhydrophobic powder coating with exceptional thermal stability and long-term durability is successfully reported (WCA of 163.78°, WSA of 1.3°). This strategy addresses the inherent limitations associated with conventional melt-extrusion and direct-blending methods, such as thermal dispersion, homogenization, pulverization, and compatibility issues, thereby achieving a synergistic enhancement of material properties. The superhydrophobic coating demonstrates superior resistance to both cold and hot liquids. Also, it exhibits exceptional thermal stability (up to 400 °C) with simultaneous mechanical reinforcement, where the intertwined structure achieves high enhancement in abrasion resistance compared to direct-blended coatings. The chemically inert and rough surface also offers resistance to aggressive inorganic/organic solvents. Long-term environmental durability is evidenced by maintaining >150° WCA after 60 days of UV-accelerated aging. The coating’s stability under thermal, mechanical, and chemical stresses allows it to outperform conventional systems, offering a facile and innovative solution for surface thermal protection in extreme conditions.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".