Development of Dual-Phobic Surfaces: Superamphiphobicity in Air and Oleophobicity Underwater
Bibliographic record
Abstract
In the present work, we describe a simple method to fabricate dual-phobic fluorosilane-coated polydimethylsiloxane/camphor-soot/polydimethylsiloxane (FPCP) composite surfaces. The surface morphology and silane treatment provide the needed texture on the FPCP composite surface to demonstrate superamphiphobicity in the air and oleophobicity in the underwater environment. High-resolution field emission-scanning electron microscopy (FESEM) imaging of the FPCP composite surface illustrates the top surface with an array of hollow cylindrical pillars. The dimensions of surface texture are measured, and the relationship between the wetting states of liquid and textures surface in the air as well as in an underwater environment is studied. Also, X-ray photoelectron spectroscopy (XPS) analysis demonstrates different changes of plain polydimethylsiloxane (PDMS), PDMS/camphor-soot/PDMS (PCP), and fluorosilane-coated PCP (FPCP) composite surfaces that are responsible for diverse wettability properties. We compared the experimentally observed equilibrium and dynamics contact angles of water and different oils on the FPCP composite surface in air and underwater system with those predicted by theoretical models. The results reported herein provide a new feasible method for the fabrication of dual-phobic surfaces (superamphiphobicity in air and oleophobicity underwater) with microtextures. The findings also improve the understanding of the complex relations between surface microstructure and wetting states. The fundamental understanding of dual-phobicity of FPCP composites is an important step toward the designing of optimal anti-icing surfaces for practical engineering applications. Such coatings with incorporated functionalities provide promising self-cleaning and anticorrosion applications under erosive/abrasive environment.
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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) | 0.000 | 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 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".