Development of Dual-Phobic Surfaces: Superamphiphobicity in Air and Oleophobicity Underwater
Why this work is in the frame
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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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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 | 0.001 | 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 it