New Electrospun Polystyrene/Al2O3 Nanocomposite Superhydrophobic Coatings; Synthesis, Characterization, and Application
Bibliographic record
Abstract
The effect of electrospinning operational parameters on the morphology, surface roughness, and wettability of different compositions of electrospun polystyrene (PS)–aluminum oxide (Al2O3) nanocomposite coatings was investigated using different techniques. For example, a scanning electron microscope (SEM) coupled with an energy dispersive X-ray (EDX) unit, a Fourier transform infrared (FTIR) spectrometer, an atomic force microscope (AFM), and water contact angle (WCA), and contact angle hysteresis (CAH) measurements using the sessile droplet method, were used. The latter used 4 µL of distilled water at room temperature. PS/Al2O3 nanocomposite coatings exhibited different morphologies, such as beaded fibers and microfibers, depending on the concentration ratio between the PS and Al2O3 nanoparticles and the operational parameters of the electrospinning process. The optimum conditions to produce a nanocomposite coating with the highest roughness and superhydrophobic properties (155° ± 1.9° for WCA and 3° ± 4.2° for CAH) are 2.5 and 0.25 wt % of PS and Al2O3, respectively, 25 kV for the applied potential and 1.5 mL·h−1 for the solution flow rate at 35 °C. The corrosion resistance of the as-prepared coatings was investigated using the electrochemical impedance spectroscopy (EIS) technique. The results have revealed that the highly porous superhydrophobic nanocomposite coatings (SHCs) possess a superior corrosion resistance that is higher than the uncoated Al alloy by three orders of magnitude.
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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) | 0.001 | 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".