New Electrospun Polystyrene/Al2O3 Nanocomposite Superhydrophobic Coatings; Synthesis, Characterization, and Application
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 | 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 | 0.001 |
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