Study on the surface morphology and wettability of nanocomposite films based on poly(methyl methacrylate) containing polyhedral oligomeric silsesquioxane/silica nanohybrids
Bibliographic record
Abstract
Superhydrophobic behavior was imparted on the surface of poly(methyl methacrylate) (PMMA) films using a hybrid of nanoparticles including polyhedral oligomeric silsesquioxane (POSS) and silica. To this end, an improved phase separation method based on the concurrent use of nonsolvent and nanoparticles was used. In the absence of nanoparticles, scanning electron microscopy (SEM) results revealed the surface segregation of PMMA macromolecules as the ethanol content was increased causing the roughness to be enhanced; however, no superhydrophobic property was attained. SEM and X‐ray photoelectron spectroscopy results demonstrated that POSS particles were be mainly localized at the bulk of the nanocomposite films, and thus, superhydrophobicity could not be achieved. Accordingly, hydrophobic silica nanoparticles were introduced to the formulation leading to the superhydrophobic behavior. The durability of the superhydrophobic samples was investigated by immersing the films into the solutions with different pH values under dynamic conditions. Despite its more initial hydrophobicity, the sample made via the concurrent use of nonsolvent and nanohybrid exhibited a poorer durability, which was attributed to the more porous structure and formation of large cracks at the surface layer of the films. The results of this study could be used as a hint of nanohybrids' efficiency in the fabrication of stable superhydrophobic surfaces. POLYM. COMPOS., 40:E127–E135, 2019. © 2017 Society of Plastics Engineers
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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".