The effect of fluorinated surface modifying macromolecules on the surface morphology of polyethersulfone membranes
Bibliographic record
Abstract
Polyethersulfone (PES) has been recently adopted for membrane materials in applications such as ultrafiltration and haemodialysis. As a biomaterial, the factors which affect the blood compatibility of PES membranes include surface energetics, hydrophobicity, and surface morphology. Surface fluorination of materials has been found to create surfaces with improved blood compatibility and chemical stability. One novel approach to generating fluorinated polymer surfaces has included the use of fluorinated surface modifying macromolecules (SMMs). These macromolecules have been reported to establish fluorinated functional groups at surfaces of polymeric materials without significantly affecting the physical properties of the base polymer. However, to date there has been relatively little information published on the nature of the surface structure for PES materials containing these SMMs. In this study, synthesized SMMs with varying chemical compositions were characterized and blended with PES, and fabricated into flat sheet membranes. The bulk thermal transitions of PES materials were not significantly altered by the addition of 4 wt% SMMs. Contact angle data showed that the addition of SMMs in PES created more hydrophobic surfaces, accompanied by an increase in surface heterogeneity. X-ray photoelectron spectroscopy studies confirmed the presence of elemental fluorine at the surface. Through microscopy studies, it was shown that surface modification was achieved by the migration of SMM concentrated microdomains to the air-membrane interface. The generated microdomains (approximately 1-2 microm in diameter) are dispersed within the top 8 microm of the surface. The concentration of microdomains was gradually depleted from the surface to the bulk of the membrane. A schematic of the morphology for SMMs within the PES membrane surface was proposed.
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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".