Study on surface modification by surface‐modifying macromolecules and its applications in membrane‐separation processes
Bibliographic record
Abstract
Abstract Surface‐modifying macromolecules (SMMs) are oligomeric fluoropolymers synthesized by polyurethane chemistry and tailored with fluorinated end groups. In the literature, several formulations of SMMs have been developed and blended with base polymers of polyurethanes and polyethersulfone for surface modification. It has been shown that SMMs migrate to the surface and the fluorine end groups orient themselves toward the air–polymer interface, reducing the surface energy of the hydrophilic base polymer to values close to that of polytetrafluoroethylene (Teflon). Because only a small amount of SMMs was needed, the bulk properties of the base polymer remained relatively unchanged. The properties of the SMM polymers were characterized, including molecular weights, elemental analysis, and thermal transitions. The morphology and surface properties of the SMM‐modified and unmodified membranes were assessed. The use of SMMs has been tested for use in ultrafiltration, pervaporation, and biomedical applications. SMM‐modified membranes offer advantages over unmodified membranes and the use of SMMs will continue to be the focus of future studies. This study reviews the recent development of surface‐modifying macromolecules (SMMs) and SMM‐blended membranes. © 2003 Wiley Periodicals, Inc. J Appl Polym Sci 89: 2902–2916, 2003
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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".