Insight into the Surface Properties of Fluorocarbon−Vinyl Acetate Copolymer Films and Blends
Bibliographic record
Abstract
Fluorocopolymers of vinyl acetate (VAc) and one of tetrafluoroethylene (TFE), chlorotrifluoroethylene (CTFE), or vinylidene fluoride (VDF), or fluorocopolymer blends with PVAc, were cast from solution on Teflon-coated glass slides and analyzed for surface composition at both the air−polymer and Teflon−polymer surfaces. Since the fluorocopolymers, synthesized in supercritical CO 2, had a random distribution of monomers, we were interested in determining whether surface enrichment of the fluorocarbon repeat units was possible relative to the bulk composition. Film surfaces were characterized by X-ray photoelectron spectroscopy (XPS) and dynamic advancing and receding water contact angles, which demonstrated that fluorocopolymer films were more hydrophobic than PVAc homopolymer film surfaces. By XPS, films of P(TFE- co -VAc) had both air and Teflon surfaces enriched with TFE relative to its bulk composition. Films of P(CTFE- co -VAc) had CTFE-enriched air surfaces for CTFE bulk concentrations >26 mol % and CTFE-enriched Teflon surfaces for all P(CTFE- co -VAc) compositions. Films of P(VDF- co -VAc) had VDF-depleted air surfaces for all P(VDF- co -VAc) bulk compositions yet VDF-enriched Teflon surfaces. Relative to the fluorocopolymers, greater surface activity of the fluorocarbon repeat units was evident for fluorocopolymer blends with PVAc at both air−polymer and Teflon−polymer surfaces for both P(TFE- co -VAc) and P(CTFE- co -VAc). Similar results were obtained for P(VDF- co -VAc) at the Teflon surface. Together these results demonstrate that even random fluorocopolymers can create surfaces enriched with fluorocarbon relative to the bulk composition. The important driving forces for surface activity include surface tension, polarity differences, and chemistry of the “counter” surface. Ultimately, these fluorocopolymers may be useful additives for coatings or paint applications.
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How this classification was reachedexpand
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 | 0.000 | 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 teacher head, 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".