Nonfouling biomaterials based on polyethylene oxide‐containing amphiphilic triblock copolymers as surface modifying additives: Synthesis and characterization of copolymers and surface properties of copolymer–polyurethane blends
Bibliographic record
Abstract
Abstract The objective of this work is to develop nonfouling biomaterials by blending polyethylene oxide (PEO)‐containing block copolymers with a polyurethane (PU) matrix; it is expected that the PEO component will migrate to the tissue‐material interface. Three amphiphilic triblock copolymers, PEO‐PU‐PEO, in which the PEO MW was 550 (copolymer 1), 2000 (copolymer 2), and 5000 (copolymer 3), respectively, were synthesized. XPS data showed that the polymer/vacuum interfaces of copolymers 2 and 3 were enriched in the PU block, whereas that of copolymer 1 was enriched in the PEO block. In contact with water, the PEO blocks for all three copolymers migrated to the surface as indicated by water contact angles. Blends of the copolymers with a segmented polyurethane were investigated. Surface enrichment of the copolymers occurred and increased over time up to a limit; the degree of enrichment was dependent on PEO block size and copolymer content. At copolymer content <10%, enrichment decreased with increasing PEO block size. For the copolymer 2 and copolymer 3 blends, enrichment increased with increasing copolymer content; at 20% copolymer the surfaces consisted essentially of pure copolymer. For the copolymer 1 blends, the surface was completely covered by copolymer at content ≥ 1%. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008
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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".