Nonfouling biomaterials based on polyethylene oxide‐containing amphiphilic triblock copolymers as surface modifying additives: Protein adsorption on PEO‐copolymer/polyurethane blends
Bibliographic record
Abstract
Surface modification of a segmented polyurethane was achieved by blending with novel PEO-containing amphiphilic triblock copolymers (PEO-polyurethane-PEO). Three copolymers having different PEO MW (550, 2000, 5000) were used as surface modification additives. The protein resistance of the blend surfaces was evaluated using radiolabeling methods. On the blends of copolymers with PEO blocks of MW 2000 and 5000, fibrinogen adsorption from physiologic buffer decreased with increasing copolymer content up to 20 wt%. On the blends with PEO blocks of MW 550, resistance to adsorption for a given copolymer content was much greater. For all three blend types at 20% copolymer content, reductions in adsorption compared to the unmodified PU matrix were greater than 95%. Reductions in adsorption were similar for the 20% blends and surfaces prepared by coating the copolymers directly on the matrix, suggesting that the 20% blend surfaces were completely covered by copolymer. At low copolymer content (< or =10 wt %), fibrinogen adsorption decreased with decreasing PEO block length. This was probably due to increasing surface coverage of the copolymers with decreasing block length. It is therefore concluded that surface density of PEO is more important than PEO MW for the protein resistance of these surfaces. Lysozyme, a much smaller protein, showed adsorption trends similar to fibrinogen. The adsorption of fibrinogen and lysozyme from binary solutions to blends of the copolymer with PEO blocks of 2000 MW was investigated to probe the effects of protein size on adsorption resistance. Fibrinogen and lysozyme showed similar fractional decreases in adsorption relative to the PU matrix independent of the surface density of PEO. However lysozyme was enriched in the surface relative to the solution, that is, it was adsorbed preferentially to fibrinogen.
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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) | 0.001 | 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".