Poly(oligo(ethylene glycol)acrylamide) Brushes by Surface Initiated Polymerization: Effect of Macromonomer Chain Length on Brush Growth and Protein Adsorption from Blood Plasma
Bibliographic record
Abstract
Three hydrolytically stable polyethyleneglycol (PEG)-based N-substituted acrylamide macromonomers, methoxypolyethyleneglycol (350) acrylamide (MPEG350Am) methoxypolyethyleneglycol (750) acrylamide(MPEG750Am) and methoxypolyethyleneglycol (2000)acrylamide (MPEG2000Am) with increasing PEG chain length were synthesized. Surface-initiated aqueous atom transfer radical polymerization (ATRP) using CuCl/1,1,4,7,10,10-hexamethyl triethylene tetramine (HMTETA) catalyst was utilized to generate dense polymer brushes from these monomers via an ester linker group on the surface of model polystyrene (PS) particles. The molecular weight, hydrodynamic thickness, and graft densities of the grafted polymer layers were controlled by changing the reaction parameters of monomer concentration, addition of Cu(II)Cl2, and sodium chloride. The graft densities of surface-grafted brushes decreased with increasing PEG macromonomer chain length, 350 > 750 >> 2000, under similar experimental conditions. The molecular weight of grafts increased with increase in monomer concentration, and only selected conditions produced narrow distributed polymer chains. The molecular weight of grafted polymer chains differs significantly to those formed in solution. The hydrodynamic thicknesses of the grafted polymer layers were fitted to the Daoud and Cotton model (DCM) for brush height on spherical surfaces. The results show that the size of the pendent groups on the polymer chains has a profound effect on the hydrodynamic thickness of the brush for a given degree of polymerization. The new PEG-based surfaces show good protection against nonspecific protein adsorption from blood plasma compared to the bare surface. Protein adsorption decreased with increasing surface density of grafted polymer chains. Poly(MPEG750Am) brushes were more effective in preventing protein adsorption than poly(MPEG350Am) even at low graft densities, presumably due to the increase in PEG content in the grafted layer.
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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".