Protein Resistance of PEG-Functionalized Dendronized Surfaces: Effect of PEG Molecular Weight and Dendron Generation
Bibliographic record
Abstract
Dendronized surfaces were prepared by chemisorption of poly(ethylene glycol) monothiol (HS-PEG 650 -OH) onto gold-coated silicon wafers followed by functionalization of the PEG terminal OH group with aliphatic polyester dendrons, generation 1−4, using divergent dendron growth. PEG monomethyl ether (PEG-OMe) chains of various molecular weight (MW) were covalently attached to the peripheral hydroxyl groups of the dendronized surfaces via EDC coupling and investigated for protein adsorption. Protein adsorption studies were carried out using fibrinogen (Fg) and lysozyme (Lys) as model proteins from phosphate buffered saline (PBS) (Fg, Lys) and plasma (Fg). In the first part of this study, the effect of functionalization of the peripheral hydroxyl groups with PEG-OMe oligomers ( M n = 350 Da) on protein adsorption was investigated. Results showed that adsorption of both Fg and Lys was reduced when dendronized surfaces were grafted with PEG-OMe oligomers. To investigate the effect of molecular weight on protein adsorption, PEG-OMe chains of greater length (750, 2000, and 5000 Da) were coupled to first generation dendronized surfaces (Au-G1(OH)). Results showed that protein adsorption decreased with increasing PEG-OMe MW up to 2000 Da. To further investigate the effect of dendron generation on protein resistance, dendronized surfaces of generation 1−4 were coupled with PEG 2000 -acid. Subsequent protein studies showed a decrease in Fg and Lys adsorption with increasing dendron generation.
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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 | 0.001 |
| 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 | 0.001 | 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".