Protein resistance of surfaces prepared by sorption of end-thiolated poly(ethylene glycol) to gold: effect of surface chain density
Bibliographic record
Abstract
Nonspecific protein adsorption generally occurs at the biomaterial-tissue interface and usually has adverse consequences. Thus, surfaces that are protein-resistant are eagerly sought with the expectation that these materials will exhibit improved biocompatibility. Surfaces modified with end-tethered poly(ethylene oxide) (PEO) have been shown to be protein-resistant to some degree. Although the mechanisms are unclear, it has been suggested that chain length, chain density, and chain conformation are important factors. To investigate the effects of PEO chain density, we selected a model system based on the chemisorption of chain-end thiolated PEO to a gold substrate. Chain density was varied by varying PEO solubility (proximity to cloud point) and incubation time in the chemisorption solution. The adsorption of fibrinogen and lysozyme to these surfaces was investigated. It was found that for 750 and 2000 MW PEO layers, resistance to fibrinogen increased with chain density and was maximal at a density of ~0.5 chains/nm2 (80% decrease in adsorption compared to unmodified gold). As PEO chain density increased beyond 0.5/nm2 adsorption increased. For PEO of 5000 MW the optimal chain density was 0.27/nm2 and gave only a 60% reduction in fibrinogen adsorption. It is suggested that, at high chain density, the chemisorbed PEO is dehydrated giving a surface that is no longer protein resistant. The PEO-modified surfaces were found also to be resistant to lysozyme adsorption with reductions similar to, if somewhat less than, those for fibrinogen. The fibrinogen to lysozyme molar ratios were within the expected range for close-packed layers of these proteins in their native conformation and were relatively insensitive to PEO chain density and MW. This may suggest that such adsorption as did occur, even at chain densities giving minimum adsorption, may have been on patches of unmodified gold.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".