Protein-Resistant Poly(ethylene oxide)-Grafted Surfaces: Chain Density-Dependent Multiple Mechanisms of Action
Bibliographic record
Abstract
A clear understanding of the mechanisms responsible for the protein-resistant nature of end-tethered poly(ethylene oxide) (PEO) surfaces remains elusive. A barrier to improved understanding is the fact that many of the factors involved (chain length, chain density, hydration, conformation, and distal chemistry) are inherently correlated. We hypothesize that, by comparing systems of variable but precisely known chain density, it should be possible to gain additional insight into the effects of the other factors. To evaluate this hypothesis, chain-end-thiolated PEOs were chemisorbed to gold-coated silicon wafers such that a range of chain densities was obtained. Three different PEOs were investigated: hydroxy-terminated chains of molecular weight 600 (600-OH), methoxy-terminated chains of molecular weight 750 (750-OCH3), and methoxy-terminated chains of molecular weight 2000 (2000-OCH3). In situ null ellipsometry was used to determine PEO chemisorption kinetics, ultimate PEO chain densities, protein adsorption kinetics, and ultimate protein adsorbed quantities. With this approach, it was possible to ascertain the effects of PEO distal chemistry (-OH, -OCH3), chain length, and layer hydration on protein adsorption. The data obtained suggested that properties related to chain density (conformational freedom, hydration) were the main determinants of protein resistance at chain densities up to a critical value of approximately 0.5 chain/nm2; at this value, protein adsorption was a minimum for the methoxy-terminated PEOs. For the hydroxyl-terminated PEO, adsorption leveled off at the critical value. Thus distal chemistry appears to be a major determinant of protein resistance at chain densities greater than the critical value.
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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 | 0.001 |
| 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".