Protein Adsorption To Chemisorbed Polyethylene Oxide Thin Films
Bibliographic record
Abstract
A major area of biomaterials research is the development of surfaces that reduce \nor eliminate non-specific protein adsorption. End-tethered PEO has been shown to reduce \nprotein and cell interactions at the tissue-material interface; the effects of polymer chain \nlength, chain density and end-group chemistry are not yet completely understood. To \ndate, there have been few detailed, systematic studies that have attempted to elucidate the \neffect of end-tethered PEO conformation, surface chain density, molecular weight (MW) \nand end-group chemistry on protein adsorption at the solid-liquid interface. \nIn the research described in this thesis PEOs of varying molecular weight (600, \n750, 2000 and 5000 MW) and terminal functional group (-OH, -OCH3) were thiolated \nand chemisorbed to gold coated silicon wafers for the purpose of characterizing film \nthickness and surface chain density for direct correlation to protein adsorption behaviour. \nTethered chain density was varied by manipulating PEO solubility and chemisorption \ntime, which in principle, should allow for variable, controlled surface chain density from \nlow to very high values. PEO layers were characterized using water contact angles, X-ray \nphotoelectron spectroscopy (XPS), self-nulling ellipsometry and neutron reflectometry \n(NR). The adsorption of two proteins having widely different molecular weights was \nexamined using radiolabeling and ellipsometry to ascertain the effectiveness of these \nsurfaces in resisting protein adsorption and to provide information about the nature of \nprotein interactions with end-tethered PEO surfaces. These experiments were carried out \nusing single or binary protein solutions in buffer. Adsorption from plasma was also investigated: (1) by Western Blot analysis of the proteins eluted after plasma contact; (2) \nvia experiments using radiolabeled fibrinogen.
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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 | 0.000 | 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) | 0.003 | 0.009 |
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; both teacher heads agree on what is shown here.
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".