Protein Adsorption To Chemisorbed Polyethylene Oxide Thin Films
Bibliographic record
Abstract
A major area of biomaterials research is the development of surfaces that reduce or eliminate non-specific protein adsorption. End-tethered PEO has been shown to reduce protein and cell interactions at the tissue-material interface; the effects of polymer chain length, chain density and end-group chemistry are not yet completely understood. To date, there have been few detailed, systematic studies that have attempted to elucidate the effect of end-tethered PEO conformation, surface chain density, molecular weight (MW) and end-group chemistry on protein adsorption at the solid-liquid interface. In the research described in this thesis PEOs of varying molecular weight (600, 750, 2000 and 5000 MW) and terminal functional group (-OH, -OCH3) were thiolated and chemisorbed to gold coated silicon wafers for the purpose of characterizing film thickness and surface chain density for direct correlation to protein adsorption behaviour. Tethered chain density was varied by manipulating PEO solubility and chemisorption time, which in principle, should allow for variable, controlled surface chain density from low to very high values. PEO layers were characterized using water contact angles, X-ray photoelectron spectroscopy (XPS), self-nulling ellipsometry and neutron reflectometry (NR). The adsorption of two proteins having widely different molecular weights was examined using radiolabeling and ellipsometry to ascertain the effectiveness of these surfaces in resisting protein adsorption and to provide information about the nature of protein interactions with end-tethered PEO surfaces. These experiments were carried out using 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) via experiments using radiolabeled fibrinogen.
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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".