Characterization of protein resistant, grafted methacrylate polymer layers bearing oligo(ethylene glycol) and phosphorylcholine side chains by neutron reflectometry
Bibliographic record
Abstract
Neutron reflectometry was used to investigate the structures of end-tethered protein resistant polymer layers based on poly(oligo(ethylene glycol) methyl ether methacrylate) [poly(OEGMA)] and poly(2-methacryloyloxyethyl phosphorylcholine) [poly(MPC)]. Layers having different graft densities were studied in both the dry and wet states. A stretched parabolic model was used to fit the neutron data, resulting in a one-dimensional scattering length density profile of the polymer volume fraction normal to the film. Measured in D(2)O, the cutoff thicknesses of OEGMA and MPC layers at high graft density (0.39 chainsnm(2) for OEGMA and 0.30 chainsnm(2) for MPC) and a chain length of 200 repeat units were 450 and 470 A, respectively, close to their contour length of 500 A, suggesting that the grafts become highly hydrated when exposed to water. It was also found that at similar graft density and chain length, the volume fraction profiles of poly(OEGMA) and poly(MPC) layers are similar, in line with the authors' previous results showing that these surfaces have similar protein resistance [W. Feng et al., BioInterphases 1, 50 (2006)]. The possible correlation of protein resistance to water content as indicated by the average number of water molecules per ethylene oxide (N(w,EO)) or phosphorylcholine (N(w,PC)) moiety was investigated. N(w,EO) and N(w,PC), estimated from the volume fraction data, increased with decreasing graft density, and when compared to the reported number of water molecules in the hydration layers of EO and PC residues, led to the conclusion that water content slightly greater than the water of hydration resulted in protein resistant surfaces, whereas water content either less than or greatly in excess of the water of hydration resulted in layers of reduced protein resistance.
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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) | 0.000 | 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 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".