Hydrophobic and Electrostatic Interactions in the Adsorption of Fibronectin at Maleic Acid Copolymer Films
Bibliographic record
Abstract
Adsorption and desorption of fibronectin (FN) were investigated at thin films of alternating maleic acid copolymers with octadecene (POMA) and with propene (PPMA). The hydrophobicity and charge density of the polymers were modulated by the choice of the comonomer. In consequence, the dominant forces between the substrate and the protein were specified as hydrophobic interaction for POMA and electrostatic interaction for PPMA. The adsorption kinetics were investigated in situ as variations of the optical thickness, adsorbed mass, and viscoelastic properties (detected by reflectometric interference spectroscopy and quartz crystal microbalance technique, respectively) while alterations of the electrosurface properties were derived from surface conductivity data and isoelectric points (by streaming potential/current measurements using a microslit electrokinetic setup). The results demonstrate that the interfacial mode of adsorbed FN depends on the predominant interactions: large amounts of FN were tightly bound to POMA by hydrophobic interactions. In contrast, FN adsorbed on PPMA was concluded to attain an unfolded structure allowing for the "electrostatic matching" of positively charged residues on FN with the maleic acid groups. This conclusion was supported by the acidic IEP of 3.2 found for FN on PPMA and a significant reduction of the surface conductivity of the FN-covered polymer film, whereas FN on POMA showed an IEP of 4.2 (close to the intrinsic IEP of FN), indicating a stochastic orientation of the adsorbed protein.
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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".