Protein Adsorption and Cell Adhesion/Detachment Behavior on Dual-Responsive Silicon Surfaces Modified with Poly(<i>N</i>-isopropylacrylamide)-<i>block</i>-polystyrene Copolymer
Bibliographic record
Abstract
Diblock copolymer grafts covalently attached to surfaces have attracted considerable attention because of their special structure and novel properties. In this work, poly(N-isopropylacrylamide)-block-polystyrene (PNIPAAm-b-PS) brushes were prepared via surface-initiated consecutive atom-transfer radical polymerization on initiator-immobilized silicon. Because of the inherent thermosensitivity of PNIPAAm and the hydrophobicity difference between the two blocks, the modified surfaces were responsive to both temperature and solvent. Moreover, the diblock copolymer brushes exhibited both resistance to nonspecific protein adsorption and unique cell interaction properties. They showed strong protein resistance in both phosphate-buffered saline and blood plasma. In particular, fibrinogen adsorption from plasma at either room temperature or body temperature was less than 8 ng/cm(2), suggesting that the surfaces might possess good blood compatibility. In addition, the adhesion and detachment of L929 cells could be "tuned", and the ability to control the detachment of cells thermally was restored by block polymerization of hydrophobic, cell-adhesive PS onto a thicker PNIPAAm layer. In addition to providing a simple and effective design for advanced cell-culture surfaces, these results suggest new biomedical applications for PNIPAAm.
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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".