Universal Bioinert Control of Polystyrene Interfaces via Hydrophobic‐Driven Self‐Assembled Surface PEGylation with a Well‐Defined Block Sequence
Bibliographic record
Abstract
This work proposes a new molecular insight of interfacial design in the control of antifouling performance for the versatile biofoulants, including proteins, blood cells, tissue cells, and bacteria. A self‐assembled bioinert interface with universal fouling resistance to general biofoulants via hydrophobic‐driven surface PEGylation is presented. The study systematically discriminates the optimum PEGylated block polymer configuration and hydrophobic/hydrophilic segmental ratio enabling to optimize the surface coverage by the bioinert moieties, thus ensuring the best resistance to biofouling. For similar copolymer molecular weights and similar polystyrene (PS)/poly(ethylene glycol) methacrylate (PEGMA), the coating density obtainable is the highest if a random copolymer is used, while it is the lowest with a triblock copolymer. That measured with a diblock copolymer lies in between. Random copolymers offer more numerous anchoring possibilities than diblock copolymers, while they are importantly fewer if triblock copolymers are used. For similar total number of hydrophilic blocks, the diblock copolymer is more efficient to resist larger cells (leukocytes, fibroblasts) while the triblock is better to promote mitigate biofouling by smaller molecules or cells (proteins, platelets, red blood cells). The length of the hydrophilic PEGylated block seems to dominate fouling resistance of large biofoulants.
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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".