Graft-then-Shrink Polymer Coatings for Localized Surface Plasmon Resonance Active Interfaces
Bibliographic record
Abstract
Increasing the polymer content on biosensors is important to improve sensor function by altering surface properties and increasing the number of capture sites for analytes. Grafting-to methods are often employed but may be limited by insufficient polymer immobilization. Herein, we have utilized Graft-then-Shrink (GtS) to simultaneously increase polymer content on grafting-to surfaces and produce low-cost, local surface plasmon resonance (LSPR) Au biosensors. The biosensors were incorporated within microwell plates, where the translocation of materials across biological barriers can be tracked by visible light absorbance shifts as a platform for biological barrier crossing molecules. Biosensors were constructed by coating a flat Au layer on stretched polystyrene (PS) with thiol-terminated polymers that, upon heating, produced LSPR active wrinkled Au layers with ∼78% greater polymer content and lower water contact angles (WCA; ∼15°) compared to Shrink-then-Graft (StG) controls (∼55°) for PEG 2 MA coatings. To demonstrate translocation detection, 48-well microplates were 3D printed for GtS biosensor incorporation in the presence of a phospholipid bilayer. Using visible light to track LSPR peak shifts, cell penetrating peptides (CPPs) were screened for bilayer translocation and rate kinetics. GtS offers a simple method to increase the polymer content within coatings and an LSPR fabrication platform to track biomolecule translocation.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".