Self‐Regenerating Antimicrobial Polymer Surfaces via Multilayer‐Design—Sequential and Triggered Layer Shedding under Physiological Conditions
Bibliographic record
Abstract
Regeneration of materials properties through surface regeneration could extend the lifetime of devices and is still an emerging field of research. (Self-)regenerating antimicrobial polymer surfaces could help to fight biofilm formation and related bacterial infections. In this paper, four different polymer multilayer designs for the regeneration of antimicrobial surfaces by layer shedding are presented. The multilayer architectures consist of 100-200 nm thick, discrete polymer layers. They are made from poly(guanidinium oxanorbornene) networks as the antimicrobial component, and different interlayers made from degradable poly(adipic anhydrides), depolymerizable poly(ethyl glyoxylate), or water-soluble poly(acrylamide). Layer shedding is designed to occur after hydrolysis, dissolution or depolymerization under simulated physiological conditions. The multilayer fabrication and disassembly is monitored by fluorescence microscopy, ellipsometry FT-IR spectroscopy and atomic force microscopy. By testing the antimicrobial activity of the restored surfaces, their functional integrity after layer shedding is confirmed.
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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".