Precision Stealth Nanofibers via PET‐RAFT Polymerisation: Synthesis, Crystallization‐driven Self‐assembly and Cellular Uptake Studies
Bibliographic record
Abstract
Abstract Stealth precision polymer nanofibers show great promise as therapeutic delivery systems. However, existing systems are largely limited to poly(ethylene glycol) (PEG) and suffer from challenging functionalization, hampering their translation. This work develops a modular, easily functionalizable platform for biocompatible stealth nanofibers based on a combination of ring‐opening polymerisation (ROP), photoinduced electron/energy transfer reversible addition–fragmentation chain transfer (PET‐RAFT) polymerisation, and crystallization‐driven self‐assembly (CDSA). Low length‐dispersity poly(fluorenetrimethylenecarbonate)‐b‐poly(N‐(2‐hydroxypropyl) methacrylamide) (PFTMC‐b‐PHPMA) nanofibers may be produced in a single‐step via CDSA, with a length that is dependent on the PHPMA DPn. Separately, living CDSA leads to nanofibers with length control between 30 nm and ca. 700 nm. Incorporation of fluorescein into the PET‐RAFT polymerization results in fluorescent PFTMC‐b‐PHPMA block copolymers that can undergo CDSA, forming fluorescent nanoparticles for preliminary cell studies. PFTMC‐b‐PHPMA nanofibers exhibited minimal toxicity to cells as well as limited cellular association, in line with previous studies on neutral polymer nanofibers. In comparison, PFTMC‐b‐PHPMA nanospheres exhibited no cellular association. These results indicate that the unique shape and core‐crystallinity of PFTMC‐b‐PHPMA nanofibers ideally positions them for use as therapeutic delivery systems. Overall, the results described herein provide the basis for a modular, easily functionalizable platform for precision stealth polymer nanofibers for a variety of prospective biomedical applications.
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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".