Scalable and Uniform Length-Tunable Biodegradable Block Copolymer Nanofibers with a Polycarbonate Core via Living Polymerization-Induced Crystallization-Driven Self-assembly
Bibliographic record
Abstract
Uniform 1D block copolymer (BCP) nanofibers prepared by the seeded-growth approach termed living crystallization-driven self-assembly (CDSA) offer promising potential for various applications due to their anisotropy, length tunability, and variable core and coronal chemistries. However, this procedure consists of a multi-step process involving independent BCP synthesis and self-assembly steps, where the latter is performed at low solution concentrations (<1 wt %), hindering scale-up. Here, we demonstrate the use of a one-pot BCP synthesis and self-assembly process, polymerization-induced CDSA (PI-CDSA), to access length-disperse nanofibers with a biodegradable crystalline poly(fluorenetrimethylenecarbonate) (PFTMC) core and a hydrophilic poly(ethylene glycol) (PEG) corona derived from PEG- b -PFTMC at concentrations up to 20 wt %, 400 times higher than those previously reported. Furthermore, living PI-CDSA could be used to access scalable, low dispersity, and length-tunable 1D PEG- b -PFTMC nanofibers at concentrations of up to 10 wt %. This provides the first example of living PI-CDSA involving an all-organic and biodegradable BCP that utilizes a conveniently implemented BCP synthesis protocol and does not involve living anionic polymerization. Significantly, samples of low-dispersity nanofibers of controlled lengths from 100 to 660 nm ( L w / L n = 1.08–1.20) were prepared, allowing for upscaled access to well-defined biodegradable nanofibers at useful length-scales for applications in nanomedicine. Interestingly, detailed studies revealed a key role for PFTMC homopolymer impurities in the BCP prepared in situ in the formation of nanofibers under the reaction conditions used.
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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".