Multifunctional Amphiphilic Nanoparticles Featuring (Bio)Degradable Core and Dual‐Responsive Shell as Biomedical Platforms for Controlled Release
Bibliographic record
Abstract
Multifunctional polymeric platforms combining (bio)degradable and biocompatible, temperature and pH‐sensitive entities hold great promise as nanocarriers for targeted drug and gene delivery, and tissue engineering. In this work, preparation and characterization of surfactant‐free polyester nanoparticles (NPs) from biobased polyesters poly(butylene sebacate) (PBSE) and poly(butylene sebacate‐co‐butylene dilinoleate)s (PBSE/PBDL) using nanoprecipitation is reported. This strategy leads to spherical nanosized particles with sizes narrowly distributed in a range of 30–200 nm which is appropriate for internalization by a variety of cells. The effect of molecular parameters and type of solvent used in the nanoprecipitation protocol on the size and shape of produced polyester nanocolloids and their in vitro degradation in PBS solution at 37 °C is elucidated by quasi‐elastic light scattering (QELS), atomic force microscopy (AFM), transmission electron microscopy (TEM), and size‐exclusion chromatography (SEC) techniques. A dense cationic brush layer (≈ 20 nm) of stimuli‐responsive and biocompatible poly(2‐dimethylaminoethyl methacrylate‐co‐acrylonitrile) is grafted on the surface of PBSE/PBDL NPs through “grafting onto” (arm first) coupling chemistry. image
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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".