Synthesis and Characterization of Gold-Nanoparticle-Loaded Block Copolymer Vectors for Biomedical Applications: A Multivariate Analysis
Bibliographic record
Abstract
Gold nanoparticles (GNPs) encapsulated in amphiphilic block copolymers are a promising system for numerous biomedical applications, although critical information on the effects of various preparation variables on the structure and properties of this unique type of nanomaterial is currently missing from the literature. In this research, we synthesized GNPs functionalized with thiol-terminated polycaprolactone (PCL-GNPs) before encapsulating them into poly(ε-caprolactone)- b -poly(ethylene glycol) (PCL- b -PEG) micellar nanoparticles via nanoprecipitation to yield GNP-loaded polymeric nanoparticles (GNP-PNPs). We explored the role of different manufacturing variables (water volume, PCL- b -PEG to PCL-GNP ratio, and PEG block length) on the sizes, morphologies, GNP occupancies, colloidal gold concentrations, and time stability of GNP-PNPs. Despite our motivation to increase colloidal gold concentrations for K-edge CT imaging applications, there was only moderate variation in the concentration of colloidal gold ( c Au = ∼100–150 μg/mL) over the range of investigated experimental variables; however, postformulation exposure to compressed air flow provided samples with increased gold concentrations and CT contrast above the visual threshold in imaging phantoms. The range of formulation variables also had only a weak effect on mean effective hydrodynamic diameters ( d h,eff = ∼150 nm). Statistical analysis of TEM images revealed that the mean number of GNPs within GNP-PNP vectors smaller than 50 nm, Z ave, d <50 nm, is generally higher for preparations involving PCL- b -PEG with the shorter of the two different PEG block lengths. Preparations with the shorter PEG block length copolymer were also found to produce GNP-PNP colloids with greater time stability in d h,eff and c Au . Consistent with our previous study using MB-MDA-231 cells, we found increased gold uptake in MCF-7 cells with increasing Z ave, d <50 nm . This study provides a roadmap for optimizing important figures of merit for existing biomedical applications, including CT imaging and radiotherapy sensitization, and for developing new diagnostic and therapeutic strategies using GNP-PNPs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".