Preparation of PLA and PLGA Nanoparticles by the Binary Solvent Dispersion Method
Bibliographic record
Abstract
A binary solvent dispersion(BSD) method has been investigated in preparation of nanoparticles of polylactide(PLA) and poly( DL lactide co glycolide)(PLGA) as functions of polymer molecular weight and monomer ratio in copolymer:L/G ratio= m (PLA)/ m (PLGA). The yield, particle size and size distribution of the nanoparticles obtained were examined. Both polymeric nanoparticles could be powderized via lyophilization, leading to the narrow size dirtribution of the particles. The effects of ethanol and acetone on the nanoparticles size and yield have been examined by using of could point titration method. It was found that the yield of nanoparticles increases with increase of ethanol in mixture with acetone and attains a maximum value near the cloud point, whereas the size of nanoparticles decreases with increases of ethanol in the solution with acetone and attains a minimum value near the cloud point. Therefore, the optimum condition for gaining desired yield and size of polymeric particles could be derived form the cloud point experimentation. The yield of nanoparticles was up to 90% with the average size being ranged in 130～180 nm for both PLA and PLGA.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".