Formation of organic molecular nanocrystals under soft confinement
Bibliographic record
Abstract
Methods to produce nano-sized organic molecular crystals in thin films are of great interest in the pharmaceutical industry due to the potential benefit of increased solubility of poorly soluble drugs and the advantages of film-based dosage forms over traditional tablet/capsule-based dosage form. One method to directly form organic nanocrystals is by crystallization in confined environments where the overall crystallization volume is constrained. We report the use of a novel solution impregnation method to form nanocrystals in polymer matrices with various microstructures in order to study the structure of the confined nanocrystals and the role of soft confinement and polymer chemistry on the nucleation process of nano-sized crystals. The particle diameter correlates with the microstructure of the polymer matrices and the nucleation kinetics. In addition, by carefully choosing the experimental conditions and the polymer matrix, polymorph control of nanocrystals can be achieved. Solid-state nuclear magnetic resonance (ssNMR) was used to examine the local structure of nanocrystals inside the polymer matrices and crystal polymer interactions. This method may serve as a novel formulation method to obtain nanocrystals of poorly soluble active pharmaceutical ingredients (APIs) for pharmaceutical industry.
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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".