Physical–chemical properties of furosemide nanocrystals developed using rotation revolution mixer
Bibliographic record
Abstract
Recently, several approaches have been reported to improve the dissolution rate and bioavailability of furosemide, a class IV drug. However, to the best of our knowledge, none of them proposed nanocrystals. In the last decade, nanocrystals successfully addressed solubility issues by increasing surface area and saturation solubility, both leading to an increase in the dissolution rate of poor water soluble drugs. The preparation of furosemide nanocrystals was by a rotation revolution mixer method. Size distribution and morphology were performed using laser diffraction and scanning electron microscopy, respectively. In addition, differential scanning calorimetry, thermogravimetry, X-ray powder diffraction (XRD) and low frequency shift-Raman spectroscopy allowed investigating the thermal properties and crystalline state. Solubility saturation and intrinsic dissolution rate (IDR) studies were conducted. The thermal analysis revealed lower melting range for the nanocrystals comparing to furosemide. Moreover, a slight crystalline structure change to the amorphous state was observed by XRD and confirmed by low frequency shift Raman. The particle size was reduced to 231 nm with a polydispersity index of 0.232, a 30-fold reduction from the original powder. Finally, the saturation solubility and IDR showed a significant increase. Furosemide nanocrystals showed potential for development of innovative formulations as an alternative to the commercial products.
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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".