Investigating the properties of subcritical water extraction with pharmaceutical tablets
Bibliographic record
Abstract
The properties of subcritical water extraction (SWE) in the sample preparation of pharmaceutical tablets were investigated. Tablets comprised of microcrystalline cellulose excipients broke apart up to 80 times faster in subcritical water than they did in room temperature water, while those containing starch readily broke apart in either. Tablets containing starch were also observed to gelatinize or “paste” over several SWE conditions, impeding subsequent filtration and analysis. This effect was avoidable, however, since it was demonstrated to disappear with increases in extraction time and temperature or decreases in sample size. Using SWE, two common over-the-counter pharmaceuticals were extracted under optimized conditions from tablets comprised of either microcrystalline cellulose or starch excipients. Analyte recoveries of 95% or more were obtained at 150 °C for vitamin C (ascorbic acid) tablets in as little as 8 min for the extraction of a whole intact tablet, 6 min for two half tablets, and 5 min for a ground tablet. By comparison, this occurred at 250 °C in just 2 min for an intact slice of an acetaminophen tablet. Reproducibility was generally quite good with these trials producing RSD values of less than 2%. The results indicate that SWE can be a potentially viable and efficient method for the sample preparation of whole, sliced, or ground pharmaceutical tablets, and further exploration of this approach is promising.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".