<i>In situ</i> wet pharmaceutical granulation captured using synchrotron radiation based dynamic micro-CT
Bibliographic record
Abstract
Synchrotron radiation based dynamic micro-computed tomography (micro-CT) is a powerful technique available at synchrotron light sources for investigating evolving microstructures. Wet granulation is the most widely used method of producing pharmaceutical granules, precursors to products like capsules and tablets. Granule microstructures are known to influence product performance, so this is an area for potential application of dynamic CT. Here, lactose monohydrate (LMH) was used as a representative powder to demonstrate dynamic CT capabilities. Wet granulation of LMH has been observed to occur on the order of several seconds, which is too fast for lab-based CT scanners to capture the changing internal structures. The superior X-ray photon flux from synchrotron light sources makes sub-second data acquisition possible and well suited for analysis of the wet-granulation process. Moreover, synchrotron radiation based imaging is non-destructive, does not require altering the sample in any way, and can enhance image contrast with phase-retrieval algorithms. Dynamic CT can bring insights to wet granulation, an area of research previously only studied via 2D and/or ex situ techniques. Through efficient data-processing strategies, dynamic CT can provide quantitative analysis of how the internal microstructure of an LMH granule evolves during the earliest moments of wet granulation. Here, the results revealed granule consolidation, the evolving porosity, and the influence of aggregates on granule porosity.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".