Pharmaceutical tablet compression: measuring temporal and radial concentration profiles to better assess segregation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Concentration monitoring inside a tablet press feed frame is important not only to assess the composition of the powder blend compressed into tablets but also to detect quality affecting phenomena such as powder segregation. Near infrared spectroscopy has been successfully used to monitor powder concentration inside the feed frame; however, so far, this methodology does not provide information on local spatial variability, since it probes a very small area of powder sample. Near infrared chemical imaging (NIR CI) has the potential to improve process monitoring because it can simultaneously acquire a plurality of spectra covering nearly the entire width of the feed frame, thereby making it possible to detect local variations in powder concentration. The present work uses both NIRS and NIR CI to monitor the concentration of Ibuprofen and Ascorbic acid in multi-component mock pharmaceutical blends flowing through the feed frame of an industrial tablet press. The concentrations of Ibuprofen and Ascorbic acid were successfully monitored in multi-component powder blends. NIR spectral wavelength ranges and pre-treatments were simultaneously optimized via a genetic algorithm. N-way PLS approach for concentration monitoring was found to be more suitable than regular PLS when analyzing spectral images and provided the ability to visualize spatial segregation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 | 0.000 |
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 it