Effects of tableting process parameters and powder lubrication levels on tablet surface temperature and moisture content
Bibliographic record
Abstract
Punch sticking is a recurrent problem during the pharmaceutical tableting process. Powder moisture content plays a key role in the buildup of sticking; it evaporates due to increased tablet temperature, accumulates at the punch-tablet interface, and causes sticking through capillary force. This study investigated the effects of compaction pressure (CP), compaction speed (CS), and lubrication level (magnesium stearate (MgSt) ratio) on tablet surface temperature (TST) and tablet surface moisture content (TSMC). TST and TSMC were measured with an infrared thermal camera and near-infrared sensor, respectively. Microcrystalline cellulose was used as the tableting powder and MgSt as the lubricant. The low range of CS values (16-32 mm/s) considered in this study did not have significant effects on TST and TSMC. MgSt ratio had a significant positive effect on TST; this may be explained by the increase in powder blend effusivity with the addition of MgSt. However, MgSt ratio did not have a significant effect on TSMC. CP had a significant positive effect on both TST and TSMC. Increased CP induced higher heat generation through particle deformation and friction during the compaction phase, leading to increased TST. Furthermore, the water vapor diffusion rate through the powder bed might have increased due to the rise in thermal energy and led to further moisture accumulation at the tablet-punch interface, causing the significant positive effect of CP on TSMC. This result may explain the occurrence of sticking regardless of the CP applied during the tableting process.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".