Influence of punch coating surface properties on sticking during the tableting process
Bibliographic record
Abstract
Introduction The present study evaluates the sticking propensity of Uncoated steel, and chromium nitride (CrN), zirconium nitride (ZrN), titanium nitride (TiN) and Ultracoat punch coatings during the tableting process of microcrystalline cellulose (MCC) conducted on a Manesty® F3 single station tableting press.Methods Surface properties including surface roughness, surface free energy (SFE) and its components, the atomic percentage of surface polar functional groups and oxides measured with X-ray photoelectron spectroscopy were used to characterize the surface propensity to sticking.Results After five hours of tablet pressing, MCC powder particles were found to adhere to the TiN coated and the uncoated steel punches. Surface analysis show that surface roughness of all the tested punches was similar. The Lewis base SFE component (LB-comp) was found to govern the acid-base interactions of the tested surfaces, and its value was higher for punch surfaces affected by sticking. The surfaces exhibiting higher LB-comp are more prone to strong acid-base interactions with water molecules that evaporate from the powder bed during compression. Therefore, these surfaces adsorbed water and allow sticking through capillary adhesion force.Conclusion The total atomic percentage of the surface polar functional groups (PFG) and oxides was also high for the surfaces that stick to MCC during tableting, suggesting that hydrophilic molecules on the punch surface favor sticking.
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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 | 0.001 |
| 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".