An experimental investigation of the effect of the amount of lubricant on tablet properties
Bibliographic record
Abstract
BACKGROUND: Magnesium stearate (MgSt) is widely used as a lubricant in the production of tablets. However, the amount added to a formulation is often too high or it is poorly mixed, which can lead to the production of tablets whose properties are out of specifications. METHOD: The objective of this work was to investigate by means of a new method based on gamma-ray flux measurement and to study the impact of the amount of MgSt on the mass, thickness, hardness, friability, and disintegration time of tablets containing a 50 : 50 wt.% microcrystalline cellulose and spray-dried lactose pre-blend. Other blends were lubricated with sodium lauryl sulfate (SLS) to compare the performance of the two lubricants in equal amounts. RESULTS: It was observed that, contrary to SLS, a greater amount of MgSt increased the variability of the tablet mass. The tablet hardness decreased with an increasing amount of MgSt, whereas it remained relatively unaffected by the presence of SLS. No solid conclusion could be drawn concerning the relationship between the lubricant concentration and the tablet friability. CONCLUSION: An amount of 0.25 wt.% MgSt and 0.75 wt.% SLS were found to be sufficient amounts of lubricants to obtain a proper compression.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".