Toothpaste Abrasion and Abrasive Particle Content: Correlating High-Resolution Profilometric Analysis with Relative Dentin Abrasivity (RDA)
Bibliographic record
Abstract
In this in vitro study, the influence of the concentration of abrasive particles on the abrasivity of toothpastes was investigated using laser scan profilometry on polymethyl methacrylate (PMMA) surfaces with the aim of providing an alternative method to developers for screening of new toothpaste formulations. PMMA plates were tested in a toothbrush simulator with distilled water and four model toothpastes with increasing content of hydrated silica (2.5, 5.0, 7.5, 10.0 wt%). The viscosity of the model toothpaste formulations was kept constant by means of varying the content of sodium carboxymethyl cellulose and water. The brushed surfaces were evaluated using laser scan profilometry at micrometer-scale resolutions, and the total volume of the introduced scratches was calculated along with the roughness parameters Ra, Rz and Rv. RDA measurements commissioned for the same toothpaste formulations were used to analyze the correlation between results obtained with the different methods. The same experimental procedure was applied to five commercially available toothpastes, and the results were evaluated against our model system. In addition, we characterize abrasive hydrated silica and discuss their effects on PMMA-sample surfaces. The results show that the abrasiveness of a model toothpaste increases with the weight percentage of hydrated silica. Increasing roughness parameter and volume loss values show good correlation with the likewise increasing corresponding RDA values for all model toothpastes, as well as commercial toothpastes without ingredients that can damage the used substrate PMMA. From our results, we deduce an abrasion classification that corresponds to the RDA classification established for marketed toothpastes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".