Physical properties and hydration behavior of a fast-setting bioceramic endodontic material
Bibliographic record
Abstract
BACKGROUND: To investigate the physical properties and the hydration behaviour of the fast-setting bioceramic iRoot FS Fast Set Root Repair Material (iRoot FS) and three other endodontic cements. METHODS: iRoot FS, Endosequence Root Repair Material Putty (ERRM Putty), gray and white mineral trioxide aggregate (G-MTA & W-MTA), and intermediate restorative material (IRM) were evaluated. The setting time was measured using ANSI/ADA standards. Microhardness was evaluated using the Vickers indentation test. Compressive strength and porosity were investigated at 7 and 28 days. Differential scanning calorimetry (DSC) was employed for the hydration test. RESULTS: iRoot FS had the shortest setting time of the four bioceramic cements (p < .001). The microhardness values of iRoot FS, ERRM Putty and MTA increased at different rates over the 28 days period. At day one, ERRM Putty had the lowest microhardness of the bioceramic cements (p < .001), but reached the same level as MTA at 4, 7 and 28 days. The microhardness of iRoot FS was lower than that of W-MTA at 7 and 28 days (p < .05). The porosity of the materials did not change after 7 days (p < .05). The compressive strength values at 28 days were significantly greater for all bioceramic groups compared to those at 7 days (p < .01). ERRM Putty had the highest compressive strength and the lowest porosity of the evaluated bioceramic cements (p < .05), followed by iRoot FS, W-MTA, and G-MTA, respectively. DSC showed that iRoot FS hydrated fastest, inducing an intense exothermic reaction. The ERRM Putty did not demonstrate a clear exothermic peak during the isothermal calorimetry test. CONCLUSIONS: iRoot FS had a faster setting time and hydrating process than the other bioceramic cements tested. The mechanical properties of iRoot FS, G-MTA and W-MTA were relatively similar.
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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".