The effect of acidity on the physicochemical properties of two hydraulic calcium silicate-based cements and two calcium phosphate silicate-based cements
Bibliographic record
Abstract
BACKGROUND: Bioceramic cements have been widely used in endodontic treatment. This study aimed to compare the microhardness, elastic modulus, internal microstructure and chemical compositions of Biodentine, WMTA, ERRM Putty, iRoot FS and IRM after exposure to PBS, butyric acid, and butyric acid followed by PBS. METHODS: Specimens of each material were prepared and randomly divided into 5 subgroups (n = 5): subgroup A: PBS (pH = 7.4) for 4 days, subgroup B: PBS (pH = 7.4) for 14 days, subgroup C: butyric acid (pH = 5.4) for 4 days, subgroup D: butyric acid (pH = 5.4) for 14 days, subgroup E: butyric acid for 4 days followed by 10 days in contact with PBS. The surface microhardness, elastic modulus, internal morphologic and chemical compositions of specimens were analyzed. RESULTS: The microhardness and elastic modulus values of all materials were significantly higher in the presence of PBS compared to exposure to butyric acid, with the same setting time (P < 0.01). After 4-day exposure to butyric acid followed by 10-day exposure to PBS, the microhardness values returned to the same level as 4-day exposure to PBS (P > 0.05). Biodentine showed significantly higher microhardness and elastic modulus values than other materials, while IRM displayed the lowest (P < 0.01). CONCLUSION: Biodentine seems the most suitable bioceramic cements when applied to an infected area with acidic pH. Further storage at neutral pH, e.g. PBS reverses the adverse effects on bioceramic cements caused by a low pH environment.
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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".