STUDY ON THE Ph INFLUENCE ON SURFACE MICROHARDNESS OF SOME REPAIR MATERIALS USED IN ENDODONTICS
Bibliographic record
Abstract
Introduction The irrigating solutions used in endodontic therapy have different pH values and different chemical properties that have sometimes been found to adversely affect the physical and chemical characteristics of reparing materials. These materials are used mainly in areas of inflamed tissue, with a lower pH. Aim of the study To evaluate the changes in the hardness of two dental materials depending on pH variations and to determine which of the additives they are combined with gives them greater stability to pH variations. Material and methods The changes in surface microhardness (Vickers microhardness) of two repair materials: Grey MTA (Dentsply Tulsa Dental, USA) and BioAggregate (Innovative BioCeramix Inc., Vancouver, Canada), mixed with four different vehicles (distilled water, physiological saline, lidocaine and calcium chloride) and subsequently subjected to different environmental pH values. Vickers microhardness of each specimen was measured by means of Emcotest M1C 010 model. Results The analysis of the average surface hardness of the two repair materials showed a significant increase in hardness at high pH (pH = 7) and higher values for BioAggregate as compared with MTA. Conclusions Ph variations of the environment in which biomaterials are setting reduce their microhardness and surface resistance, and this was more significant when the two materials were combined with lidocaine and distilled water.
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".