Bibliographic record
Abstract
The surface properties of dental materials such as dental alloys, dental composite and teeth play an important role in their interaction with the surrounding environment and the overall performance of the material itself. Thus, surface treatments of dental materials are often necessary to optimize the outcome of dental treatments. Examples of surface treatments in dentistry include adhesives, tooth whitening products and disclosing agents. However, despite being very useful, these treatments have a series of limitations that require attention.Dental adhesives have an excellent performance in enamel bonding, however, their ability to improve adhesion to dental metals is still very limited. Disclosing agents are excellent tools for identifying plaque accumulation on the surface of teeth, however, they are unable to discriminate dental surface characteristics such as the presence of composite resins. Finally, tooth whitening products have limited success.The main hypothesis of this thesis was that surface chemical modifications of dental materials could improve the outcome of dental treatments.Our main objective was to chemically modify the surface of dental materials both natural and synthetic to improve the outcome of dental treatments and our specific objectives were to:1. Modify the surface chemistry of dental alloys to facilitate their chemical bonding to composite resin.2. Modify the surface chemistry of composite resin to develop a composite disclosing agent.3. Modify the surface chemistry of tooth enamel to enhance its properties. For our first specific objective we used diazonium chemistry to modify the surfaces of dental alloys and enhance their adhesion to dental composites. Our results indicated that the bond strength between the tested dental alloys and composite resin using diazonium coupling agents significantly increased by three to four folds. Our second specific objective was to develop a composite disclosing agent to facilitate removal of resin-based adhesives while avoiding damage to the surrounding sound tooth structures. To achieve this, we characterized the interaction of composite resin with various organic molecules with structures comparable to composite monomers. The selective adsorption of these molecules to composite resin and not to tooth enamel makes them suitable to be used as priming agents in a two-step composite disclosing agent. The optimal staining conditions were confirmed clinically in a pilot study on orthodontic patients. Our third specific objective was to develop a new tooth whitening treatment based on the modification of the inorganic phase of tooth enamel. We investigated the effect of a hydrophobic environment on the surface properties, reactivity of tooth enamel to titanium ions as well as the depth of penetration of titanium treatments into enamel. Our results indicated that titanium treatments could penetrate deep into tooth enamel and enhance tooth shade and microhardness.The findings of this work proved our hypothesis that surface chemical modifications of dental materials could improve the outcome of dental treatments. We conclude that: 1. The treatment of dental alloys with diazonium ions could provide an easy and simple way to enhance the strength of the bond between dental alloys and composite resin. 2. Surface chemical modifications of composite resin using organic molecules, and food dyes could be used as a composite disclosing agent.3. A hydrophobic environment is a promising means to allow deep infiltration of organic metallic compounds into tooth enamel and enhance dental enamel properties
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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 | 0.000 |
| 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 | 0.001 |
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".