Biocompatibility and Durability of Diazonium Adhesives on Dental Alloys
Bibliographic record
Abstract
PURPOSE: A new type of diazonium-based adhesive has been recently developed by our team to bind dental alloys (Titanium, stainless steel, and cobalt chromium) to dental polymers. Here, we explored the endurance of the resulting adhesive after thermal-cycling and autoclave aging. MATERIALS AND METHODS: Polished samples of titanium (Ti), stainless steel (SS) and cobalt chromium (Co-Cr) were coated with a diazonium-based adhesive. Untreated samples served as controls (n = 12 per each condition). X-ray photoelectron spectroscopy (XPS) was performed to characterize the elemental compositions of the different surfaces. Biocompatibility of the coated alloys was assessed with human gingival fibroblasts (HGF). Inductively coupled plasma (ICP) and total organic carbon (TOC) analyses were used to quantify the ions and organic matters released from the diazonium coated alloys. Endurance of the adhesives was assessed by exposing the samples to autoclaving and thermal-cycling. The tensile strength of the poly(methylmethacrylate) (PMMA)-alloy bond was also tested. RESULTS: Results of mechanical testing demonstrated a higher endurance of the coated CoCr, Ti, and SS compared to the uncoated alloys. The human fibroblasts cultured on the substrates remained alive and metabolically active, and the coatings did not release significant amounts of toxic chemicals in solutions. CONCLUSIONS: The results further support the use of diazonium-based adhesives as new coupling agents for dental applications.
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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".