Effect of handling material on mechanical and optical properties of feldspathic porcelain
Bibliographic record
Abstract
Abstract Objective To evaluate the optical, chemical, and mechanical properties of feldspathic porcelain after handling with different instrument materials. Material and methods Feldspathic porcelain was manipulated with different spatula compositions: metal spatula (MS), plastic spatula (PS), and glass spatula (GS) for the fabrication of 30 (n = 10) disks. Contrast ratio (CR), translucency parameter (TP), and surface Knoop microhardness (KHN) were measured. The color change (ΔE00) was evaluated using the CIEDE2000 system. Scanning electron microscopy (SEM) and energy‐dispersive spectroscopy (EDS) were used for surface analysis. Data were analyzed using one‐way ANOVA and Tukey test (P < 0.05). Results All groups presented different morphological surfaces with higher presence of Al on the MS. PS group presented lower Al, Si, K than MS, and GS. Higher CR was observed for PS (0.734; P < 0.043), followed by MS (0.696; P < 0.043) and GS (0.65; P < 0.011). The highest TP (13.06) and KHN (386.27) were presented by GS (P < 0.001). MS and PS presented similar KHN results. The higher ΔE00 were found for plastic/metal comparison. Also, the L* values for the MS group (67.49) were lower than the other groups. Conclusion The use of metal spatula promoted higher color alteration during feldspathic porcelain manipulation than did the other materials. Handling with glass instrument promoted higher microhardness than other spatula materials. Clinical significance The effect of the material used for ceramic handling on feldspathic porcelain properties is often ignored. This study shows that the handling spatula material must be carefully chosen to avoid inadvertent changes to the feldspathic porcelain restoration.
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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) | 0.002 | 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".