Physicochemical, Mechanical, and Esthetic Properties of the Composite Resin Manipulated with Glove Powder and Adhesive as a Modeling Liquid
Bibliographic record
Abstract
Composite resins with low flowability are usually handled and manipulated before insertion into the tooth preparation with gloved hands and/or using an instrument covered with a little amount of adhesive to facilitate modeling. We investigated if the modeling techniques (combined or not) affected physicochemical and esthetic properties of a composite resin. Specimens were fabricated and divided into groups according to the handling/modeling technique: Gloved-hands (composite was hand-manipulated with powdered latex gloves); Adhesive (adhesive was used in between the composite layers); Gloved-hands + Adhesive; Control (no adhesive and no touch with gloved-hands). The highest values for flexural strength (MPa), modulus of elasticity (GPa), and fracture toughness (MPa.m0.5) were obtained for Adhesive and Gloved-hands + Adhesive (p < 0.05); the lowest values were obtained for Control and Gloved-hands (p < 0.05). The Control group had the highest sorption. The Gloved-hands (p < 0.05) group had the highest solubility. Adhesive and Gloved-hands + Adhesive had a similar solubility (p > 0.05). The Control group (p < 0.05) had the lowest solubility. There was no statistical interaction between translucency vs. handling/modeling techniques and color stability vs. handling/modeling techniques. Adhesive as a modeling liquid protected the composite against sorption and solubility (if powdered gloves were used) and improved its physical/mechanical properties. Translucency and color stability were not correlated with modeling techniques.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".