Mechanical Properties of PMMA-Based Biocomposites with Polyamide and Polyvinylpyrrolidone Blends for Denture Applications
Bibliographic record
Abstract
By blending heat-cured polymethylmethacrylate (PMMA) resin with two different types of polymers, which are polyamide (PV) type (6) and polyvinylpyrrolidone (PVP) type (K30), added separately with various weight fractions (0, 2, 4 and 6%) to heat-cured PMMA resin as blend matrix, attempts have been made in this study for developing the PMMA resin properties employed for prosthesis complete denture.Sisal powder and coconut powder, two different types of natural powders, were added separately to the polymer blend matrices with varying weight fractions (2%, 4%, and 6%) to prepare composite specimens.Hand lay-up methods were used for preparing all specimens.This study covered the flexural test using the 3-point bending method, the impact test using the Izod method, and the maximum shear stress test using the 3-point bending method.According to the results, adding polymer blends and reinforcing powders boosts the impact strength, while lowering flexural modulus, flexural strength, and maximum shear stress.The composite specimens made of (PMMA-2% PVP-6% sisal) manifested the best impact strength and value (11.875 KJ/m 2 ).These findings lead to the conclusion that using PMMA-based biocomposites with polyamide and polyvinylpyrrolidone blends improved impact strength for denture applications, which is one of the most important properties of these 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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".