Investigating the Potential of Walnut Shell Powder as a Filler in PMMA Denture Bases
Bibliographic record
Abstract
With an increasing of dental problems for aging population, there is more needing for more effective treatments to enhance the quality of denture base.Natural powders have biocompatibility characteristics and considerable mechanical properties as well as being inexpensive and eco-friendly.Poly methyl methacrylate (PMMA) is widely utilized in medical and dental field due to its high compatibility and ease of manufacturing.However, its mechanical properties are relatively weak.In current work, PMMA bio composites were reinforced with walnut shells powder of two particle sizes (25 and 150 μm) in various concentrations.Physical and mechanical properties of bio composites were investigated.SEM showed a dense and smooth bio composite morphology, indicating strong PMMA-reinforcement adhesion with no new phases.The results exhibited a considerable advancement in the mechanical properties values for all bio composite specimens as compared with neat PMMA.Specimens that strengthened with fine size from walnut shells powder exhibited enhanced properties compared with those of the coarse size for the same material.For composites reinforced with fine particles (25μm), the maximum values for compression strength and hardness for samples strengthened with were 325.56 MPa and 86 shore-D respectively.In contrast, composites reinforced with coarse particle (150μm) of the same material had the maximum values of 306.409MPa and 82.2 shore-D respectively.In light of the findings, it can be concluded that adding of fine particles of walnut shells powder to neat (PMMA) resin is one of the promising substances utilized to enhance the mechanical properties for denture bases more than coarse particles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".