Effect of Nanoparticles Reinforcement on Mechanical Properties of Polycarbonate Resin - A Systematic Review and Meta-Analysis of In vitro Studies
Bibliographic record
Abstract
INTRODUCTION: Incorporation of nanoparticles but to the polymers may improve the mechanical properties. This systematic review aimed to analyze the effect of nanoparticles reinforcement on mechanical properties of non-metallic clasp retained denture polycarbonate resin. The systematic review followed PRISMA guidelines and the population, intervention, comparison, outcome (PICO) question was 'What is the effect of nanoparticles reinforcement on flexural strength, impact strength, tensile strength, and surface hardness of polycarbonate resin?''. DATA SOURCES: The published literatures were searched in Google scholar, PubMed, Scopus, Embase, Web of Science, Cochrane, Science Direct and ProQuest till 31 December 2024. Risk of bias analysis was done using Joanna Briggs Institute critical for non-randomized experimental studies and the meta-analysis was conducted using Newcastle Ottawa Scale. After applying the selection criteria, 22 in vitro studies were thoroughly assessed. SELECTED STUDIES: Published literature from eleven in-vitro studies were eligible for systematic review and three studies were eligible for meta-analysis. STUDY INFERENCES: There was an insignificant difference in surface hardness and significant difference found in flexural strength, impact strength, tensile strength after nanoparticle reinforcement ( P = 0.24; MD: 1.75; 95% confidence interval -4.62-8.15; I2 = 100%, P < 0.00001), impact strength ( P = 0.06; MD: 2.22; 95% confidence interval -2.89-7.32; I2 = 99%, P < 0.00001), tensile strength ( P = 0.20; MD: 3.63; 95% confidence interval -8.85-16.11; I2 = 100%, P < 0.00001), surface roughness ( P = 0.27; MD: 1.22; 95% confidence interval -1.43-3.86; I2 = 0%, P > 0.0001). CONCLUSION: The literature based evidenced showed that nanoparticle reinforcement showed changes in flexural strength, impact strength and tensile strength of polycarbonate resin.
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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 | 0.024 | 0.050 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.041 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".