Enhancement of Mechanical and Biological Properties of Polyurethane Clear Aligners Using Sustainable SiO2 Nanoparticles
Bibliographic record
Abstract
In this study, polyurethane (PU) was employed as the base material for the fabrication of clear dental aligners due to its favorable optical transparency, flexibility, and biocompatibility.The primary objective was to enhance the mechanical and biological performance of PU through the incorporation of sustainable SiO₂ nanoparticles.Four different nanoparticle loadings (0.1, 0.2, 0.3, and 0.4 wt%, with an average particle size of 57.7 nm) were dispersed within the polymer matrix.Fourier transform infrared spectroscopy (FTIR) was used to identify functional groups and assess possible interactions between the nanoparticles and PU chains.The dispersion state and surface morphology were examined using scanning electron microscopy (SEM).The results demonstrated that, at lower nanoparticle concentrations, SiO₂ nanoparticles were uniformly distributed within the PU matrix without noticeable agglomeration, which is essential for effective reinforcement.Mechanical testing revealed a significant improvement in hardness, increasing from 72 Shore D for neat PU to 96 Shore D for nanoparticle-reinforced samples.Tensile strength also exhibited a progressive increase from 45 MPa for pure PU to 51, 54, 58, and 64 MPa with increasing nanoparticle content.These improvements were attributed to restricted molecular mobility, enhanced stress transfer, and the formation of a more integrated and rigid microstructure due to the homogeneous dispersion of nanoparticles.Biological evaluations indicated that higher nanoparticle concentrations enhanced antibacterial activity, while all formulations remained non-toxic, confirming their suitability for biomedical applications.Overall, the findings demonstrate that sustainable SiO₂ nanoparticle reinforcement significantly improves the mechanical robustness and biological performance of polyurethane, highlighting its potential for advanced clear aligner 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) | 0.000 | 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) | 0.001 | 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".