Study of Water Sorption in Methacryl-Based Polyhedral Oligomeric Silsesquioxane (POSS) Dental Composites Using Molecular Dynamics Simulations
Bibliographic record
Abstract
Methacrylate-based polyhedral oligomeric silsesquioxane (POSS) is one of the new composites used as a dental resin. Both monofunctional methacryl isobutyl POSS (MIPOSS) and multifunctional methacryl POSS (MAPOSS) are reported to be possible resins that possess the desired properties for using them as dental resins. Our group's previous comparative study on these two resins showed that the MAPOSS composite has superior mechanical properties compared with the MIPOSS composite. In this article, molecular dynamic simulations (MD simulations) are performed to study the water sorption in these two composites. Water sorption in dental composites can have several effects on the material properties, performance, and longevity of dental restorations. Water sorption in MAPOSS and MIPOSS composites is analyzed by studying the hydrogen bonding, cluster analysis, density projection calculations, and diffusion coefficient calculation of water molecules within the resin matrix. MD simulations results are further used to understand the interaction of water molecules with the resin matrix comprehensively, which governs the composite's mechanical properties. The water sorption study showed that the MAPOSS composite has less water sorption capacity than the MIPOSS composite. The practical significance of this study is to find properties that affect dental restoration and longevity, which can help in the design of better materials for dental 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".