Silica Coating of Nonsilicate Nanoparticles for Resin-Based Composite Materials
Bibliographic record
Abstract
). The hypothesis was that the coated nonsilicate nanoparticles would stably reinforce a polymeric matrix due to effective silanation. Silica coating was applied via a sol-gel method, with tetraethyl orthosilicate as a silica precursor, followed by heat treatment. The chemical and microstructural characteristics of the nanopowders were evaluated before and after silica coating through x-ray diffraction, BET (Brunauer-Emmett-Teller), energy-dispersive x-ray spectroscopy, field emission scanning electron microscopy, and transmission electron microscopy analyses. Coated and noncoated nanoparticles were silanated before preparation of hybrid composites, which contained glass microparticles in addition to the nanoparticles. The composites were mechanically tested in 4-point bending mode after aging (10,000 thermal cycles). Results of all chemical and microstructural analyses confirmed the successful obtaining of silica-coated nanoparticles. Two distinct aspects were observed depending on the type of nanoparticle tested: 1) formation of a silica shell on the surface of the particles and 2) nanoparticle clusters embedded into a silica matrix. The aged hybrid composites formulated with the coated nanoparticles showed improved flexural strength (10% to 30% higher) and work of fracture (35% to 40% higher) as compared with composites formulated with noncoated nanoparticles. The tested hypothesis was confirmed: silanated silica-coated nonsilicate nanoparticles yielded stable reinforcement of dimethacrylate polymeric matrix due to effective silanation. The silica-coating method presented here is a versatile and promising novel strategy for the use of crystalline nonsilicate ceramics as a reinforcing phase of polymeric composite biomaterials.
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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) | 0.003 | 0.001 |
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".