Thermal, mechanical, and electrical properties of difunctional and trifunctional epoxy blends with nanoporous materials
Bibliographic record
Abstract
In the present study, the aim is to synthesize the particulate nanocomposites with difunctional and trifunctional epoxy blend as matrix and synthesized nanoporous materials as fillers. Organic/inorganic hybrid networks were prepared by the novel solvent free method. Viscoelastic, thermal, and electrical properties of di- and trifunctional epoxy and the effect of different nanoparticles in the particulate nanocomposites have been studied by dynamic mechanical analyzer, thermogravimetry (TGA), and dielectric strength. Epoxy mixed with different compositions of TGPAP and particulate nanocomposites by the addition of different types of nanomaterials shows higher storage modulus than the pure epoxy. The addition of TGPAP and nanofillers decreases the thermal stability of epoxy matrix. The evolved gas analysis (TG-FTIR) was also done in order to study the products formed during degradation. An increase in dielectric strength and impact strength (4%) was also observed in the particulate nanocomposites.
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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".