Study of dielectric relaxation of epoxy composites containing micro and nano particles
Bibliographic record
Abstract
The influences of micro and nanoparticles on relaxation kinetics were studied using Differential Scanning Calorimetry and Dielectric Spectroscopy. The samples are composed of Quartz and/or Organically Modified Montmorillonite dispersed in an epoxy matrix. The heat capacity step, normalized to epoxy quantity, did not show significant variations, contrary to the glass transition temperature, which decreased for the nanostructured microcomposite. The main α relaxation parameters, fragility index and relaxation time at the glass transition temperature combined to show that, even though molecular movements are hindered by the presence of particles acting as obstacles, the global mobility of the molecular chains is increased because reticulation of the epoxy has been prevented. The local β relaxation, associated with crankshaft motions of the hydroxylether groups, is not affected by micro and nanoparticles. The modification of the γ relaxation associated with the ending epoxy groups suggests that different interfacial interactions occur with nanoparticles/epoxy and microparticles/epoxy. The relaxation parameters will be situated in the context of the dielectric breakdown results. Dielectric breakdown strengths are more modified by inhomogeneity in sample preparation than by addition of organically modified Montmorillonite to the composite material.
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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".