Thermal and dielectric properties of clay/epoxy nanocomposites with low percentage of graphite oxide
Bibliographic record
Abstract
This work is concerned with hybrid nanocomposite materials that contain both clay and graphite oxide incorporated in an epoxy matrix. In order to assess the challenge of improving the thermal conductivity while not changing or even improving the electrical properties, a new composite system was designed by applying an additional thermal conductive phase represented by the graphite oxide. In this paper, extremely low quantities of graphite oxide were used (going from 0.0012 to 0.0025 wt%) in order to avoid the forming of an electrical conduction percolation network. The impact of the additional filler on the thermal and electrical properties of the clay/graphite oxide/epoxy nanocomposites were investigated. Using thermal conductivity measurements, it was found that even for low quantities of GO filler added to the clay/epoxy nanocomposite material, the thermal conductivity is improved significantly. Moreover, using dielectric characterization techniques (Dielectric Spectroscopy, Space Charge or Dielectric Breakdown measurements), it was found that the electrical properties of the material remain unchanged or are slightly improved by the extra graphite oxide filler.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".