Effect of a novel green modification of alumina nanoparticles on the curing kinetics and electrical insulation properties of epoxy composites
Bibliographic record
Abstract
Abstract A novel green surface modification was successfully implemented on alumina nanoparticles using chitosan (CS) to prevent nanoparticles' aggregation. To evaluate the surface changes of nanoparticles, FTIR, TGA, TEM, and SEM analyses were used. The cure kinetics of the uncured samples was analyzed by DSC. Different methods such as KAS, Friedman, Starink, and FWO were applied to measure the activation energy. The activation energy of epoxy reinforced with chitosan‐functionalized alumina (epoxy/[CS‐EPO‐alumina]) was less than that of epoxy reinforced with alumina (epoxy/alumina), which was a confirmation of the positive effect of CS on curing reaction kinetics. Using the Malek method, the Sestak‐Berggren autocatalytic equation was chosen to investigate the cure kinetics of the epoxy. It was found that the Sestak‐Berggren equation is well matched with the experimental data and the model was suitable to predict the epoxy curing reaction reliably. Moreover, the glass transition temperatures of all samples were approximately the same. The effect of surface modification of alumina on the electrical insulating behavior of epoxy was also studied. It was found that CS functionalized alumina (CS‐EPO‐alumina) increased volume resistivity of epoxy at a temperature range of 30 to 80°C more than that of alumina. Electric stability and breakdown strength of epoxy/alumina and epoxy/(CS‐EPO‐alumina) also enhanced, where epoxy/(CS‐EPO‐alumina) experienced a further increase compared to epoxy.
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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".