Thermal performance customization of polyimide films by nanocomposite engineering with Al2O3 and ZnO nanoparticles
Bibliographic record
Abstract
The fascinating properties of polyimide films, such as outstanding thermal stability, chemical/radiation resistance, excellent mechanical strength, and a low dielectric constant, can be further optimized by inorganic fillers, making them potential candidates for replacing metals/ceramics in modern technologies. In this study, the effect of Al 2 O 3 and ZnO nanoparticles (NPs) on the thermal performance of polyimide was evaluated by varying nanoparticle loadings (3%, 5%, 7%, and 9%). The incorporation of nanoparticles within the polyimide matrix was confirmed by wide-angle X-ray diffraction (WAXRD) analysis. Their homogenous distribution throughout the matrix was verified by scanning electron microscopy (SEM) and transmission electron microscopy (TEM). Thermal decomposition of the polyimide matrix started at approximately 400°C, with relatively small weight loss up to 500°C, suggesting significantly high thermal stability. This stability was further improved by the addition of Al 2 O 3 nanoparticles, while ZnO nanoparticles lowered the temperature resistance. The isothermal thermogravimetric analysis (TGA) further complemented the results of dynamic TGA as substantially high thermal endurance at 400°C was observed for polyimide nanocomposites, suggesting their capability to withstand elevated temperatures for extended periods. The glass transition temperature of the polyimide matrix was enhanced by both types of nanoparticles in a concentration-dependent manner. The thermal performance of polyimide was significantly affected by nanoparticle concentration.
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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".