Microwave heating of graphene nanoplatelet polymer composites: Experimental and finite element study
Bibliographic record
Abstract
Abstract Compared with contemporary electrofusion techniques that use embedded wires for Joule heating, microwave heating may facilitate the joining of thermoplastic polymer components, promising shortened fusion periods, superior heating uniformity, and reduced energy consumption. This study investigates the fabrication of multifunctional polylactide acid (PLA) composites with strong microwave absorption using graphene nanoplatelets (GNP). GNP/PLA nanocomposites were fabricated using a two‐step scalable manufacturing method, that is, solution blending and hot compression molding. The GNP content of the composites ranged from 0% to 8% by weight. The samples were characterized for dielectric permittivity, heat capacity, and electrical and thermal conductivity. Thermal imaging was used to investigate the efficacy of microwave heating in GNP/PLA nanocomposites as a function of microwave power and filler weight fractions. The microwave heating process in GNP composites was studied using multi‐physics finite element software. The experimental results were compared to numerical model predictions for maximum temperature and microwave energy absorbed. The produced nanocomposites were discovered to have strong microwave absorption characteristics and hence rapid heating, making this type of composite a prospective choice for gasket materials that facilitate fusion bonding for thermoplastic‐based components via localized heating.
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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) | 0.002 | 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".