Comparison of microwave heating of pure and functionalized graphene-nanoplatelet polymer composites: experimental and finite element Study
Bibliographic record
Abstract
Microwave heating can potentially speed up the joining of thermoplastic polymer components compared to modern electrofusion procedures that employ embedded wires for Joule heating. This could result in shorter fusion times, improved heating consistency, and lower energy usage. This work examines how functionalized graphene nanoplatelets (fGNP) can create multifunctional polylactide acid (PLA) composites with substantial microwave absorption. Tannic acid was used to treat graphene nanoplatelets, resulting in fGNP. The fGNP/PLA nanocomposites were produced using a two-step scalable manufacturing process that involved solution blending and hot compression moulding. The composites' fGNP concentration ranged between 0 and 8% by weight. The samples were evaluated for dielectric permittivity, heat capacity, and electrical and thermal conductivity. Thermal imaging was utilized to determine the effectiveness of microwave heating in fGNP/PLA nanocomposites as a function of microwave power and filler weight fraction. The microwave heating process in the composites was investigated using Multiphysics finite element software. The experimental results were compared to numerical model projections of the maximum temperature and microwave energy absorbed. The experimental and computational results for fGNP/PLA nanocomposites were contrasted to similar results for plain (non-functionalized) GNP in PLA. The generated nanocomposites were discovered to have excellent microwave absorption properties and, hence, quick heating, making this composite type a promising candidate for gasket materials that promote fusion bonding for thermoplastic-based components by localized heating.
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) | 0.002 | 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".