Rapid and facile preparation of nanocomposite film heaters for composite manufacturing
Bibliographic record
Abstract
Nanocomposite film heaters are promising for out-of-oven (OoO) and energy-efficient curing of fiber-reinforced polymer composites. However, the current techniques for manufacturing nanocomposite film heaters are intensive in terms of time and energy and require expensive resources. In this work, we present a facile and rapid approach for preparation of nanocomposite film heaters with excellent heat generation properties based on a frontally polymerizable resin system. This approach enables rapid fabrication of nanocomposite films within a few minutes and without the need for using expensive equipment, making it suitable for mass production of nanocomposite film heaters. Various characterization techniques are used to determine the morphology, composition, and mechanical properties of nanocomposite films. The electrothermal performance of nanocomposite film heaters are then evaluated under various conditions. Nanostructured heaters exhibit excellent Joule heating properties, where temperatures as high as ∼132°C can be reached within only 2 min using a low input power density of ∼2 W cm−2. Finally, a nanocomposite film heater is used for OoO curing of a small composite panel with minimal energy consumption. Using this approach, 0.1 MJ of energy is consumed during the 4-h cure cycle of a commercial prepreg system, which would otherwise require at least 40.5 MJ of energy to cure using a convection oven.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".