Polycarbonate biocomposites reinforced with a hybrid filler system of recycled carbon fiber and biocarbon: Preparation and thermomechanical characterization
Bibliographic record
Abstract
ABSTRACT In this article, we present the investigation of the use of a partly biobased hybrid reinforcement system to improve the mechanical properties of polycarbonate (PC). To minimize the amount of recycled carbon fibers (rCFs) used in this study, their initial quantity of 20% was reduced and replaced by pyrolyzed biocarbon (BC) particles in amounts of 5%, 10%, 15%, and 20%. The materials were prepared during an extrusion‐/injection‐molding processing procedure. In addition to basic mechanical tests (tensile, flexural, and Izod tests), the samples were also subjected to detailed dynamic mechanical analysis to determine the thermomechanical relationships, such as the C factor, entanglement density, adhesion factor, and reinforcing efficiency. The results confirm the positive effect of hybridization, especially for the samples with low BC contents. In relation to the 20% pure BC composites, the hybrid samples containing the same amount of mixed filler (10%; rCF10–BC10) achieved an almost triple (270%) increase in the tensile strength and a 35% increase in the modulus. The impact resistance was also increased by 170%. Differential scanning calorimetry analysis showed significant changes in the glass‐transition temperatures for the BC‐rich samples; this was due to the sensitivity of the PC matrix to the processing degradation. The application of a small quantity of epoxy‐based chain extender proved to be effective in reducing this unfavorable phenomenon. © 2018 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2018, 135, 46449.
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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) | 0.000 | 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".