Comparison in composite performance after thermooxidative aging of injection molded polyamide 6 with glass fiber, talc, and a sustainable biocarbon filler
Bibliographic record
Abstract
ABSTRACT Degradation is an unavoidable part of a material's life making it important to both monitor and control the aging behavior of plastics. This study compares thermooxidative degraded composites of a novel bio‐based and sustainable filler, Biocarbon (MBc), against that of traditional and commercially available fillers (glass fiber and talc) used in the automotive industry. The influence of thermooxidative degradation on the composites was studied under accelerated heat aging for 1000 h at 140°C. The mechanical properties of the composites were evaluated using notched Izod impact as well as both tensile and flexural tests. Morphological structure of the composites was investigated using a scanning electron microscopy. Dynamic mechanical analysis and differential scanning calorimetry were used to evaluate the physical transitions both before and after aging. The glass‐filled composites displayed the best performance; while, both the talc and biocarbon composites possessed similar strength and ductility performances. Advantageously, the biocarbon composites experienced an 11% reduction in density as compared to talc‐filled composites with similar weight content. After aging, all composites exhibited reduced tensile and flexural strengths ranging from 5 to 67% partly due to chain scission. Whereas, the modulus of all composites increased with a range of 1–24% due to an annealing effect. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci.2020,137, 48618.
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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".