Statistical Design of Biocarbon Reinforced Sustainable Composites from Blends of Polyphthalamide (PPA) and Polyamide 4,10 (PA410)
Bibliographic record
Abstract
A full factorial design with four factors (the ratio of polyphthalamide (PPA) and polyamide 4,10 (PA410) in the polymer matrix, content percent of biocarbon (BioC), the temperature at which it was pyrolyzed and the presence of a chain extender (CE)), each factor with two levels (high and low), was carried out to optimize the mechanical properties of the resulting composites. After applying a linear model, changes in tensile strength, elongation at break and impact energy were not statistically significant within the considered material space, while the ones in the flexural modulus, the tensile modulus, density and heat deflection temperature (HDT) were. The two most influential factors were the content of BioC and its pyrolysis temperature, followed by the content of PPA. The affinity of PPA with a high-temperature biocarbon and the affinity of PA410 with a lower-temperature biocarbon, appear to explain the mechanical properties of the resulting composites. The study also revealed that the addition of CE hindered the mechanical properties. By maximizing the flexural modulus, tensile modulus and HDT, while minimizing the density, the optimal composite predicted is an 80 [PPA:PA410 (25:75)] wt% polymer composite, with 20 wt% of a BioC, pyrolyzed at a calculated 823 °C.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".