Biocomposites with Size-Fractionated Biocarbon: Influence of the Microstructure on Macroscopic Properties
Bibliographic record
Abstract
This study is an experimental investigation of using biocarbon as renewable carbonaceous filler for engineering-plastic-based blends. Poly(trimethylene terephthalate) (PTT) and poly(lactic acid) (PLA) combined with a terpolymer were selected as the blend matrix. Biocarbon with various particle size ranges was segregated and used as filler. Depending on the particle size and aspect ratio of the biocarbon used, the microstructure of the composite was found to change. Composites having a biocarbon particle size range of 20-75 μm resulted in a morphology showing better dispersion of the blend components when compared with composites containing other biocarbon particle size ranges. Furthermore, the addition of epoxy-based multifunctional chain extender was found to result in much finer morphologies having dispersed polymer particles of very small size. Impact strength increased significantly in composites that possessed such morphologies favoring high energy dissipation mechanisms. A maximum notched Izod impact strength of 85 J/m was achieved in certain composite formulations, which is impressive considering the inherent brittleness of PTT and PLA. From rheological observations, incorporation of biocarbon increased viscosity, but the shear-thinning behavior of the matrix was preserved. By increasing the injection mold temperature, fast crystallization of PTT was achieved, which increased the heat deflection temperature of composites to 80 °C. This study shows that composites with overall improvement in mechanical and thermal performance can be produced by selecting biocarbon with appropriate particle sizes and suitable processing aids and conditions, which all together control the morphology and crystallinity.
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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".