Biocarbon from peanut hulls and their green composites with biobased poly(trimethylene terephthalate) (PTT)
Bibliographic record
Abstract
Abstract There are millions of tons of post-food processing residues discarded annually. Currently, these waste materials are discarded to landfill, used as animal feed or incinerated. This suggests that there are potential uses for these materials in value-added applications. This work focuses on the characterization and valorization of peanut hulls through the generation of green composites. Peanut hulls were pyrolyzed at 500 °C and analyzed to discover their unique surface morphology and relatively low ash content. Raman spectral analysis determined I D /I G values of 0.74 for the samples, suggesting greater graphitic content than disordered carbon content. Such results were confirmed in X-ray diffraction analysis by the presence of (002) and (100) planes. Partially biobased engineering thermoplastic, poly(trimethylene terephthalate) (PTT), was combined with 20 wt.% biocarbon. The tensile and flexural moduli improved with the addition of biocarbon, and the bio-content increased from 35 to 48 wt.% as compared to neat PTT. The higher temperature biocarbon was found to have superior performance over the lower temperature sample. The enhanced sustainability of these materials suggested that peanut hulls can be valorized via thermochemical conversion to generate value-added products. Future works could focus on the optimization of these materials for non-structural automotive components or electrical housings.
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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".