Bio-Based Composites from Industrial By-products and Wastes as Raw Materials
Bibliographic record
Abstract
Innovative bio-based composites combining humins as biorefinery by-product with keratin or lignin as wastes or industrial side-products were developed. The bio-composites were prepared using three types of matrix formulations allowing the synthesis of elastic to rigid thermosets. These matrices were combined with chicken feathers powder, non-woven chicken feathers mat or lignin to produce bio-composites. A maximum quantity of bio-fillers was used, around 10 wt.%. The effect of the bio-fillers on the matrix’s crosslinking was studied by rheology and DSC. Then, the obtained materials were analyzed by TGA, DMA, tensile tests, water absorption and SEM. The results show a very good compatibility of the humins matrix with the bio-fillers, without any preliminary modification of the matrix, that is exceptional for the point of view of a composite. The overall performances of the neat matrix were maintained or improved through the composites. Therefore, bio-composites with potentially interesting thermal and mechanical properties have been synthesized. In the case of the elastic ductile matrix the Young’s modulus value was improved from 1 to 22 MPa, while for the rigid matrix the increase was from 106 to 443 or 667 MPa, in the case of composites with non-woven chicken feathers mat or lignin. To our knowledge this is the first study combining humins matrix with keratin. The obtained bio-composites are sustainable materials linked via the used raw materials to the circular economy and biomass valorization.
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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".