Polylactide-Based Renewable Green Composites from Agricultural Residues and Their Hybrids
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Agricultural natural fibers like jute, kenaf, sisal, flax, and industrial hemp have been extensively studied in green composites. The continuous supply of biofibers in high volumes to automotive part makers has raised concerns. Because extrusion followed by injection molding drastically reduces the aspect ratio of biofibers, the mechanical performance of injection molded agricultural residue and agricultural fiber-based composites are comparable. Here, the use of inexpensive agricultural residues and their hybrids that are 8-10 times cheaper than agricultural fibers is demonstrated to be a better way of getting sustainable materials with better performance. Green renewable composites from polylactide (PLA), agricultural residues (wheat straw, corn stover, soy stalks, and their hybrids) were successfully prepared through twin-screw extrusion, followed by injection molding. The effect on mechanical properties of varying the wheat straw amount from 10 to 40 wt % in PLA-wheat straw composites was studied. Tensile moduli were compared with theoretical calculations from the rule of mixture (ROM). Combination of agricultural residues as hybrids is proved to reduce the supply chain concerns for injection molded green composites. Densities of the green composites were found to be lower than those of conventional glass fiber composites.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it