Sustainable Green Composites: Value Addition to Agricultural Residues and Perennial Grasses
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide This work has explored the potential use of lignocellulosic agricultural residues like soy stalk, corn stalk, wheat straw, and perennial grasses, like switchgrass and miscanthus, as reinforcement for engineering value-added biobased composite materials. The effect of incorporating 30 wt % lignocellulosic fibers into a biodegradable polymer matrix comprising a pre-blend of poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) and poly(butylene adipate-co-terephthalate) (PBAT) has been investigated. Results of this work explain that fiber chemical composition and fiber length distribution provide a complementary effect on the mechanical and thermal properties of the resulting biobased composites. Comparing the effects of all the fiber types, miscanthus (MS)-based composites, showed slightly higher tensile strength, and Young’s modulus improved by 104%. The highest heat deflection temperature of 110 °C was obtained with PHBV/PBAT/MS composites. This study has revealed prospects for hybridization of these lignocellulosic fibers to fabricate hybrid composites with enhanced performance.
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.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".