Development and characterization of mycelium bio-composites by utilization of different agricultural residual byproducts
Bibliographic record
Abstract
Mycelium bio-composites was developed by incubating Pleurotus ostreatus fungi on different substrates from agricultural residual byproducts, including rice straw, bagasse, coir-pith, sawdust, and corn straw. The scanning electron microscope (SEM) results showed that the hypha of composite derived from bagasse was the densest, and the diameter of hypha was the biggest (0.77 µm), which was presumably due to the existence of cellulose in bagasse in the form of dextran and xylan. The maximum and minimum compression strength for sawdust substrate and corn straw substrate were 456.70 and 270.31 kPa, respectively. The flexural strength for bagasse substrate and rice straw substrate were 0.54 and 0.16 MPa, respectively. The two composites derived from rice straw and bagasse exhibited higher hydrophobic properties than others. In comparison, mycelium bio-composite derived from bagasse showed the best comprehensive properties. Except for a little worse anti-creep ability and waterproof performance, other properties of mycelium bio-composites could be comparable to commercially expanded polystyrene (EPS) packaging material. Derived from this study, mycelium material provided a good way to use agricultural residual byproducts and could be a good alternative to non-biodegradable materials for packaging applications.
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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) | 0.000 | 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 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".