Investigation of hybrid substrates of waste paper and hemp hurds for mycelium-based materials production
Bibliographic record
Abstract
Hemp hurds are popular for producing mycelium-based materials (MBMs), which are emerging biomaterials for the packaging industry. However, hemp hurds have been repurposed for alternative uses, resulting in potential challenges of limited availability and increased costs of substrates for manufacturing MBMs. This study aimed to reduce the reliance on hemp hurds as a substrate in the production of MBMs.Waste paper, which is more readily available and cost-effective, was blended with hemp hurds to create mycelium-based materials in this research. The fabrication duration and physical and mechanical characteristics of samples were assessed and compared to those made of pure hemp hurds substrates. The results showed that samples made of hybrid substrates exhibited longer production duration than those from 100 % hemp hurds. In addition, different fungal species have varying abilities to digest hybrid substrates, resulting in different morphology of the final product. Fomes fomentarius- based samples had a velvety and foam-like appearance, Ganoderma lucidum -based samples exhibited a more compact structure, and Trametes pubescens- based samples showed a loose structure with less mycelium skin on the surface. The study demonstrated that adding waste paper to the substrates increased the dry density of final products. Non-etheless, the dry density of the final products (0.097 g/cm 3 – 0.145 g/cm 3 ) remained competitive because it was significantly lower than that of pulp moulding packaging (0.2–1.0 g/cm 3 ), a commonly used green packaging material nowadays. In addition, incorporating waste paper increased products’ compressive properties. Compressive strength at 35 % strain was increased by 13.9 % for Ganoderma lucidum -based samples, 25.7 % for Fomes fomentarius -based samples, and 30.8 % for Trametes pubescens -based samples.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| 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 teacher head, 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".