Pure mycelium materials production from agri‐processing water: Effects of feedstock composition on material properties for packaging applications
Bibliographic record
Abstract
Abstract In this work, pure mycelium materials (PMMs) were produced by cultivating fungi Trametes versicolor and Irpex lacteus on lignocellulose‐rich agricultural processing water. This water was a side stream from the alkali treatment of purposely grown biomass (miscanthus) for cellulose fiber extraction and contained lignocellulosic residues. Agri‐processing water yielded ~75‐mg/L PMMs with superior mechano‐physical properties than synthetic medium‐based ones. These properties were further enhanced by PMM post‐processing with glycerol. The thermal stability of PMMs was demonstrated by their higher melting temperature than low density polyethylene (LDPE) while their degradation between 200–380°C, and density of <1.0 g/cm 3 , like LDPE. Their mechanical performance was studied on filmlike specimens via dynamic mechanical analyzer. PMMs showed a viscoelastic behavior with a high storage modulus of 34 MPa at 65°C suggesting their suitability for packaging applications. This work provides guidelines on optimizing PMM production using agri‐processing water to obtain tunable mechano‐physical properties. Practitioner Points Valorization of agri‐processing water to produce high‐value PMM packaging products. No pure or expensive nutrient supplementation was needed for agri‐based feedstock. Relationships between feedstock composition and PMM properties were established. PMMs showed a similar thermal profile and density as typical petro‐based packaging materials. The addition of glycerol postproduction induced flexibility in PMMs.
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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 | 0.000 | 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 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".