Approach to Sustainable Fibers from Spent Mushroom Substrate for Future All-Natural-Materials
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Spent mushroom substrates (SMS), a lignocellulosic residue from mushroom cultivation, represent a promising raw material for the valorization of nontoxic materials supporting the circular bioeconomy. The inherent biological pretreatment of the birch wood substrate during shiitake cultivation reduces the need for chemicals prior to fibrillation. SMS was fibrillated using an extruder and a blender at high (28 wt %) and low (5 wt %) solid contents, respectively, with and without a predispersion step. Extrusion proved to be the most energy-efficient method, requiring only 11 kWh/t, compared with 417 kWh/t for blending. When combined with predispersion, extrusion is the second most energy-efficient fibrillation method (789 kWh/t), compared to blending with predispersion (1195 kWh/t). Microscopy and fiber fractionation confirmed fibrillation into microfibers after extrusion and the presence of residual mycelium. Sheet formation by vacuum filtration over a coarse mesh significantly lowered the filtration time compared to a fine filter. Sheets produced from fibrillated SMS possessed tensile strength up to 7.5 times higher than commercial birch kraft pulp sheets prepared under the same conditions. The improved tensile strength is due to the presence of mycelial fibrils, which enhanced fiber–fiber bonding. Overall, extrusion provides a scalable, energy-efficient route for SMS fibrillation for the production of future all-natural materials without the need for chemical modification.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".