3D Bioprinting of Food Grade Hydrogel Infused with Living <i>Pleurotus ostreatus</i> Mycelium in Non-sterile Conditions
Bibliographic record
Abstract
Mycelium is the root-like network of fungi. Mycelium biocomposites prepared by template replication (molding) can function as environmentally friendly alternatives to conventional polystyrene foams, which are energy- and carbon-intensive to manufacture. Recently, several studies have shown that 3D bioprinting technologies can be used to produce high value functional mycelium products with intricate geometries that are otherwise difficult or impossible to achieve via template replication. A diverse range of nutrients, thickeners, and gelling agents can be combined to produce hydrogels suitable for 3D bioprinting. 3D bioprinting with hydrogel formulations infused with living fungi produces engineered living materials that continue to grow after bioprinting is complete. However, a hydrogel formulation optimized for intricate 3D bioprinting of Pleurotus ostreatus mycelium, which is among the strains most commonly used in mycelium biocomposite fabrication, has yet to be described. Here, we design and evaluate a versatile hydrogel formulation consisting of malt extract (nutrient), carboxymethylcellulose and cornstarch (thickeners), and agar (gelling agent), all of which are easily sourced food grade reagents. We also outline a reproducible workflow to infuse this hydrogel with P. ostreatus liquid culture for 3D bioprinting of intricate structures comprised of living P. ostreatus mycelium and characterize the changes in height and mass as well as hardness of the prints during mycelium growth. Finally, we demonstrate that the workflow does not require a sterile bioprinting environment to achieve successful prints and that the same mycelium-infused hydrogel can be supplemented with additives such as sawdust to produce mycelium biocomposite objects. These findings demonstrate that 3D bioprinting using mycelium-based feedstocks could be a promising biofabrication technique to produce engineered living materials for applications such as mushroom cultivation, food preparation, or construction of the built environment.
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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".