Engineering shootable mycelium-bound composites (MBCs) as living building materials
Bibliographic record
Abstract
Conventional building materials, such as concrete, steel, and brick, are energy-intensive to produce, contribute to significant carbon emissions, and deplete finite natural resources. In response to critical environmental issues caused by these non-renewable materials, mycelium-based composites (MBCs) have attracted widespread attention. MBCs provide a renewable alternative where mycelium grows on lignocellulosic fibers forming a lightweight, low energy cost, and an effective insulating material. However, the bottleneck of large-scale architectural applications of MBCs lies in conventional mold-based manual manufacturing processes. Even though 3D-printable mycelium composites facilitate automated production and greater design freedom, the size is still limited at the centimeter scale due to the extended time required for layers to fuse from hyphal growth. The development of shootable MBCs, which can potentially overcome these constraints for flexible forms and automated large-scale construction, has not yet been explored. This study investigates biopolymer modification methods for psyllium husk gels to engineer shootable MBCs with high cohesive and adhesive properties that enable deposition on vertical surfaces. Engineered formulations demonstrated consistent shootability over a span of 50 minutes with minimal material loss (<10%), and robust mycelium growth at both the surface and throughout the cross section. The resulting composition exhibits circular building lifecycle principles as all ingredients are biodegradable and compostable at the end of building life. Additionally, a shootable composite broadens the fabrication methods and applications of MBCs, showing promise for use with shotcrete-like spraying techniques in the building industry.
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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 | 0.000 |
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".