Microstructure-Driven Hygrothermal Behavior of Mycelium-Based Composites for Bio-Based Insulation
Bibliographic record
Abstract
This study investigates the coupled hygrothermal behavior of mycelium-based composites (MBCs) as a function of their microstructural organization, governed by fungal species, substrate type, additive incorporation, and treatment method. Eleven composite formulations were selected and characterized using a multi-scale experimental approach, combining scanning electron microscopy, dynamic vapor sorption, vapor permeability tests, capillary uptake measurements, and transient thermal conductivity analysis. SEM analysis revealed that Ganoderma lucidum forms dense and interconnected hyphal networks, whereas Trametes versicolor generates looser, localized structures. These morphological differences directly influence water vapor transport and heat conduction. Additive-enriched composites exhibited up to 21.8% higher moisture uptake at 90% RH, while straw-based composites demonstrated higher capillary uptake and free water saturation (up to 704 kg/m3), indicating enhanced moisture sensitivity. In contrast, hemp-based formulations with Ganoderma lucidum showed reduced sorption and vapor permeability due to limited pore interconnectivity. Thermal conductivity varied nonlinearly with temperature and moisture content. Fitting the experimental data with an exponential model revealed a moisture sensitivity coefficient thirty times lower for GHOP compared to VHOP, highlighting the stabilizing effect of a compact microstructure. The distinction between total and effective porosity emerged as a key factor in explaining discrepancies between apparent and functional moisture behavior. These findings demonstrate that hygric and thermal properties in MBCs are governed not by porosity alone, but by the geometry and connectivity of the internal fungal network. Optimizing these structural features enables fine control overheat and mass transfer, laying the groundwork for the development of high-performance, bio-based insulation materials.
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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".