Benjamín Petržela

Recycled cotton fibers offer a promising route for sustainable thermal insulation, but their flammability limits wider application. This study evaluated rigid recycled cotton insulation boards bonded with a polyvinyl acetate (PVAc)–starch system and modified with kraft lignin or aluminium trihydroxide (ATH) at dosages of 10 and 15 wt.%. Board density, thermal conductivity, volumetric heat capacity, thermal diffusivity, cone-calorimeter behavior, and ignitability under single-flame exposure were assessed. Thermal conductivity remained within a narrow range of 0.071–0.075 W·m⁻¹·K⁻¹, indicating that the basic insulation function was preserved despite formulation changes. Greater differences were observed in volumetric heat capacity and thermal diffusivity, particularly in lignin-modified boards at higher PVAc contents. Fire behavior was strongly formulation-dependent and showed no uniform dose-dependent response. The lowest peak heat release rate (pHRR) was obtained for the formulation containing 10 wt.% ATH and 20 wt.% PVAc, while the lowest total heat release (THR) was obtained for the formulation containing 10 wt.% ATH and 18 wt.% PVAc, reaching 170.31 kW·m⁻² and 62.30 MJ·m⁻², respectively. Lignin produced a less consistent effect on heat release. Under single-flame exposure, edge application was generally more critical than surface application, while ATH, particularly at 15 wt.%, provided the most consistent reduction in ignition and early flame spread. Overall, ATH was the more promising fire-modifying additive, although further formulation optimization and formal reaction-to-fire classification are required.

Summer overheating is an escalating challenge for lightweight timber constructions, which inherently lack the thermal mass of traditional masonry. This study investigates the thermo-physical properties of a mycelium-based biocomposite (MBB) insulation produced from industrial wood waste, with particular focus on volumetric heat capacity (C_v). The C_v and thermal conductivity (λ) of MBB were experimentally measured and benchmarked against seven reference insulation materials spanning bio-based, mineral, and petroleum-derived categories, with results visualized on an Ashby diagram. The areal heat capacity (κ) of nine representative wall assemblies was theoretically calculated per EN ISO 13786. Even though the MBB achieved the highest thermal conductivity (λ = 0.0641 ± 0.0024 W·m⁻¹·K⁻¹) among the tested insulation materials, it offers 4.7 times higher C_v than EPS. Analytical modeling indicates a thermal phase shift of 8.2 h for a 185 mm layer, compared to 4.6 h for EPS. The softwood timber + MBB wall assembly achieved an areal heat capacity approaching the lower boundary of traditional masonry performance. These findings demonstrate MBB’s potential as a bio-based, waste-derived insulation for significantly enhancing the thermal inertia of lightweight timber buildings and mitigating summer overheating risk.

Mycelium-based biocomposites (MBBs) represent a sustainable alternative to synthetic composites, as they are produced from lignocellulosic substrates bonded by fungal mycelium. Their mechanical performance depends on multiple interacting factors, including the substrate composition, fungal species, and processing conditions, which makes property optimisation challenging. In this study, an artificial neural network (ANN) model was developed to predict two mechanical properties of MBBs, namely internal bonding (IB) and compressive strength (CS). An ANN model was trained on experimental data, using the substrate composition, fungal species, and physical properties of MBBs. The ANN predictions were compared with measured values, and the model accuracy was evaluated. The results showed that the ANN achieved a high predictive accuracy, with coefficients of determination of 0.992 for IB and 0.979 for CS. IB values were predicted more precisely than CS, likely due to microstructural heterogeneities. The heterogeneities were visualised using scanning electron microscopy. Composites produced with Ganoderma sessile and Trametes versicolor exhibited the highest IB. Interestingly, Trametes versicolor achieved the highest CS on virgin wood particles but the lowest values on recycled wood, underlining the strong influence of the substrate quality. The study demonstrates that ANNs can effectively predict the mechanical properties, reducing the number of experimental tests needed for material characterisation.