Temperature-dependent behaviour and microstructure of fungus-treated carbonate sand
Bibliographic record
Abstract
This study investigates a bio-mediated reinforcement technique for carbonate sand using fungal mycelium, emphasising its temperature-dependent mechanical performance under elevated temperature conditions. Specimens composed of Pleurotus ostreatus, wheat bran, and carbonate sand are subjected to undrained triaxial shear tests at 20, 35, and 50 °C. The microstructural mechanisms underlying the thermal response are also analysed to support the interpretation of the strength behaviour. The results indicate that fungal treatment significantly enhances shear strength, reduces pore pressure accumulation, and raises dilative behaviour during shear. As the temperature increases, the mechanical response transitions from strain hardening to strain softening, with peak strength and stiffness increasing by approximately 20%. With rising temperature, the strength parameters exhibit a reduction in cohesion and an increase in internal friction angle, indicating a shift from bonding-dominated to friction-dominated behaviour. Thermal exposure induced hyphal shrinkage and fusion, enhancing interparticle bonding through the formation of adhesive bridges. These changes reduce surface porosity and pore circularity by over 50%, contributing to matrix densification and enhanced stability. These findings demonstrate that fungal mycelium enables thermally resilient, low-carbon ground improvement, particularly in tropical, coastal, and carbonate-rich environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".