Investigating temperature effects on the shear behavior of clays: molecular dynamics simulations
Bibliographic record
Abstract
The shear behavior of clays is critically important for the stability and safety of nuclear waste repositories and clay gouges. These contexts expose clayey geomaterials to high pressures and temperatures. Under such varying thermodynamic conditions, the shear behavior of these materials becomes complex, necessitating thorough investigations. This study aims to elucidate the effect of temperature on the shear behavior of three clayey materials—kaolinite, illite, and montmorillonite—through molecular dynamics simulations. The research replicates a geotechnical shear setup at the molecular scale, varying the environmental temperature including sub-freezing temperatures below 300 K and elevated temperatures in the range of 300–500 K and hydrostatic pressure. The results reveal stick–slip behavior, enabling the calculation of nanoscale cohesion, friction angle, and shear modulus across different temperatures. Thermal effects are notably significant for illite and kaolinite, both exhibiting a marked decrease in shear properties with increasing temperature. Kaolinite demonstrates a high shear modulus of up to 40 GPa, indicating substantial shear strength compared to montmorillonite and illite. Illite displays the highest friction angle among the studied materials, approximately 5°. For montmorillonite, the influence of temperature on shear behavior is comparatively less pronounced. This study provides critical insights into the nanoscale mechanical behavior of clay minerals under varying thermal conditions, contributing to the broader understanding of geomaterial stability in high-stake environments such as nuclear waste containment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".