Synthesizing Hydrophobic Sand and Comparison of Shear Strength Properties with Hydrophilic Sand
Bibliographic record
Abstract
Soil can become hydrophobic following wildfires, oil-spills, or due to the presence of organic substances. While this phenomenon has been widely observed by researchers, there is little information regarding the shear strength properties of hydrophobic soil to date; therefore, this study investigated the shear strength of artificially hydrophobized sand particles. Hydrophobic particles were created by coating Ottawa sand particles with organic silane to form water repellent films around the particles. Direct shear tests were conducted at multiple normal stresses for the naturally hydrophilic and artificially hydrophobic sands under dry and submerged conditions. In the dry condition, the hydrophobic sand showed an average 26% reduction in peak shear stress, with the angle of friction reducing from 36.9 to 29.7° compared to the hydrophilic sand. The average peak shear stress reduction was 34%, with the angle of friction reducing from 38.7 to 26.1° in the submerged test. These results indicate that particle hydrophobicity reduces shear strength. This study highlights that with the increasing number of disasters that results in soil hydrophobicity, more research is needed on the strength properties of hydrophobic soils.
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".