Hydrophobic and mechanical enhancement of silica sand treated with tung oil
Bibliographic record
Abstract
This study investigated the hydrophobic and mechanical enhancement of silica sands treated with tung oil, a natural hydrophobic stabilizer, for road embankment applications. Through laboratory testing including apparent contact angle (ACA) and water drop penetration time (WDPT) measurements, uniaxial compression tests, and direct shear box tests, the effects of mean particle size (0.24–0.99 mm), tung oil concentration (0.01%–5%), and heating duration (1–14 days at 60 °C) were quantified. Key findings revealed: (1) peak hydrophobicity (ACA = 95.1°–128.7°, WDPT > 3600 s) occurred at 0.05% tung oil concentration, beyond which a reduced water repellency was observed; (2) both peak and near-constant-volume shear strength, along with their corresponding parameters (friction angle and cohesion), increased with tung oil concentration and heating duration but decreased with larger mean particle size, due to the decreased interparticle bonding demonstrated by scanning electron microscopy analysis; (3) stabilization efficiency showed a similar trend to shear strength but decreased with higher normal stresses. The results demonstrated tung oil’s potential dual functionality for cost-effective, zone-specific subgrade treatment, with low concentrations (0.05%) serving as impervious barriers and higher concentrations (3%) forming bonded load-bearing layers.
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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) | 0.001 | 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".