Synergistic effects of density, gradation, particle size, and particle shape on the water entry pressure of hydrophobized sands
Bibliographic record
Abstract
This paper examines the specific role and interdependencies between soil density, particle size, particle shape, and gradation on the water entry pressure of hydrophobized sands. The tested granular materials include quartz sands of three size ranges, glass beads, and crushed glass. All granular materials were initially silanized with polydimethylsiloxane (PDMS) coatings to achieve the same intrinsic hydrophobicity. The water-ponding method was adopted to measure the water entry pressure in a comprehensive parametric study that consisted of 96 tests. Despite an acute sensitivity of the water entry pressure to relative density, the results revealed a more dominant effect of particle size. The largest water entry pressure (hydrophobized fine sand) was 14 times that of the lowest (hydrophobized coarse sand). Relatively higher water entry pressures were recorded with sands having larger coefficients of uniformity. A comparison between hydrophobized glass beads and crushed glass also revealed that rounded particles were less effective in retarding water infiltration in a dense state. A relationship, based on Jurin's law, was proposed for dense sands whereby the water entry pressure is obtained from the coefficient of uniformity, mean particle size, and particle shape.
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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 | 0.001 |
| 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".