Determination of diffusion and adsorption coefficients for volatile organics in an organophilic clay - sand - bentonite liner
Bibliographic record
Abstract
In the design of barriers for containment of petroleum products it is essential to know the conditions for contaminant transport. In this work, a batch test method was used to determine the adsorption coefficients (Kd) of benzene, toluene, and 2-fluorotoluene (a tracer for toluene) on three soils. For Ottawa sand using soil to water ratios of 0.100.30 g/mL, Kd values were 2.51.2 mL/g (benzene), 11.33.6 mL/g (toluene), and 10.93.7 mL/g (2-fluorotoluene), respectively. Using organophilic clay at similar soil to water ratios, the Kd values were 40.950.0 mL/g (benzene), 154129 mL/g (toluene), and 157114 mL/g (2-fluorotoluene), respectively. Kd values for bentonite were 37.60.14 mL/g (benzene), 60.316.5 mL/g (toluene), and 51.233.6 mL/g (2-fluorotoluene) using soil to water ratios in the range 0.010.05. In general, for a given mixture, toluene was two to five times more adsorptive than benzene, indicating that hydrophobicity was an important factor in their adsorption. The diffusion coefficients in material comprised of 3% organophilic clay, 12% bentonite, and 85% Ottawa sand ranged from 0.48 × 106 to 2.5 × 106 cm2/s at 20°C. These values are lower than those measured for natural clay with low organic carbon content.Key words: diffusion, adsorption, volatile organics, organophilic clay, liner materials.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".