Diffusion through sodium and polymer enhanced bentonites exposed to dilute and aggressive solutions
Bibliographic record
Abstract
Chemical incompatibility between sodium bentonite (NaB) and aggressive waste solutions has led to the development of enhanced bentonites for geoenvironmental applications. Enhanced bentonites, such as bentonite-polyacrylic-acid composite (BPC), have been shown to maintain low enough values of hydraulic conductivity (e.g., <10−10 m/s) for diffusion to be the dominant transport mechanism, even upon exposure to aggressive solutions. However, quantification of diffusion properties of enhanced bentonites has been limited. In this study, apparent diffusion coefficients (Da) for chloride were measured for NaB and polymer-enhanced NaBs. Diffusion tests were performed using dilute (5 mmol/L) to aggressive (100 mmol/L) calcium chloride (CaCl2) solutions. In addition, scanning electron microscopy was performed to support interpretation of diffusion results. For CaCl2 solutions <100 mmol/L, Da values for BPC were lower (by approximately half an order of magnitude) than Da values for NaB. However, differences in Da due to polymer enhancement diminished as CaCl2 concentration increased. Predicted steady-state flux through a BPC geosynthetic clay liner (GCL) was dominated by diffusion and up to two orders of magnitude lower than flux through an NaB GCL. These results provide insight regarding diffusion in polymer-enhanced bentonites and expected performance of containment barriers with enhanced-bentonite GCLs.
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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) | 0.000 | 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".