Anion Transport Through Bentonite Under Various Geochemical Conditions
Bibliographic record
Abstract
The use of bentonite clay in industrial applications is widespread: it is used as an engineered barrier for long-term management of radioactive wastes, CO2 storage, landfill liners, and contaminant containment. These applications have diverse environmental conditions ranging from various temperatures, pHs, saline contents, and ionic concentrations. Since bentonite is a low permeability clay, anion transport is diffusion dominated but geochemical reactions can also play a significant role and transport will be affected by environmental conditions. In this study, anion transport (bisulfide) under various conditions was examined using experimental and numerical techniques to understand the various geochemical and surface mediated reactions that are occurring in the bentonite. The case study presented is for the use of bentonite in long term storage of nuclear waste but can be extended to other applications.First, diffusion experiments were performed to examine the transport and reactive nature of bisulfide (HS-) through bentonite compacted at dry density of 1090-1330 kg m-3. Experimental data of bisulfide transport were fitted using the inverse solution technique of Hydrus-1D model and different fitting parameters (e.g., diffusion, sorption, and reaction sink). Simulation results suggest that the HS- sorption/reaction affecting itsdiffusive transport through bentonite can be modeled using a simple nonlinear adsorption process.Second, batch experiments were performed to understand the maximum allowable sorption that could take place under key geochemical conditions, including temperature, pH, and ionic strength. The results of batch sorption experiments performed suggest that HS- sorption increases with increasing temperature but decreases with increasing pH and ionic strength.Lastly, since transport and reactive processes are interconnected, the results of these experiments were incorporated into a 1D transport COMSOL model to understand which geochemical process governs bisulfide transport through bentonite. Various processes were examined including linear and non-linear sorption, reactive transport, and anion exclusion. The model was validated using the experiments and showed that HS- was retained in the bentonite due to reactive processes and anion exclusion effects.
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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".