Exploring Bisulfide Transport through Bentonite Under Deep Geological Repository Conditions
Bibliographic record
Abstract
Management of used nuclear fuel is a major technical challenge faced by nuclear energy producing nations worldwide. In Canada, the Nuclear Waste Management Organization is responsible for the design and implementation of a deep geological repository (DGR), which will be placed ~500 m below ground surface in a stable host rock to safely contain and isolate used fuel. Within a DGR, used nuclear fuel will be placed in used fuel containers (UFCs) that are encased in copper because of copper’s corrosion resistance. The UFCs will be surrounded by highly compacted bentonite to suppress the transport of corrosive agents to the UFC and limit the movement of radionuclides out of a DGR in the unlikely case of a breach. Over the design lifespan of one million years for the DGR, it is possible that sulfate-reducing bacteria near the bentonite-host rock interface can produce bisulfide (HS - ) that can be transported to the UFC surface and potentially corrode the outer copper barrier. Therefore, it is crucial to understand HS - transport mechanisms through bentonite to assess the long-term performance of a DGR. This study aims to quantify HS - transport through bentonite using through-diffusion experiments under a range of anticipated DGR and bentonite conditions (e.g., temperature, ionic concentration, bentonite densities). In addition, as geochemical reactions/sorption are expected to affect HS - transport, batch experiments are being conducted to understand these processes independent of transport. The preliminary batch of experimental results show that ~85% of HS - was partitioned from the aqueous to solid phase within first hour and ~97% after 24 hours. However, this partitioning efficiency decreased with increased liquid-to-solid ratio because of the reduction in available reaction/sorption sites on the bentonite. This presentation shows the results from both transport and batch experiments performed on the bentonite under relevant DGR conditions.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".