Modelling key reactive processes relevant to bisulfide transport through highly compacted bentonite
Bibliographic record
Abstract
• Reactive bisulfide (HS - ) transport in deep geological repositories (DGRs) was modelled. • Models coupled HS - transport with HS - reactions with iron species in bentonite clay. • HS - was retained by the bentonite due to reactive processes. • Anion exclusion was found to be occurring in bentonite. • Relatively short HS - diffusion delays do not impact copper container corrosion. The Canadian deep geological repository (DGR) design consists of copper coated used fuel containers (UFCs) placed within a highly compacted bentonite (HCB) buffer surrounded by a suitable host rock. Although the copper is thermodynamically stable in oxygen-free environments, it is potentially susceptible to microbiologically influenced corrosion from bisulfide (HS - ). Therefore, understanding HS - corrosion is important to ensure long-term performance of UFCs. Various reactions in the bentonite barrier of the DGR can affect HS - transport through the HCB and therefore the extent of copper corrosion caused by HS - . Since HS - transport and reactive processes are interconnected, numerical models are required to assess the complex HS - reactive transport dynamics and quantify the influence of reactive processes on HS - transport and corrosion. In this paper, various HS - transport models were coupled with (i) a key geochemical reaction between HS - and iron (i.e., simulating HS - retardation due to iron sulfide formation) or (ii) HS - adsorption. Since HS - is an anion, anion exclusion was also explored. Valuable insight was obtained through validation, comparison, and sensitivity analyses of these models. A comparison between experimental and modelled HS - transport dynamics showed that HS - is being retained by the bentonite due to reactive processes and anion exclusion is occurring. Lastly, HS - transport was simulated for the entire DGR lifespan and was found to be delayed ( ≈ 50–800 years) due to FeS formation or HS - adsorption. However, these predicted HS - diffusion delays are relatively short in a DGR lifespan (i.e., 1 million years) and do not impact long-term HS - corrosion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".