Effect of a cement-bentonite grout on AVM glass alteration and C-steel corrosion at nanometer scale
Bibliographic record
Abstract
Two experimental mockups were operated for one year at 70°C to study the corrosion of C-steel and alteration of AVM glass surrounded by Cox claystone. Only one system included cement-bentonite grout (CBG). Nanometer-scale analyses (TEM, XANES) examined the glass/C-steel interface to assess CBG's effects. The most notable difference was the presence of a nanometric magnetite layer on the C-steel surface in the CBG system. This layer, promoted by the slightly alkaline pH (8–10) solution influenced by CBG, could act as a passivating barrier, potentially mitigating corrosion, although corrosion rates showed no significant differences over one year. A 5 µm-thick sodium-depleted gel layer of disordered SiO 2 formed on AVM glass in both systems, with similar porosity (14–20 nm). The open porosity may limit the gel's protective capacity. However, glass alteration rates decreased over time due to passivation mechanism primarily driven by reduced diffusion through the gel layer, with chemical affinity playing a lesser role. Secondary phases (Si-Fe-O, Si-Mg-O) were detected only in the CBG system, likely originating from the claystone or CBG rather than the glass itself. These findings indicate that CBG had little effect on AVM glass alteration but may enhance long-term C-steel corrosion resistance through magnetite layer formation.
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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) | 0.001 | 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) | 0.001 | 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".