Thermodynamics, kinetics, and mechanics of cesium sorption in cement paste: A multiscale assessment
Bibliographic record
Abstract
Cesium-137 is a common radioactive byproduct found in nuclear spent fuel. Given its 30 year half life, its interactions with potential storage materials---such as cement paste---is of crucial importance. In this paper, simulations are used to establish the interaction of calcium silicate hydrates (C-S-H)---the main binding phase of cement paste---with Cs at the nano- and mesoscale. Different C-S-H compositions are explored, including a range of Ca/Si ratios from 1.0 to 2.0. These calculations are based on a set of 150 atomistic models, which qualitatively and quantitatively reproduce a number of experimentally measured features of C-S-H---within limits intrinsic to the approximations imposed by classical molecular dynamics and the steps followed when building the models. A procedure where hydrated ${\mathrm{Ca}}^{2+}$ ions are swapped for ${\mathrm{Cs}}^{1+}$ ions shows that Cs adsorption in the C-S-H interlayer is preferred to Cs adsorption at the nanopore surface when Cs concentrations are lower than 0.19 Mol/kg. Interlayer sorption decreases as the Ca/Si ratio increases. The activation relaxation technique nouveau is used to access timescales out of the reach of traditional molecular dynamics (MD). It indicates that characteristic diffusion time for ${\mathrm{Cs}}^{1+}$ in the C-S-H interlayer is on the order of a few hours. Cs uptake in the interlayer has little impact on the elastic response of C-S-H. It leads to swelling of the C-S-H grains, but mesoscale calculations that access length scales out of the range of MD indicate that this leads to practically negligible expansive pressures for Cs concentrations relevant to nuclear waste repositories.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".