Interaction grand potential between calcium–silicate–hydrate nanoparticles at the molecular level
Bibliographic record
Abstract
Calcium-silicate-hydrate (or C-S-H), an inosilicate, is the major binding phase in cement pastes and concretes and a porous hydrated material made up of a percolated and dense network of crystalline nanoparticles of a mean apparent spherical diameter of ∼5 nm that are each stacks of multiple C-S-H layers. Interaction forces between these nanoparticles are at the origin of C-S-H chemical, physical, and mechanical properties at the meso- and macroscales. These particle interactions and the resulting properties may be affected significantly by nanoparticle density and environmental conditions such as the temperature, relative humidity, or concentration of chemical species in the bulk solution. In this study, we combined grand canonical Monte Carlo simulations and an extension of the mean force integration method to derive the pair potentials. This approach enables realistic simulation of the physical environment surrounding the C-S-H particles. We thus constructed the pair potentials for C-S-H nanoparticles of defined chemical stoichiometry at 10% relative humidity (RH), varying the relative crystallographic orientations at a constant particle density of ρpart ∼ 2.21 mmol L(-1). We found that cohesion between nanoparticles is affected strongly by both the aspect ratio and the crystallographic misorientation of interacting particles. This method and the findings underscore the importance of accounting for relative dimensions and orientation among C-S-H nanoparticles in descriptions of physical and simulated multiparticle aggregates or mesoscale systems.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".