Molecular dynamics predictions of transport properties for carbon dioxide hydrates under pre-nucleation conditions using TIP4P/Ice water and EPM2, TraPPE, and Zhang carbon dioxide potentials
Bibliographic record
Abstract
(1) Introduction: New technologies that leverage gas hydrates phenomena include carbon capture and sequestrations. These processes are often semi-continuous and require regulation of the system's flow properties for proper operation. Accurate computational models for the viscosity of carbon dioxide hydrate systems at pre-nucleation conditions can be important for process design and control of such technologies. (2) Methods: This work validates the viscosity predictions of molecular dynamics simulations using previously measured experimental data. The TIP4P/Ice force field was used to model water, while the EPM2, TraPPE, and Zhang force fields were used for carbon dioxide. The Green-Kubo and Einstein formulations of viscosity and diffusivity were used in this work. (3) Results: All force fields overpredicted viscosity when compared to experimental data, but EPM2 resulted in lower discrepancies. Additionally, EPM2 was determined to model molecular behavior expected from the macroscopic trends in viscosity with respect to temperature and pressure. (4) Conclusions: The EPM2 force field more accurately predicted the viscosity of carbon dioxide hydrates systems at pre-nucleation conditions relative to TraPPE and Zhang.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".