Phase Equilibria of CO<sub>2</sub> and <i>n</i>-Alkanes in Bulk and Confined Space Using Parallelized Wang–Landau Transition-Matrix Monte Carlo Simulations
Bibliographic record
Abstract
The accurate and fast simulation of CO 2 and n -alkane phase equilibria is crucial for guiding their industrial applications. We used Wang–Landau Transition-Matrix Monte Carlo (WL-TMMC) with the Free Energy and Advanced Sampling Simulation Toolkit (FEASST) software to compute the vapor–liquid equilibrium (VLE) of CO 2 -methane and CO 2 -hexane systems in both bulk and confined spaces. The bulk-phase simulation results were compared with literature data and constant volume Gibbs Ensemble (NVT-GEMC) results, with relative errors less than 6%. For confined systems, the results were compared with gauge cell grand-canonical Monte Carlo (gauge-GCMC) and pore–pore GEMC, with relative errors less than 8%. Notably, the WL-TMMC exhibits significant advantages in computing VLE for confined spaces. It requires only a single simulation to determine a pair of VLE points without being constrained by prespecified chemical potentials or pore geometry. Furthermore, the method provides free energy information for different fluid states, enabling the construction of a complete van der Waals loop from a single simulation. In conclusion, we demonstrate that WL-TMMC in FEASST is a robust and reliable tool for studying CO 2 - n -alkane VLE.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".