High-Pressure CO<sub>2</sub> and CH<sub>4</sub> Transport in Smooth Crystalline Silica Mesopores: A Molecular Dynamics Study
Bibliographic record
Abstract
In this study, we use molecular dynamics (MD) simulation to study pressure-driven CO 2 and CH 4 flows and their slippage behaviors in β-cristobalite mesopores. The result illustrates that both CO 2 and CH 4 have an apparent adsorption layer on pore surface. However, significant differences in gas slippage are observed: CH 4 flow shows considerable slippage, while it is negligible for CO 2 flow. This disparity is attributed to the collective effect of gas molecular configurations and surface structure. The linear molecular structure of CO 2 allows it to align perpendicular to the surface, even penetrating into the surface. Notably, the perpendicular orientation of CO 2 molecules is energetically favored near the center of the equilateral triangle formed by adjacent oxygen atoms on β-cristobalite surface. Conversely, the symmetric molecular structure of CH 4, coupled with its larger size, prevents its penetration into pore surfaces. Therefore, despite smooth crystalline surfaces, CO 2 topological accessible plane is much more curved than that of CH 4 . Consequently, CO 2 displays hesitating motions undergoing rotational movements, which significantly hinders its slippage. This study highlights the collective influences of gas molecular characteristics and surface structure on gas slippage, affording important insights into gas sequestration and the development of functional materials for gas separation.
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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) | 0.000 | 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 | 0.000 |
| 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".