Zigzag pore based molecular simulation on the separation of CO<sub>2</sub>/CH<sub>4</sub> mixture by carbon membrane
Bibliographic record
Abstract
A zigzag‐type pore structure was proposed for the separation of a CO2/CH4 binary mixture by carbon molecular sieve membrane (CMSM). A defect‐free pore and three kinds of defect pores, i.e., random, uniform, and partial defect, were considered to improve the separation performance. Adsorption and diffusion behaviours of pure gases and a gas mixture were simulated by the grand canonical Monte Carlo (GCMC) method and non‐equilibrium molecular dynamics (NEMD) method, respectively. The pressure ranged from 10–100 kPa and the temperature was between 273–348 K during simulation. The calculated isotherms of CO2 and CH4 showed an acceptable agreement with the experimental data for the defect‐free pores of 0.67 nm in size at 298 K. Examination on the effect of pore sizes showed that 0.67 nm was the appropriate option for separation. The total selectivity was 20.1 at 298 K and 100 kPa with the pore size of 0.67 nm, which is consistent with the experimental value. The adsorption was determined as the dominant separation mechanism between adsorption and diffusion. Compared with the defect‐free pores, the introduction to the random and uniform defect pores can improve the total selectivities and the random defect provided superior separation performance. An appropriately low temperature and small pore size were beneficial to the 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) | 0.001 | 0.001 |
| 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".