Diffusion of CH <sub>4</sub> and N <sub>2</sub> in Barium-Exchanged Reduced Pore Zorite (Ba-RPZ) and Zeolite 4A
Bibliographic record
Abstract
Barium-exchanged reduced pore zorite (Ba-RPZ) is a titanosilicate molecular sieve that separates CH4 from N2 based on their relative molecular sizes. A detailed study of N2 and CH4 adsorption equilibrium and diffusion on Ba-RPZ was completed using low and high-pressure volumetry. Adsorption equilibrium data for Ba-RPZ from vacuum to 1.2 bar were measured at 30, 40, and 50 °C for CH4 and at 30, 50, and 70 °C for N2. Constant volume uptake experiments were conducted to estimate the diffusivities of CH4 at 30, 40, and 50 °C and N2 at −20, −10, and 0 °C. Similar experiments were carried out with zeolite 4A to validate the methods used in this study. On the one hand, the transport of N2 in Ba-RPZ was found to be controlled by diffusion in the micropores. On the other hand, the transport of CH4 in Ba-RPZ was described by a dual-resistance model, including a barrier resistance and micropore diffusional resistance. Both the barrier and micropore diffusion coefficients demonstrated concentration dependence. While the micropore diffusion constant followed Darken’s relationship, the barrier resistance did not. The activation energies of the micropore diffusion and barrier resistance for CH4 on Ba-RPZ were calculated to be 30.46 and 60.19 kJ/mol, while that of micropore diffusion for N2 on Ba-RPZ was calculated to be 25.77 kJ/mol. A concentration-dependent dual-resistance diffusion model for methane was constructed and validated using experimental data across a range of pressures and temperatures. The concentration-dependent dual-resistance model was able to describe the complex diffusion behavior methane displays as it progressed from the dual-resistance controlled region to the micropore-controlled region of the isotherm. The calculated limiting N2/CH4 kinetic selectivity of Ba-RPZ was shown to be ∼3 orders of magnitude larger than the current benchmark material for CH4/N2 separation (Sr-ETS-4).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".