Adsorption of carbon dioxide, methane, and nitrogen: pure and binary mixture adsorption by ZSM-5 with SiO<sub>2</sub>/Al<sub>2</sub>O<sub>3</sub>ratio of 30
Bibliographic record
Abstract
The adsorption of binary gas mixtures of CO2–N2, CO2–CH4, and CH4–N2 were studied by using H-ZSM-5 as the adsorbent with a SiO2/Al2O3 ratio of 30. Pure isotherms for N2 and CH4 at 40°C and CH4–N2 binary isotherms at 40°C and 1.0 atm total pressure have been determined using concentration pulse chromatography. For CO2–N2 and CO2–CH4 pure and binary systems, previously published data were used. The applicability of the binary adsorption prediction models, Extended Langmuir, Extended Nitta, Ideal Adsorbed Solution Theory, and the Flory–Huggins form of the Vacancy Solution Theory have been studied. The CH4–N2 binary isotherms exhibit behavior similar to the pure component isotherms, with CH4 as the dominant adsorbate. The separation factor steadily declines as the mole fraction of CH4 in the gas phase is increased. All the theoretical models used reasonably predict the binary systems for CH4–N2. The CO2–N2 system was not predicted well. CO2–CH4 behavior was predicted reasonably well by all the models, except by the Extended Nitta. The models appear to be able to predict systems where the adsorption capacities of each component are relatively similar.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".