Novel Differential Column Method for Measuring Multicomponent Gas Adsorption Isotherms in NaY Zeolite
Bibliographic record
Abstract
Separation and purification of gas mixtures using selective adsorbents is widely used in different industries such as gas drying, air separation, and H 2 purification. Equilibrium analysis involving adsorption of binary gas mixtures provides important information related to the adsorbent performance in the separation of gases. In this study, a novel technique termed “differential column technique” was developed for binary isotherm measurement employing streams containing carbon dioxide, carbon monoxide, and ethylene at different compositions. This technique is based on measuring the gas desorption by changing equilibrium pressure conditions. The isotherm curve was generated by summing desorption amounts desorbed at each pressure step. Through the application of this technique, the single-component isotherms of CO 2, CO, and ethylene on zeolite NaY were measured, and the isotherms were compared to the results obtained by a standard gravimetric technique. (The average relative deviation is less than 6%.) The main advantage of the technique is the significant time savings, e.g., one experimental run is required to generate an isotherm compared to multirun experiments using a standard breakthrough technique, in addition to using a simpler experimental setup and generally smaller amount of sample (agglomerated or in a powder form). Another important feature of this technique is the relatively simple extension that allows measurements of gas mixture equilibria. As such, the proposed technique has the potential to be used as a fast screening technique for adsorbent selection based on single-component or mixture analysis. To investigate the consistency of the proposed technique, the binary isotherms of competitive, CO 2 –C 2 H 4, and noncompetitive, CO 2 –CO, mixtures were investigated at different gas compositions. In addition, the effects of sorbate concentrations in the gas phase and interactions with the NaY zeolite active surface were investigated in relation to the adsorption selectivity and capacity, i.e., strong interaction of both CO 2 and ethylene with NaY site resulted in close adsorption selectivity 0.8 ≤ S CO 2 /C 2 H 4 ≤ 1.7, while CO 2 adsorbed more selectively compared to CO, 14 ≤ S CO 2 /CO ≤ 30, as a result of weak CO interaction with the adsorbent sites. Finally, the binary adsorption isotherms and selectivity were predicted by the multisite Langmuir model using the single component’s isotherm parameters. Modest agreements (error ≤ 28%) were obtained between the predicted and experimental results.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".