Pure and Binary Adsorption Equilibria of Carbon Dioxide and Nitrogen on Silicalite
Bibliographic record
Abstract
For different separation applications of CO 2 and N 2, pure and mixture adsorption isotherms of these gases on silicalite adsorbent were determined experimentally. Constant volume and concentration pulse chromatographic techniques were used for the determination of pure and binary adsorption behavior, respectively. Pure component isotherms were determined up to 5 bar pressure for the temperature range (40 to 100) °C. Binary adsorption behavior for CO 2 and N 2 mixtures, covering the whole concentration range, were determined experimentally for a total pressure of 1 bar for the same temperature range. The applicability of different pure adsorption isotherm models was discussed for the pure isotherms, and ideal separation factors were determined. For the mixture adsorption isotherms, three binary concentration pulse methods, HT−CPM (Harlick and Tezel−Concentration Pulse Method), MTT−CPM (Modified Triebe and Tezel−Concentration Pulse Method), and MVV−CPM (Modified Van der Vlist and Van der Meijden−Concentration Pulse Method) were considered, and HT−CPM was found to be the most applicable one for this particular system. The experimental binary isotherms were compared to the predicted ones by using different binary adsorption models for this system. The results obtained showed that silicalite is a promising adsorbent for the separation of CO 2 and N 2 .
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".