Kinetic behaviours of carbon dioxide and carbon monoxide on carbon molecular sieve
Bibliographic record
Abstract
Abstract A carbon molecular sieve (CMS) is a carbonaceous material with a narrow pore size distribution, which can separate molecules based on their size, shape, and adsorption kinetic rate. In this study, a commercial CMS was used to measure the adsorption kinetics of carbon dioxide and carbon monoxide. The rate of adsorption was investigated by considering two main resistances, surface barrier and diffusion. The results showed that molecular parameters, such as difference in shape, size, and interactions of molecules, lead to different adsorption kinetics mechanisms. In the system investigated in this study, the adsorption kinetics of both CO2 and CO sorbates were controlled by combined diffusion and surface barrier mechanisms, in which the surface barrier was found to be the main resistance to gas molecule uptake. Even though this study confirmed surface resistance as a dominant transfer mechanism, the systematic use of the combined model in the analysis provided further insights in the mass transfer due to adsorption of CO2 and CO molecules in the CMS adsorbent. The rate constants were found to follow the Darken equation for both sorbates. The kinetic selectivity of CO2 over CO was calculated from the combined surface barrier/diffusion model parametric analysis. The results generally showed a greater selectivity to carbon dioxide over carbon monoxide, i.e. higher mass transfer rates.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".