Modelling adsorption based on an isoreticular <scp>MOF</scp> ‐series of <scp>IFPs</scp> —Part <scp>II</scp> : Dynamic adsorption in fixed beds
Bibliographic record
Abstract
Abstract Based on experimental pure component data for the characterization of the isostructural imidazolate framework Potsdam (IFP) series reported in Part I, a model for the simulation of non‐isothermal dynamic adsorption of CO 2 /CH 4 ‐mixtures in fixed‐bed columns is presented in this Part II. The robustness of the model is examined and validated, by comparison to experimental breakthrough data at different process conditions, such as varying concentration, temperature, and pressure. Thereby, different predictive methods for the estimation of adsorption equilibria of mixtures are compared (RAST, IAST, ML). The results show that ideal behaviour can be assumed with good accuracy for the system under consideration, except for IFP‐2, which shows significant deviations at increased pressures and temperatures. A detailed kinetic analysis reveals that mass transfer is significantly influenced by micropore diffusion. Thus, only for IFP‐1 the dynamic separation of CO 2 and CH 4 is equilibrium‐driven, while for the remaining IFPs the kinetic regime dominates the process, which in some cases increases the separation efficiency (IFP‐2 to ‐7) but can also inhibit it (IFP‐8). The determined intracrystalline diffusion coefficients show very good agreement with values for metal organic framework (MOF) compounds of similar structure reported in the literature.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".