Modelling adsorption based on an isoreticular <scp>MOF</scp> ‐series of <scp>IFPs</scp> –Part <scp>I</scp> : Collection of physical properties and single component equilibria
Bibliographic record
Abstract
Abstract An isoreticular series of metal organic frameworks (MOFs) of IFPs (IFP, imidazolate frameworks Potsdam) is investigated for their morphological properties and adsorption behaviour. The materials are characterized phenomenologically with respect to their particle size and tendency of agglomerate formation, and with respect to their internal structure. For this purpose, material densities, pore size distributions, specific inner surfaces, and porosities are determined. The main part of the investigation is based on the analysis of gravimetrically determined adsorption equilibria for carbon dioxide (CO 2 ) and methane (CH 4 ) and their modelling. In this context, two different approaches for the consideration of the buoyancy of the sample are compared. The adjusted measurement data are globally approximated as sets of isotherms at different temperatures with two different modifications of the Langmuir model. Results show that both models are well suited for the interpolation of the experimental data in the temperature range under consideration. Comparison of the heats of adsorption derived from the isosteric method with values extracted from the model equations confirms them as physically consistent. This provides the opportunity to numerically simulate the dynamic separation of CO 2 /CH 4 ‐mixtures under consideration of the heat tone based on the single component data. The IFPs can be divided phenomenologically into two categories. One is exclusively microporous (IFP‐4, ‐6, ‐7, and ‐8), while the other exhibits hierarchical structures of micropores coupled with mesopores (IFP‐1, ‐2, ‐3, and ‐5). Equilibrium data indicate that the latter are better suited for the separation of CO 2 /CH 4 ‐mixtures due to their higher selectivities and capacities.
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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 | 0.001 |
| 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 | 0.000 |
| 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".