Computational and Experimental Assessment of CO<sub>2</sub> Uptake in Phosphonate Monoester Metal–Organic Frameworks
Bibliographic record
Abstract
Phosphonate monoesters (PMEs) as ligands for metal–organic frameworks can potentially direct topology, enhance water stability, and modify pore chemistry. Here, we show, experimentally and computationally, not only that is the ratio of phosphonate to phosphonate monoester significant, but also that gas sorption depends on the distribution of the monoesters in the structure. A phosphonate monoester ligand, 1,3,5-tri(4-phosphonato)benzene-tris(monoethylester), was coordinated to copper(II) to form two different frameworks based on the same copper–phosphonate chain building units, one dense ( 1 ) and the other with an experimental surface area over 1000 m 2 g –1 ( CALF-33-Et 3 ). One of the three phosphonate monoesters in CALF-33-Et 3 can be hydrolyzed to make an isostructural material, CALF-33-Et 2 H, with approximately the same surface areas but vastly superior CO 2 sorption. Controlling the hydrolysis at this site allowed the partially hydrolyzed variants, CALF-33-Et 3– x H x (where 0 < x < 1), to be prepared and their gas sorption studied by experiment and simulation to determine CO 2 binding sites and binding energies. These results show that each PME group can impact multiple gas sorption sites meaning that clustering versus random distributions of ester groups gives very different gas uptake. Finally, an algorithm is put forward that allows the CO 2 uptake of the hydrolyzed MOF to be simulated by algebraically combining the isotherms of the nonhydrolyzed and fully hydrolyzed forms. This method can be used to assess both the degrees of ester hydrolysis and the distribution of ester groups in the solid.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".