Pore Perfection vs Defect Design: Examining the Complex Relationship between Pore Structure and Carbon Dioxide Adsorption in Zr-Based MOFs
Bibliographic record
Abstract
This work examines the relationship between defects, pore size, and pore functionalization as it pertains to the enthalpy of adsorption between carbon dioxide and zirconium-based metal–organic frameworks (UiO-66 and UiO-67). When UiO-66 is synthesized without defects, carbon dioxide adsorption is more exothermic relative to when UiO-66 contains defects (−24.3 vs −20.9 kJ/mol). We repeated the experiments with pristine/defective UiO-67 and observed the opposite trend (−16.9 vs −21 kJ/mol), albeit less exothermic. With the exception of defective UiO-66, which had no change in the enthalpy of adsorption, dehydrating the cluster of pristine/defective UiO-66 (−21 kJ/mol) and UiO-67 (−14 kJ/mol) produced materials that were less exothermic upon carbon dioxide adsorption. This work indicates that there is a hierarchy of adsorption interactions that can work independently or in tandem to increase the enthalpy of adsorption. These include the small tetrahedral pore of UiO-66, hydrogen bonding, and dispersion interaction enhanced by the electron-withdrawing Zr(IV). Postsynthetic modification of the node with methanol/methoxy groups had a strong effect on the defect containing UiO-66. In this MOF, the pore sizes appeared nearly identical to the pristine UiO-66 and contained an enthalpy adsorption of −28 kJ/mol; this is the highest value obtained in this work.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".