Improving CO<sub>2</sub> Fixation with Epoxides by Replacing Zirconium by Hafnium in UiO66 MOFs
Bibliographic record
Abstract
Abstract The impact of replacing Zr 4+ with Hf⁴⁺ as the metal site on CO₂ adsorption and catalytic activity in CO₂ fixation reaction with epoxide under mild conditions was investigated in UiO66‐NH₂ grafted with carbodiimides N,N ′‐dicyclohexylcarbodiimide (DCC) and N,N ′‐diisopropylcarbodiimide (DIC) ( UiO66(M)‐DCCBr and UiO66(M)‐DICBr ; M = Zr and Hf). Leveraging on Hf⁴⁺’s greater oxophilicity and stronger M—O bonds, Hf‐based UiO66‐NH 2 materials exhibited increased CO₂ adsorption capacity, influencing the catalytic performance in CO₂ fixation with epoxides. The materials were characterized by multiple techniques such as PXRD, FTIR, TGA, SEM, elemental analysis, as well as N₂ and CO₂ adsorption equilibrium measurements. UiO66(Hf)‐DCCBr and UiO66(Hf)‐DICBr demonstrated superior activity compared to their Zr‐based counterparts with high yield and TOF (14.6 and 11.9 h⁻¹, respectively) under milder conditions (0.1 MPa, 90 °C, 16 h, co‐catalyst‐free and solvent‐free). These findings underscore the pivotal role of unsaturated metal sites in enhancing the catalytic efficacy of guanidinium ionic UiO66‐NH₂ materials. Moreover, these catalysts exhibit excellent thermal stability and can be recycled and reused at least five times without a noticeable reduction in their catalytic efficiency.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".