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 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) | 0.001 | 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".