High‐Entropy Lanthanide–Organic Framework as an Efficient Heterogeneous Catalyst for Cycloaddition of CO<sub>2</sub> with Epoxides and Knoevenagel Condensation
Bibliographic record
Abstract
Abstract Multimetallic synergistic effects have the potential to improve CO 2 cycloesterification and Knoevenagel reaction processes, outperforming monometallic MOFs. The results demonstrate superior performance in these processes. To investigate this, we created and characterized a selection of single‐component Ln(III)‐MOFs (Ln=Eu, Tb, Gd, Dy, Ho) and high‐entropy lanthanide‐organic framework (HE‐LnMOF) using solvent‐thermal conditions. The experiments revealed that HE‐LnMOF exhibited heightened catalytic efficiency in CO 2 cycloesterification and Knoevenagel reactions compared to single‐component Ln(III) MOFs. Moreover, the HE‐LnMOF displayed significant stability, maintaining their structural integrity after five cycles while sustaining elevated conversion and selectivity rates. The feasible mechanisms of catalytic reactions were also discussed. HE‐LnMOF possess multiple unsaturated metal centers, acting as Lewis acid sites, with oxygen atoms connecting the metal, and hydroxyl groups on the ligand serving as base sites. This study introduces a novel method for synthesizing HE‐LnMOF and presents a fresh application of HE‐LnMOF for converting CO 2 .
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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".