Azobenzene as an Effective Ligand in Europium Chemistry—A Synthetic and Theoretical Study
Bibliographic record
Abstract
The preparation and characterization of two novel europium-azobenzene complexes that demonstrate the effectiveness of this ligand for stabilizing reactive, redox-active metals are reported. With the family of rare earth metals receiving attention due to their potential as catalysts, critical components in electronic devices, and, more recently, in biomedical applications, a detailed understanding of factors contributing to their coordination chemistry is of great importance for customizing their stability and reactivity. This study introduces azobenzene as an effective nonprotic ligand system that provides novel insights into rare earth metal coordination preferences, including factors contributing to the coordinative saturation of the large, divalent europium centers. The two compounds demonstrate the impact of the solvent donors (tetrahydrofuran (THF) and dimethoxyethane (DME)) on the overall coordination chemistry of the target compounds. Apart from the side-on coordination of the doubly-reduced azobenzene and the anticipated N-N bond elongation due to decreased bond order, the two compounds demonstrate the propensity of the europium centers towards limited metal-π interactions. The target compounds are available by direct metallation in a straightforward manner with good yields and purity. The compounds demonstrate the utility of the azobenzene ligands, which may function as singly- or doubly-reduced entities in conjunction with redox-active metals. An initial exploration into the computational modeling of these and similar complexes for subsequent property prediction and optimization is performed through a methodological survey of structure reproduction using density functional theory.
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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) | 0.002 | 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".