The Simple Synthesis of Ferrocene Ligands from a Practitioner's Perspective
Bibliographic record
Abstract
Abstract This review is a personal account of the research carried out in our laboratories on the design and synthesis of ferrocene compounds used in catalysis, primarily as ligands but also in material science applications. The emphasis of the research has always been to find the simplest possible synthetic routes towards these ferrocene compounds, which may be used as convenient starting materials. This is why lithiation and quench methodology has been used extensively and successfully in most applications. It has taken many years to achieve the convenient synthesis of some very simple compounds such as 1,2‐dibromoferrocene and 1,2,3,4,5‐pentabromoferrocene, and the reasons for this are highlighted. The review concentrates on the synthesis of ferrocenophanes, ferrocenylphosphanes, ferrocenylalkylamines and their precursor compounds, describing some of their early coordination chemistry. Subsequently, the successful use of ferrocene‐based ligands in the Lucite Alpha process is discussed. The coordination chemistry of a selection of the ligands prepared is discussed, and the rationale behind the specific ligand design strategies is given. Included is some research material, previously unpublished, which serves to fill in gaps in the overview. Finally, some derivatives prepared for applications in material science are described from a synthetic standpoint.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".