Photoreactivity and Photopolymerization of Silicon‐Bridged [1]Ferrocenophanes in the Presence of Terpyridine Initiators: Unprecedented Cleavage of Both Iron–Cyclopentadienyl Bonds in the Presence of Chlorosilanes
Bibliographic record
Abstract
The photopolymerisation of sila[1]ferrocenophane [Fe(eta-C5H4)2SiMe2] (3) with 4,4',4''-tri-tert-butyl-2,2':6',2''-terpyridine (tBu3terpy) as initiator has been explored. High-molecular-weight polyferrocenylsilane (PFS) [{Fe(eta-C5H4)2SiMe2}n] (5) was formed in high yield when a stoichiometric amount of tBu3terpy was used at 5 degrees C. Photopolymerisation of ferrocenophane 3 at higher temperatures gave PFS 5 in lower yield and with a reduced molecular weight as a result of a slower propagation rate. Remarkably, when Me3SiCl was added as a capping agent before photopolymerisation, subsequent photolysis of the reaction mixture resulted in the unprecedented cleavage of both iron-Cp bonds in ferrocenophane 3: iron(II) complex [Fe(tBu3terpy)2Cl2] (7Cl) was formed and the silane fragment (C5H4SiMe3)2SiMe2 (8) was released. The iron-Cp bond cleavage reaction also proceeded in ambient light, although longer reaction times were required. In addition, the unexpected cleavage chemistry in the presence of Me3SiCl was found to be applicable to other photoactive ferrocenes such as benzoylferrocene. For benzoylferrocene and ferrocenophane 3, the presence of metal-to-ligand charge transfer (MLCT) character in their low-energy transitions in the visible region probably facilitates photolytic iron-Cp bond cleavage, but this reactivity is suppressed when the strength of the iron-Cp bond is increased by the presence of electron-donating substituents on the cyclopentadienyl rings.
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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".