Cyclic and Linear Polyferrocenes with Silicon and Tin as Alternating Bridges
Bibliographic record
Abstract
The synthesis and characterization of ferrocene-based oligomers that contained two different elements (Si and Sn) as alternating bridges is described for the first time. The salt-metathesis reaction of R(2) Si[(C(5) H(4) )Fe(C(5) H(4) Li)](2) (R=Me, Et) with R'(2) SnCl(2) (R'=Me, nBu, tBu) afforded a mixture of oligomers (6(Me) SnMe(2), 6(Et) SnMe(2), 6(Me) SnnBu(2), 6(Et) SnnBu(2), 6(Me) SntBu(2), and 6(Et) SntBu(2)). These oligomers were characterized by (1) H, (13) C, (29) Si, and (119) Sn NMR spectroscopy and by mass spectrometry. MS (MALDI-TOF) studies of 6(Et) SnMe(2) revealed the presence of linear (l) and cyclic (c) species that contained up to 20 ferrocene moieties. The molecular weights of the polymers were determined by gel-permeation chromatography (GPC) and by dynamic-light scattering (DLS). GPC analysis revealed average molecular weights of 2100-6300 Da with respect to polystyrene as a standard. DLS analysis yielded very similar results. Some compounds, c-(6(Me) SnMe(2) )(1), c-(6(Me) SntBu(2))(2), c-(6(Et) SnMe(2))(1), c-(6(Et) SntBu(2))(2), l-(6(Me) SnnBu(2) )(2), and l-(6(Me) SnnBu(2))(3), which contained up to six ferrocene moieties, were isolated in their pure form either by column chromatography or by crystallization. The Si- and Sn-bridged macrocycles that contained four ferrocene units (c-(6(Me) SntBu(2))(2) and c-(6(Et) SntBu(2))(2)) were structurally characterized by single-crystal X-ray analysis.
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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".