Polymerization of Racemic 2,2′-Dialkyl-Sila[1]ferrocenophanes: DFT-Assisted Polymer Analysis by <sup>29</sup>Si NMR Spectroscopy Using Model Compounds
Bibliographic record
Abstract
The described research aimed at the preparation of racemic, C 2 symmetric sila[1]ferrocenophanes with alkyl groups in 2,2′-positions to complement their known enantiopure counterparts. As the synthetic approach, the well-known Ugi amine chemistry was chosen to introduce planar chirality into the ferrocene framework. The challenge in this multistep process is the separation of a rac and meso mixture of diols obtained through the reduction of 1,1′-dialkanoylferrocenes by LiAlH 4 or NaBH 4 . From the three tested alkanoyl groups, only one led to a significant excess of the rac diol, which could be separated by crystallizations and converted to rac -2,2′-diisobutyl-dimethylsila[1]ferrocene. Thermal ring-opening polymerization of this new monomer gave a poly(ferrocenylsilane) that consists of two types of diads, as revealed by two sets of peaks in its 29 Si NMR spectrum centered at −5.66 and −7.74 ppm. 29 Si NMR chemical shifts could be predicted with density functional theory (DFT) methods for planar-chiral bis(ferrocenyl)dimethylsilanes that were used to model these meso and racemo diads of the polymer. These calculations realistically predict that silicon atoms of racemo diads are higher shielded than those of meso diads. Surprisingly, the 29 Si NMR peaks of both diads are split into a set of peaks, revealing a sensitivity of δ values beyond diads.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".