Synthesis of iron‐containing polymers with azo dyes in the backbones or side chains
Bibliographic record
Abstract
Abstract The synthesis of cationic cyclopentadienyliron‐containing polymers with pendent azobenzene chromophores was accomplished via metal‐mediated nucleophilic aromatic substitution reactions. All of the desired polymers were isolated as vibrantly coloured materials and displayed excellent solubility in polar aprotic solvents. Cationic and neutral cyclopentadienyliron polymers incorporating azo dyes in the backbone were also prepared. Reactions of azo dyes with dichlorobenzene complexes allowed for the isolation of cationic cyclopentdienyliron (CpFe+) complexes with azo dye chromophores. These complexes were then reacted with 1,1′‐ferrocenedicarbonyl chloride to produce the trimetallic monomers with terminal chloro groups. These monomers contained two pendent CpFe+ cations and a neutral iron moiety in the backbone. Nucleophilic substitution reactions of these monomers with oxygen and sulfur containing dinucleophiles gave rise to a new class of polymeric materials. The pendent CpFe+ moieties could also be cleaved from the polymer backbones using photolysis to afford novel ferrocene based polymers. The UV‐vis spectra of the organoiron monomers and polymers display similar wavelength maxima however incorporating azobenzene chromophores with electron‐withdrawing substituent into the polymer chains resulted in bathochromic shifts of the λmax values.
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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 | 0.001 |
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".