High Molar Mass Poly(alkylphosphinoboranes) <i>via</i> Iron-Catalyzed Dehydropolymerization
Bibliographic record
Abstract
High molar mass polyphosphinoboranes substituted with an alkyl group at phosphorus [RPH–BH 2 ] n (R = t Bu, 1-Ad, i Pr, Cy, n Hex, and Me) have been successfully prepared via dehydropolymerization of the phosphine–boranes RPH 2 ·BH 3 using an iron precatalyst, [CpFe(CO) 2 (OTf)] (100 °C, toluene, 2.0 M, 10–100 h). Substrate purity and the reaction conditions were found to be crucial in obtaining a high molar mass material as the major product ( M n = 18,200–57,200 g mol –1 and D̵ = 1.24–3.40). For example, the addition of small quantities of primary phosphines, a potential monomer contaminant, was found to lead to a lower molar mass oligomeric material [RPH–BH 2 ] x . Our experiments indicated that the added Lewis basic primary phosphine does not induce main-chain scission post-polymerization. In contrast, a phosphine-mediated termination process during the catalytic cycle appears to compete with the polymerization of the phosphine–borane monomer. The resulting poly(alkylphosphinoboranes) were characterized by multinuclear NMR spectroscopy, gel permeation chromatography, and electrospray ionization mass spectrometry. The thermal properties were also investigated by thermogravimetric analysis, which showed the materials to be stable to weight loss up to 100–120 °C, and differential scanning calorimetry, which revealed strongly side group-dependent T g values that ranged from −76 to 87 °C.
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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) | 0.001 | 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 | 0.001 |
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".