Catalytic Synthesis, Characterization, and Properties of Polyaminoborane Homopolymers and Random Copolymers
Bibliographic record
Abstract
Polyaminoboranes, [RNH–BH 2 ] n, are boron–nitrogen analogues of polyolefins: however, to date, few soluble, well-characterized examples have been described. Herein, we show that metal-catalyzed dehydrocoupling of N -alkylamine–boranes Ph(CH 2 ) x NH 2 ·BH 3 ( x = 2–4) with pendant aryl groups yields soluble polyaminoboranes [Ph(CH 2 ) x NH–BH 2 ] n, using skeletal nickel, [Rh(μ-Cl)(1,5-COD)] 2 (COD = cyclooctadiene), and [IrH 2 (POCOP)] (POCOP = κ 3 -1,3-(OP t Bu 2 ) 2 C 6 H 3 ) as precatalysts in THF. Application of the most efficient catalytic system (1 mol %, [IrH 2 (POCOP)], THF, −40 °C) enabled the isolation of high molar mass, soluble polyaminoborane [Ph(CH 2 ) 4 NH–BH 2 ] n in moderate (ca. 40%) yield. Structural characterization was achieved by multinuclear nuclear magnetic resonance, infrared, and elemental analysis; and the molar mass was determined to be high ( M n > 10 000 g mol –1 ) by gel permeation chromatography, dynamic light scattering, and, for comparison, also 1 H diffusion-ordered spectroscopy methods. The optimized dehydropolymerization conditions for the Ir catalyst were also used to prepare copolymers from mixtures of Ph(CH 2 ) 4 NH 2 ·BH 3 with either MeNH 2 ·BH 3, Ph(CH 2 ) 2 NH 2 ·BH 3, or NH 3 ·BH 3 . Significantly, soluble copolymers containing ca. 67% of [NH 2 –BH 2 ] repeat units were prepared. The thermal stability of the polyaminoborane homopolymers and copolymers was studied by thermogravimetric analysis. The use of a cross-linker, H 3 B·NH 2 (CH 2 ) 8 NH 2 ·BH 3, in the dehydropolymerization reactions led to improved ceramic yields after pyrolysis indicating that, with appropriate structural design, polyaminoboranes may be of potential future interest as precursors of boron-based ceramics.
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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".