Triarylborane-Catalyzed Formation of Cyclic Organic Carbonates and Polycarbonates
Bibliographic record
Abstract
Effective utilization of carbon dioxide as a C1 feedstock is an ongoing challenge for chemists. The catalytic reaction of epoxides and carbon dioxide to produce cyclic or polycarbonates has become an important reaction that continues to be dominated by metal-based catalysts. Metal-free catalysts have shown promise as an alternative for these transformations, but this area remains quite underdeveloped. In this work, we show that arylboranes, BPh 3 and B(C 6 F 5 ) 3, can be used as catalysts, in the presence of a suitable cocatalyst or as a preformed Lewis acid/base adduct, to prepare either the cyclic organic carbonate [e.g., a turnover number (TON) of 2960 was obtained for propylene oxide to propylene carbonate] or polycarbonate product (e.g., copolymerization of vinylcyclohexene oxide gave a polycarbonate with 99+% carbonate linkages, M n 6270 g mol –1, Đ 1.03). Selectivity toward cyclic or polymer product is dependent on the substrate used. Lower activity was observed using B(C 6 F 5 ) 3 due to its increased Lewis acidity. Kinetic studies of this “metal-free” reaction reveal a process that is first-order in all reagents with the surprising exception of carbon dioxide, for which an inverse dependence was discovered. This means reactions can be performed at atmospheric pressure (TON 3960 for glycidyl chloride to cyclic carbonate at P CO 2 1 atm). In terms of polycarbonate formation, when a bicyclic epoxide containing a vinyl functional group was employed as a substrate, the vinyl functionality could be cross-linked (both intra- and intermolecularly) or part of a functional monomer, leading to polycarbonates with T g values of 184 and 122 °C, respectively. These data highlight that a wide range of sustainable, organic carbonate materials can be produced at modest pressures using arylborane catalysts, the reactivity of which can be modified by adjustment of electronics and potentially sterics.
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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 | 0.000 |
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".