Aluminium coordination complexes in copolymerization reactions of carbon dioxide and epoxides
Bibliographic record
Abstract
Al complexes are widely used in a range of polymerization reactions (ROP of cyclic esters and cationic polymerization of alkenes). Since the discovery in 1978 that an Al porphyrin complex could copolymerize propylene oxide with carbon dioxide, Al coordination compounds have been studied extensively as catalysts for epoxide-carbon dioxide copolymerizations. The most widely studied catalysts are Al porphyrin and Al salen derivatives. This is partially due to their ability to act as mechanistic models for more reactive, paramagnetic Cr catalysts. However, this in depth mechanistic understanding could be employed to design more active Al catalysts themselves, which would be beneficial given the wide availability of this metal. Polymerization data (% CO3 linkages, M(n), M(w)/M(n) and TON) for these complexes are presented and mechanisms discussed. In most cases, especially those employing square-based pyramidal Al complexes, co-catalysts are required to obtain high levels of carbon dioxide incorporation. However, in some cases, the use of co-catalysts inhibits the copolymerization reaction. Lewis acidic Al phenolate complexes have been used as activators in CHO-carbon dioxide copolymerizations to increase TOF and this has recently led to the development of asymmetric copolymerization reactions. Given the ready availability of Al, the robustness of many complexes (e.g. use in immortal polymerizations) and opportunity to prepare block copolymers and other designer materials, Al complexes for copolymerization of carbon dioxide are surely worth a second look.
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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".