Polymeric N-heterocyclic carbenes in frustrated Lewis pair chemistry
Bibliographic record
Abstract
The cooperative action of sterically bulky Lewis acids and bases forms frustrated Lewis pairs (FLPs) that activate and catalytically transform reactive substrates. Loading of these functional groups onto a polymer backbone has led to an established new field of stimuli-responsive materials capable of serving as recyclable catalysts. Poly(FLPs) were originally built from phosphine and borane-functionalized polymers, with recent work demonstrating the use of amines in these systems. Polymeric N-heterocyclic carbenes (poly(NHCs)) have been seldom explored in FLP chemistry. Herein, we explore the application of poly(NHCs) in a similar manner to our previously reported work. A styrene-based benzimidazolium functionalised copolymer was synthesised via controlled radical polymerisation. Isolation of the active poly(NHC) proved challenging due to dimerization yielding a crosslinked network formed upon removal of solvent or precipitation. Application of the in situ generated poly(NHC) through incorporation of boranes in various solvents was precluded either by coordination of the Lewis acid or the lack of carbene formation in non-coordinating solvents. Despite these challenges, using the poly(NHC) in conjunction with a highly Lewis acidic copolymer enabled the fixation of CS 2 to form a covalently crosslinked poly(FLP) network.
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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".