A bulky polymer-supported frustrated Lewis pair: Dihydrogen cleavage with poly(methylenephosphine)-tris(pentafluorophenyl)borane
Bibliographic record
Abstract
• Demonstrated H 2 activation by poly(methylenephosphine)s and B(C 6 F 5 ) 3 . • Characterization aided by molecular model compounds. • Hydrogen-loaded polymer can be utilized to reduce imines. Polymeric frustrated Lewis pairs (FLPs) have recently attracted attention due to their potential to enhance recyclability or catalytic activity when compared to their monomeric counterparts. In this work, we explore Lewis basic inorganic polymers, namely poly(methylenephosphine)s ( PMP s), as novel platforms for polymeric FLPs. To aid in characterization of the polymers, molecular model FLPs, MesP(R)–CHPh 2 [R = Me ( 1a ), n-Bu ( 1b ), CHPh 2 ( 1c )] and B(C 6 F 5 ) 3 , were employed. Each successfully activated H 2 to afford [MesPH(R)–CHPh 2 ][HB(C 6 F 5 ) 3 ] ( 2a-c ). Following a similar strategy, PMP ( M n = 12,850 Da, Đ = 1.08), produced by the living anionic polymerization of MesP=CPh 2 , was shown to effectively cleave H 2 . Specifically, approximately 50 % of the phosphine moieties in PMP were converted to the corresponding phosphonium borate ionomer. This hydrogen-loaded polymer successfully reduced imine, PhC(H)=N( t- Bu), thereby reforming PMP and demonstrating proof-of-concept for PMP s as polymeric Lewis bases in FLP chemistry.
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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".