Trace water affects tris(pentafluorophenyl)borane catalytic activity in the Piers–Rubinsztajn reaction
Bibliographic record
Abstract
Improved methods to control silicone synthesis are required due to the sensitivity of siloxane bonds to acid/base-mediated chain redistribution/depolymerization. The Piers-Rubinsztajn reaction employs tris(pentafluorophenyl)borane as an efficient catalyst (<0.1 mol%) for siloxane bond formation from hydro- and alkoxysilanes - typical reactions proceed in open flasks at room temperature within minutes. While advantageous under ideal conditions, the boron catalyst activity may be affected by age, storage conditions and various environmental factors, particularly humidity. Under conditions of high humidity it may be necessary to apply heat and/or use increased catalyst loading in the reactions; there is often an induction time. We examine induction times in the Piers-Rubinsztajn reaction as a function of water concentrations in the reagent or catalyst solution and show that water in the reagent solution or atmosphere is less problematic than water found in the catalyst stock solution. A relatively linear increase in induction time accompanied higher water concentrations in the catalyst solution - no such effect was observed when the water was in the reagent solution. Reaction rates in both scenarios were similar, i.e., not affected by the induction time. Improvements in the stability of catalyst solutions were observed when B(C6F5)3 was stored in low molecular weight silicone oils, and pre-complexed with HSi(OSiMe3)3. These outcomes are ascribed to the ability of HSi groups to outcompete water in binding with B(C6F5)3 to initiate reaction, unless the boron is pre-complexed with water.
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".