Extension of the Application of Piers‐Rubinsztajn Conditions to Produce Triarylamine Pendant Dimethylsiloxane Copolymers
Bibliographic record
Abstract
Abstract Summary: In this paper we show how the strong organic Lewis acid catalyst, tris(pentafluorophenyl)borane (B(C6F5) or BCF), can be used to facilitate the functionalization of simple polymeric silicones with a triarylamine yielding a novel class of charge transporting materials. The reaction conditions we refer to as Piers‐Rubinsztajn Conditions and we have previously shown such conditions to be suitable when using phenylated silicones as precursors. In this work we found they also work successfully for a silicone oligomer as well as cocopolymers of polymethylhydridosiloxane (PMHS) and polydimethylsiloxane (PDMS) and a PMHS homopolymer, all of which are highly abundent and available in industrial quanitities. The resulting material was either a waxy solid, viscous oil or a glass. An additional “finishing” step with anisole using the same chemistry was found necessary to prevent gelation of the copolymer and homopolymer of PMHS. Even after finishing a small fraction (<5%) of Si‐H groups remained in the silicones. This nonetheless provides a rapid and mild method to synthesize functional silicones and tune their physical properties, using commonly available starting materials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".