Nitroxide‐Mediated Polymerization of Bio‐Based Farnesene with a Functionalized Methacrylate
Bibliographic record
Abstract
Abstract Farnesene (Far) is a bio‐based terpene monomer that is similar in structure to commercially used dienes like butadiene and isoprene. Nitroxide‐mediated polymerization (NMP) is adept for the polymerization of dienes, but not particularly effective at controlling the polymerization of methacrylates using commercial nitroxides. In this study, Far is statistically copolymerized with a functional methacrylate, glycidyl methacrylate (GMA), by NMP using N‐succinimidyl modified commercial BlocBuilder (NHS‐BB) initiator. Reactivity ratios are determined to be r Far = 0.54 ± 0.04 and r GMA = 0.24 ± 0.02. The ability of the poly(Far‐stat‐GMA) chains to reinitiate for chain extension with styrene showed a clear shift in molecular weight and monomodal distribution. Copolymerizations using a new alkoxyamine, Dispolreg 007 (D7), is explored as it is shown to homopolymerize methacrylates, but not yet reported for statistical copolymerizations. Bimodal molecular weight distributions are observed when an equimolar ratio of Far and GMA is copolymerized with D7 due to slow decomposition of the initiator, but chain ends are active as shown by successful chain extension with styrene. Both NHS‐BB and D7 initiators are used to synthesize poly[Far‐b‐(GMA‐stat‐Far)] and poly(Far‐b‐GMA) diblock copolymers. While the NHS‐BB initiated polymer chains have lower dispersity, D7 exhibits more linear polymerization kinetics and maintains more active chain ends.
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 | 0.000 |
| 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".