Hydrogenation of poly(myrcene) and poly(farnesene) using diimide reduction at ambient pressure
Bibliographic record
Abstract
Abstract Ambient pressure chemical hydrogenation using p‐toluene sulfonyl hydrazide (TSH) via thermal diimide formation (N2H2) permitted reduction of double bonds of poly(myrcene) (poly[Myr]) and poly(farnesene) (poly[Far]). Both pendent and backbone double bonds in poly(Myr) (Mn = 56 kg/mol) and poly(Far) (Mn = 62 kg/mol) synthesized by conventional free radical polymerization were hydrogenated to almost completion. Furthermore, TSH semi‐batch addition efficiently hydrogenated double bonds, while avoiding undesired autohydrogenation of diimides that occurred in batch mode. Thermal stability improved for hydrogenated poly(Myr) and poly(Far), where temperature at 10% weight loss (T10%) increased from 188 to 404°C for poly(Myr) and from 310 to 379°C for poly(Far). Tgs of poly(Myr) and poly(Far) also increased by about 10–25°C, indicating increased stiffness after hydrogenation. Finally, viscosities of poly(Myr) and poly(Far) were also increased after hydrogenation, and a greater increase was observed for poly(Myr) (by two orders of magnitude from 102 to 104 Pa s) due to its Mn being much higher than its entanglement molecular weight. Poly(Far) viscosity only increased by 1.5 times after hydrogenation (~104 Pa s), comparable to the poly(Myr) after hydrogenation, suggesting unsaturated poly(Far) was more entangled than unsaturated poly(Myr) because of its longer side chains.
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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".