Hydrogenation of natural rubber latex in the presence of OsHCl(CO)(O<sub>2</sub>)(PCy<sub>3</sub>)<sub>2</sub>
Bibliographic record
Abstract
Abstract Hydrogenation is an important method of chemical modification, which improves the physical, chemical, and thermal properties of diene elastomers. Natural rubber latex (NRL) can be quantitatively hydrogenated to provide a strictly alternating ethylene–propylene copolymer using a homogeneous osmium catalyst OsHCl(CO)(O2)(PCy3)2. A detailed kinetic investigation was carried out by monitoring the amount of hydrogen consumption during the reaction using a gas‐uptake apparatus. The kinetic results of NRL hydrogenation indicated that this system had a second‐order dependence of the hydrogenation rate on hydrogen pressure and then decreased toward a zero‐order dependence for hydrogen pressures above 13.8 bar. The hydrogenation was also observed to be first‐order with respect to catalyst concentration and inverse first‐order on rubber concentration due to impurities present in the rubber latex. Additions of a controlled amount of acid demonstrated a beneficial effect on the hydrogenation rate of NRL. The temperature dependence of the hydrogenation rate was investigated and an apparent activation energy (over the range of 120–160°C) was calculated as 57.6 kJ/mol. Mechanistic aspects of this catalytic process are discussed on the basis of kinetic results. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 100: 640–655, 2006
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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".