Z-Selective Hydrofunctionalization of Dienes
Bibliographic record
Abstract
Olefins play a fundamental role in synthetic organic chemistry because they are useful building blocks that create molecules. However, geometry control (E- vs Z-) in olefin synthesis is of utmost importance because the olefin geometry has a tremendous impact on its physical, chemical and biological properties. Additionally, Z-olefins are less stable compared to their E-olefin counterparts; due to this difference, general methods to make olefins results in more cases of E-olefins production with relatively fewer Z-olefins caused by its instability. It has been reported that Z-olefins can be synthesized from dienes through a rhodium-catalyzed formate mediated transformation, with tolerance to several reducible functional groups. With this successful method in hand, the focus is to make functionalized Z-alkenes while still maintaining tolerance to reducible functional groups under mild reaction conditions.
 Thus, this project presents the production of Z-olefins through rhodium-catalyzed hydrofunctionalization using the starting materials, dienes and aldehydes. This method requires an inert atmosphere and the reaction progress can be monitored by Nuclear Magnetic Resonance (NMR) using an internal standard to quantify the amount of product formed. In this process, it was observed that the starting material was consumed until more than 95% conversion. In addition, the possibility of using different dienes, such as diene esters and phenyl dienes, as well as different aldehydes could further broaden the scope of this method. The usefulness of this process can be applied to the production of complex molecules. For example, in the synthesis of a glucagon receptor antagonist, which is a drug that is used in the treatment of diabetes. Currently, there is a limited number of methods used to create Z-olefins; however, this proven procedure can be further applied in other hydrofunctionalization
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 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 teacher head, 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".