Three different mechanisms for azo-ether hydrolyses in aqueous acid
Bibliographic record
Abstract
It has been shown recently that most ethers hydrolyze in aqueous acid media not by the traditional A1 or A2 process, but by a mechanism involving rate-determining proton transfer to the substrate, concerted with C–O bond cleavage. The reactions of azoethers are more complicated, because the azo group can be protonated in the acid reaction medium as well. This protonation has to be accounted for in the kinetic analysis. Often it simply ties up the substrate in an unreactive form; the hydrolysis reaction slows down as a result of the azo-protonated compound not being the reactant in the hydrolysis. However, there are other possibilities. If the ether group is suitably located in the substrate the azo-protonated compound can react with three water molecules (a “water wire”) in a fast reaction, and the alkoxy group is lost as a result. Depending on the acidity, in this mechanism either the initial three-water attack, or the breakup of the resulting intermediate, can be rate-determining, and both of these were observed. A third possibility is that ring protonation of suitable substrates can occur, giving delocalized carbocations that can form a hydrolysis product in subsequent fast reactions. Thus, three different hydrolysis mechanisms for azoethers in acidic media can be observed. Six azoethers were studied, one of which contained two methoxy groups. Both of these hydrolyzed, but by different mechanisms.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".