Rapid Ni, Zn, and Cu Ion-Promoted Alcoholysis of <i>N</i>,<i>N-</i>Bis(2-picolyl)- and <i>N</i>,<i>N-</i>Bis((1<i>H</i>-benzimidazol-2-yl)methyl)-<i>p</i>-nitrobenzamides in Methanol and Ethanol
Bibliographic record
Abstract
The methanolysis and ethanolysis of the Ni(II), Zn(II), and Cu(II) complexes of N,N-bis(2-picolyl)-p-nitrobenzamide (1) and N,N-bis((1H-benzimidazol-2-yl)methyl)-p-nitrobenzamide (2) were studied under pH-controlled conditions at 25 °C. Details of the mechanism were obtained from plots of the kobs values for the reaction under pseudo-first-order conditions as a function of [M2+]. Such plots give saturation kinetics for the Cu(II)-promoted reactions of 1 and 2 in both solvents, the Zn(II)-promoted reaction of 1 in methanol, and the Zn(II)- and Ni(II)-promoted reactions of 2 in methanol and ethanol. Logs of the maximal observed rate constants obtained from the latter plots, (kobs(max)), when plotted versus s(s)pH, are curved downward only for the Cu(II) complexes of 1 and 2 in both solvents and the Zn(II) complex of 1 in methanol. Despite differences in the metal-binding abilities and pKa values for formation of the active form, there is a common reaction mechanism, with the active form being 1:M(II):(–OR) and 2:M(II):(–OR), where M(II):(–OR) is the metal-bound alkoxide. The acceleration provided by the metal ion is substantial, being 10(14)–10(19) relative to the k2(¯OMe) value for the alkoxide-promoted alcoholysis of the uncomplexed amide.
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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) | 0.001 | 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".