Metal Ion Hydrocarbon Bidentate Bonding in Alkyl Acetates, Methyl Alkanoates, Alcohols and 1-Alkenes: A Comparative Study
Bibliographic record
Abstract
The relative affinity of the monovalent metal ions Li + , Na + , Cu + and Ag + towards a series of aliphatic alkyl acetates and some selected 1-alkenes (P) was examined using the kinetic method. A detailed analysis of the dissociation characteristics of a series of mixed metal-bound dimer ions of the type P 1 -M+-P 2 and the evaluated proton affinities (PAs) of the monomers shows that the affinity of the cation towards long-chain alkyl acetates and alkenes (having a chain length ? C 4 ) is markedly enhanced. In line with recent studies of nitriles, alcohols and methyl alkanoates, this is attributed to a bidentate interaction of the metal ion with the functional group or double bond and the aliphatic chain. In particular, the longer chain alkyl acetates, methyl alkanoates and alcohols show a remarkably similar behaviour with respect to silver ion hydrocarbon bonding. The Ag + adducts of the alkyl acetates dissociate by loss of CH 3 COOH. This reaction becomes more pronounced at longer chain lengths, which points to metal ion bidentate formation in [Ag + ···1-alkene] product ions having a long hydrocarbon chain. In the same vein, the heterodimers [1-hexene···Ag + ···1-heptene] and [1-heptene·Ag + ···1-octene] dissociate primarily into [Ag + ···1-heptene] and [Ag + ···1-octene] ions, respectively. Hydrocarbon bidentate formation in [Ag + ···1-octene] also reveals itself by the reluctance of this ion to react with water in an ion trap, as opposed to [Ag + ···1-hexene] which readily undergoes hydration.
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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".