Binding Energies of the Silver Ion to Alcohols and Amides: A Theoretical and Experimental Study
Bibliographic record
Abstract
The silver ion binding energies to alcohols (methanol, ethanol, n-propanol, i-propanol, and n-butanol) and to amides (acetamide, N-methylacetamide, N, N-dimethylacetamide, formamide, N-methylformamide, and N, N-dimethylformamide) have been calculated using density functional theory (DFT) and measured using the threshold collision-induced dissociation (TCID) method. For DFT, the combined basis sets of ECP28MWB for silver and 6-311++G(2df,2pd) for the other atoms were found to be optimal using a series of test calculations on Ag (+) binding to methanol and to formamide. In addition, the Ag (+) binding energies of all ligands were evaluated with nine functionals after full geometric optimizations. TCID binding energies were measured using a triple quadrupole mass spectrometer. Reasonable to good agreements were obtained between the calculated and experimental silver(I) binding energies. Ligation of Ag (+) to the alcohols was primarily via the oxygen, although n-propanol and n-butanol exhibited additional, bidentate coordination via the CH hydrogens. By contrast, silver(I) binding to the amides was all monodentate via the carbonyl oxygen. There appears to be strong correlations between the binding energies and the polarizabilities of the ligands.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".