Cation−π and π–π Interactions in Aqueous Solution Studied Using Polarizable Potential Models
Bibliographic record
Abstract
Polarizable potential models for the interaction of Li(+), Na(+), K(+), and NH4(+) ions with benzene are parametrized based on ab initio quantum mechanical calculations. The models reproduce the ab initio complexation energies and potential energy surfaces of the cation-π dimers. They also reproduce the cooperative behavior of "stacked", cation-π-π trimers and the anticooperative behavior of "sandwiched", π-cation-π trimers. The NH4(+) model is calibrated to reproduce the energy of the NH4(+)-H2O dimer and yields correct free energy of hydration and hydration structure without further adjustments. The models are used to investigate cation-π interactions in aqueous solution by calculating the potential of mean force between each of the four cations and a benzene molecule and by analyzing the organization of the solvent as a function of the cation-benzene separation. The results show that Li(+) and Na(+) ions are preferentially solvated by water and do not associate with benzene, while K(+) and NH4(+) ions bind benzene with 1.2 and 1.4 kcal/mol affinities, respectively. Molecular dynamics simulations of NH4(+) and of K(+) in presence of two benzene molecules in water show that cation-π and π-π affinities are mutually enhanced compared to the pairwise affinities, confirming that the cooperativity of cation-π and π-π interactions persists in aqueous solution.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".