Anion Receptors Composed of Hydrogen- and Halogen-Bond Donor Groups: Modulating Selectivity With Combinations of Distinct Noncovalent Interactions
Bibliographic record
Abstract
Studies of a series of urea-based anion receptors designed to probe the potential for anion recognition through combinations of hydrogen and halogen bonding are presented. Proton- and fluorine-NMR spectroscopy indicates that the two interactions act in concert to achieve binding of certain anions, a conclusion supported by computational studies. Replacement of the halogen-bond donating iodine substituent by fluorine (which does not participate in halogen bonding) enables estimation of the contribution of this interaction to the free energy of anion binding. Evidence for attractive contacts between anions and electron-deficient arenes arising from the use of perfluoroarene-functionalized ureas as control receptors is also discussed. The magnitude of the free energy contribution of halogen bonding depends both on the geometric features of the group linking the hydrogen- and halogen-bond donor groups and on the identity of the bound anion. The results are interpreted in relation to fundamental features of the halogen-bonding interaction, including its directionality and unusual preference for halides over oxoanions. Cooperation between two distinct noncovalent interactions leads to unusual effects on receptor selectivity, a result of fundamental differences in the interactions of halogen- and hydrogen-bond donor groups with anions.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".