Electrostatic Control of Regioselectivity in Au(I)-Catalyzed Hydroarylation
Bibliographic record
Abstract
Competing pathways in catalytic reactions often involve transition states with very different charge distributions, but this difference is rarely exploited to control selectivity. The proximity of a counterion to a charged catalyst in an ion paired complex gives rise to strong electrostatic interactions that could be used to energetically differentiate transition states. Here we investigate the effects of ion pairing on the regioselectivity of the hydroarylation of 3-substituted phenyl propargyl ethers catalyzed by cationic Au(I) complexes, which forms a mixture of 5- and 7-substituted 2 H -chromenes. We show that changing the solvent dielectric to enforce ion pairing to a SbF 6 – counterion changes the regioselectivity by up to a factor of 12 depending on the substrate structure. Density functional theory (DFT) is used to calculate the energy difference between the putative product-determining isomeric transition states (ΔΔ E ‡ ) in both the presence and absence of the counterion. The change in ΔΔ E ‡ upon switching from the unpaired transition states in high solvent dielectric to ion paired transition states in low solvent dielectric (Δ(ΔΔ E ‡ )) was found to be in good agreement with the experimentally observed selectivity changes across several substrates. Our calculations indicate that the origin of Δ(ΔΔ E ‡ ) lies in the preferential electrostatic stabilization of the transition state with greater charge separation by the counterion in the ion paired case. By performing calculations at multiple different values of the solvent dielectric, we show that the role of the solvent in changing selectivity is not solely to enforce ion pairing, but rather that interactions between the ion paired complex and the solvent also contribute to Δ(ΔΔ E ‡ ). Our results provide a foundation for exploiting electrostatic control of selectivity in other ion paired systems.
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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 | 0.001 |
| 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".