Influence of N-Substituents on the Adsorption Geometry of OH-Functionalized Chiral N-Heterocyclic Carbenes
Bibliographic record
Abstract
Adsorption of chiral molecules on heterogeneous catalysts is a simple approach for inducing an asymmetric environment to enable enantioselective reactivity. Although the concept of chiral induction is straightforward, its practical utilization is far from simple, and only a few examples toward the successful chiral induction by surface anchoring of asymmetric modifiers have been demonstrated so far. Elucidating the factors that lead to successful chiral induction is therefore a crucial step for understanding the mechanism by which chirality is transferred. Herein, we identify the adsorption geometry of OH-functionalized N-heterocyclic carbenes (NHCs), which are chemical analogues to chiral modifiers that successfully promoted α-arylation reactions once anchored on Pd nanoparticles. Polarized near-edge X-ray absorption fine structure (NEXAFS) measurements on Pd(111) revealed that NHCs that were associated with low enantioselectivity were characterized with a well-ordered structure, in which the imidazole ring was vertically positioned and the OH-functionalized side arms were flat-lying. OH-functionalized NHCs that were associated with high enantioselectivity revealed a disordered/flexible adsorption geometry, which potentially enabled better interaction between the OH group and the prochiral reactant.
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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) | 0.001 | 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".