Aminolactone Chiral Modifiers for Heterogeneous Asymmetric Hydrogenation: Corrected Structure of Pantoyl-Naphthylethylamine, In-Situ Hydrogenolysis, and Scanning Tunneling Microscopy Observation of Supramolecular Aminolactone/Substrate Assemblies on Pt(111)
Bibliographic record
Abstract
As established by Baiker and co-workers, pantoyl-naphthylethylamine (PNEA) is an efficient synthetic chiral modifier for the asymmetric hydrogenation of ketopantolactone (KPL) to pantolactone on supported Pt catalysts. We report a scanning tunneling microscopy (STM) study of PNEA and PNEA-derived aminolactone species on Pt(111) and a reassignment of the relative stereochemistry of the modifier. Robust organic chemistry methods were used to establish that the structure of PNEA is R, S rather than R, R . The dissociative chemisorption of a fraction of PNEA adsorbed on Pt(111) yields two fragments that we attribute to a process involving C–N bond scission. We show that C–N bond scission occurs under hydrogenation conditions on PNEA-modified Pt/Al 2 O 3 catalysts, forming the aminolactone amino-4,4-dimethyldihydrofuran-2-one (AF). STM measurements on ( S )-AF and 2,2,2-trifluoroacetophenone coadsorbed on Pt(111) show the formation of isolated 1:1 complexes. In contrast, measurements on coadsorbed ( S )-AF and KPL show fluxional supramolecular AF/KPL assemblies. The possibility that such assemblies contribute to the overall enantioselectivity observed for PNEA-modified Pt catalysts is discussed.
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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".