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Abstract A fundamental understanding of the enantiospecific interactions between chiral adsorbates and understanding of their interactions with chiral surfaces is key to unlocking the origins of enantiospecific surface chemistry. Herein, the adsorption and decomposition of the amino acid proline (Pro) have been studied on the achiral Cu(110) and Cu(111) surfaces and on the chiral Cu(643)R&Ssurfaces. Isotopically labelled 1‐13C‐l‐Pro has been used to probe the Pro decomposition mechanism and to allow mass spectrometric discrimination ofd‐Pro and 1‐13C‐l‐Pro when adsorbed as mixtures. On the Cu(111) surface, X‐ray photoelectron spectroscopy reveals that Pro adsorbs as an anionic species in the monolayer. On the chiral Cu(643)R&Ssurface, adsorbed Pro enantiomers decompose with non‐enantiospecific kinetics. However, the decomposition kinetics were found to be different on the terraces versus the kinked steps. Exposure of the chiral Cu(643)R&Ssurfaces to a racemic gas phase mixture ofd‐Pro and 1‐13C‐l‐Pro resulted in the adsorption of a racemic mixture; i.e., adsorption is not enantiospecific. However, exposure to non‐racemic mixtures ofd‐Pro and 1‐13C‐l‐Pro resulted in amplification of enantiomeric excess on the surface, indicative of homochiral aggregation of adsorbed Pro. During co‐adsorption, this amplification is observed even at very low coverages, quite distinct from the behavior of other amino acids, which begin to exhibit homochiral aggregation only after reaching monolayer coverages. The equilibrium adsorption ofd‐Pro and 1‐13C‐l‐Pro mixtures on achiral Cu(110) did not display any aggregation, consistent with prior scanning tunneling microscopy (STM) observations ofdl‐Pro/Cu(110). This demonstrates convergence between findings from equilibrium adsorption methods and STM experiments and corroborates formation of a 2D random solid solution.
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Bayesian learning of chemisorption for bridging the complexity of electronic descriptors
Abstract Building upon thed-band reactivity theory in surface chemistry and catalysis, we develop a Bayesian learning approach to probing chemisorption processes at atomically tailored metal sites. With representative species, e.g., *O and *OH, Bayesian models trained with ab initio adsorption properties of transition metals predict site reactivity at a diverse range of intermetallics and near-surface alloys while naturally providing uncertainty quantification from posterior sampling. More importantly, this conceptual framework sheds light on the orbitalwise nature of chemical bonding at adsorption sites withd-states characteristics ranging from bulk-like semi-elliptic bands to free-atom-like discrete energy levels, bridging the complexity of electronic descriptors for the prediction of novel catalytic materials.
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- Award ID(s):
- 1845531
- PAR ID:
- 10360533
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Nature Communications
- Volume:
- 11
- Issue:
- 1
- ISSN:
- 2041-1723
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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