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Cited by 220

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Naturally Chiral Metal Surfaces as Enantiospecific Adsorbents

Sholl, David S., Asthagiri, Aravind, Power, Timothy D.·2001·The Journal of Physical Chemistry B

Abstract

The creation of solid surfaces with intrinsic chirality is one promising route for developing novel enantiospecific processes. This paper focuses on high Miller index surfaces of pure metals such as Pt(643) and Pt(531). Many high Miller index surfaces exist that are intrinsically chiral due to their surface structure. We present a succinct naming and characterization scheme for these chiral metal surfaces. We also discuss the existence of intrinsically chiral semiconductor surfaces. After reviewing previous experimental work on chiral metal surfaces, we describe a series of atomistic simulations we have used to assess the enantiospecific adsorption properties of chiral hydrocarbons on chiral Pt surfaces. To examine the role of thermally driven surface disorder on surface chirality, we present a model for Pt step edge roughening based directly on diffusion energy barriers computed using density functional theory. Detailed simulations with this model indicate that naturally chiral metal surfaces retain their net chirality even after their local structure is disrupted by thermal step roughening.

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Metadata

Authors
Sholl, D. S., Asthagiri, A., & Power, T. D.
Venue
The Journal of Physical Chemistry B
Published
2001-04-26
Volume
105
Issue
21
Pages
4771-4782
ISSN
  • 1520-5207 (ISSN-L)
  • 1520-6106
OpenAlex
W2951033497
Cited by
220
References
1
FWCI
15.41
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Naturally Chiral Metal Surfaces as Enantiospecific Adsorbents. https://app.cochat.ai/papers/10.1021/jp004524x
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Sholl, D. S., Asthagiri, A., & Power, T. D. (2001). Naturally Chiral Metal Surfaces as Enantiospecific Adsorbents. The Journal of Physical Chemistry B. https://doi.org/10.1021/jp004524x

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.