ArticleThe journal of physical chemistry. A2023
Computational Insights into the Adsorption of Ligands on Gold Nanosurfaces.
Article in The journal of physical chemistry. A, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Atomistic QM/Classical Modeling of Surface-Enhanced Infrared Absorption.The journal of physical chemistry. C, Nanomaterials and interfaces · 2026Article
- On the Energy Contributions Driving Pyridine Adsorption on Silver and Gold Nanoparticles.Nanomaterials (Basel, Switzerland) · 2025Article
- Modeling Raman Spectra in Complex Environments: From Solutions to Surface-Enhanced Raman Scattering.The journal of physical chemistry letters · 2025Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We study the adsorption process of model peptides, nucleobases, and selected standard ligands on gold through the development of a computational protocol based on fully atomistic classical molecular dynamics (MD) simulations combined with umbrella sampling techniques. The specific features of the interface components, namely, the molecule, the metallic substrate, and the solvent, are taken into account through different combinations of force fields (FFs), which are found to strongly affect the results, especially changing absolute and relative adsorption free energies and trends. Overall, noncovalent interactions drive the process along the adsorption pathways. Our findings also show that a suitable choice of the FF combinations can shed light on the affinity, position, orientation, and dynamic fluctuations of the target molecule with respect to the surface. The proposed protocol may help the understanding of the adsorption process at the microscopic level and may drive the in-silico design of biosensors for detection purposes.
Identifiers
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Registered trials
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