A1 Journal article (refereed)
A method for structure prediction of metal-ligand interfaces of hybrid nanoparticles (2019)
Malola, S., Nieminen, P., Pihlajamäki, A., Hämäläinen, J., Kärkkäinen, T., & Häkkinen, H. (2019). A method for structure prediction of metal-ligand interfaces of hybrid nanoparticles. Nature Communications, 10, Article 3973. https://doi.org/10.1038/s41467-019-12031-w
JYU authors or editors
Publication details
All authors or editors: Malola, Sami; Nieminen, Paavo; Pihlajamäki, Antti; Hämäläinen, Joonas; Kärkkäinen, Tommi; Häkkinen, Hannu
Journal or series: Nature Communications
eISSN: 2041-1723
Publication year: 2019
Volume: 10
Article number: 3973
Publisher: Nature Publishing Group
Publication country: United Kingdom
Publication language: English
DOI: https://doi.org/10.1038/s41467-019-12031-w
Publication open access: Openly available
Publication channel open access: Open Access channel
Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/65458
Abstract
Hybrid metal nanoparticles, consisting of a nano-crystalline metal core and a protecting shell of organic ligand molecules, have applications in diverse areas such as biolabeling, catalysis, nanomedicine, and solar energy. Despite a rapidly growing database of experimentally determined atom-precise nanoparticle structures and their properties, there has been no successful, systematic way to predict the atomistic structure of the metal-ligand interface. Here, we devise and validate a general method to predict the structure of the metal-ligand interface of ligand-stabilized gold and silver nanoparticles, based on information about local chemical environments of atoms in experimental data. In addition to predicting realistic interface structures, our method is useful for investigations on the steric effects at the metal-ligand interface, as well as for predicting isomers and intermediate structures induced by thermal dynamics or interactions with the environment. Our method is applicable to other hybrid nanomaterials once a suitable set of reference structures is available.
Keywords: nanoparticles; ligands; computational chemistry
Contributing organizations
Related projects
- Structure prediction of hybrid nanoparticles via artificial intelligence (HNP-AI)
- Häkkinen, Hannu
- Research Council of Finland
- STRUCTURE PREDICTION OF HYBRID NANOPARTICLES VIA ARTIFICIAL INTELLIGENCE
- Kärkkäinen, Tommi
- Research Council of Finland
- Competitive funding to strengthen universities’ research profiles. Profiling actions at the JYU, round 3
- Hämäläinen, Keijo
- Research Council of Finland
Ministry reporting: Yes
Reporting Year: 2019
JUFO rating: 3