Issue 19, 2023

In silico characterization of nanoparticles

Abstract

Nanoparticles (NPs) make for intriguing heterogeneous catalysts due to their large active surface area and excellent and often size-dependent catalytic properties that emerge from a multitude of chemically different surface reaction sites. NP catalysts are, in principle, also highly tunable: even small changes to the NP size or surface facet composition, doping with heteroatoms, or changes of the supporting material can significantly alter their physicochemical properties. Because synthesis of size- and shape-controlled NP catalysts is challenging, the ability to computationally predict the most favorable NP structures for a catalytic reaction of interest is an in-demand skill that can help accelerate and streamline the material optimization process. Fundamentally, simulations of NP model systems present unique challenges to computational scientists. Not only must considerable methodological hurdles be overcome in performing calculations with hundreds to thousands of atoms while retaining appropriate accuracy to be able to probe the desired properties. Also, the data generated by simulations of NPs are typically more complex than data from simulations of, for example, single crystal surface models, and therefore often require different data analysis strategies. To this end, the present work aims to review analytical methods and data analysis strategies that have proven useful in extracting thermodynamic trends from NP simulations.

Graphical abstract: In silico characterization of nanoparticles

Article information

Article type
Tutorial Review
Submitted
08 mar 2023
Accepted
21 apr 2023
First published
26 apr 2023
This article is Open Access
Creative Commons BY-NC license

Phys. Chem. Chem. Phys., 2023,25, 13228-13243

In silico characterization of nanoparticles

B. Kirchhoff, C. Jung, D. Gaissmaier, L. Braunwarth, D. Fantauzzi and T. Jacob, Phys. Chem. Chem. Phys., 2023, 25, 13228 DOI: 10.1039/D3CP01073B

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