Kinetics of Nanoparticle Catalysis from First Principles
Doctoral thesis, 2019
This thesis develops the methodologies of first-principles kinetic simulations over NPs. Multiple factors affect modeling of reactions over NPs, such as reaction energy landscapes and entropy changes during reaction. This makes it important to investigate different methodological choices, within kinetic modeling.
Herein, Complete Potential Energy Sampling (CPES) is introduced as a method to calculate adsorbate entropy. CPES directly samples the adsorbate potential energy landscape, which allows for systematic improvements over approximate models within mean-field kinetics. CPES is tested on CO-oxidation over Pt(111), where it improves agreement with experimental references. Furthermore, CPES is applied to enable accurate description of molecular entropy in zeolites.
Reaction energy landscapes on NPs are challenging to calculate as NPs contain multiple different sites. Thus, NPs are commonly approximated using extended surfaces as model systems. In this thesis, the challenge of mapping out NP reaction energy landscapes is solved pragmatically using scaling relations. Kinetic Monte Carlo simulations are used to investigate the kinetics for CO-oxidation over Pt and selective acetylene hydrogenation over Pd/Cu single-atom alloys. It is found that kinetic couplings between the NP-sites govern the kinetics. The kinetic couplings influence how turnover frequency and selectivity depend on particle size, shape, and strain. Thus, the energetics of isolated sites and extended surface models are found to have limited value as descriptors for NP catalysis.
Density functional Theory
Mean field approximation
Kinetic Monte Carlo
Chalmers, Physics, Chemical Physics
Adsorbate Entropies with Complete Potential Energy Sampling in Microkinetic Modeling
Journal of Physical Chemistry C,; Vol. 121(2017)p. 7199-7207
Monte Carlo Potential Energy Sampling for Molecular Entropy in Zeolites
Journal of Physical Chemistry C,; Vol. 122(2018)p. 20351-20357
MonteCoffee: A programmable kinetic Monte Carlo framework
Journal of Chemical Physics,; Vol. 149(2018)
First-Principles Microkinetic Modeling of Methane Oxidation over Pd(100) and Pd(111)
ACS Catalysis,; Vol. 6(2016)p. 6730-6738
Connection between macroscopic kinetic measurables and the degree of rate control
Catalysis Science and Technology,; Vol. 7(2017)p. 4034-4040
Scaling Relations and Kinetic Monte Carlo Simulations To Bridge the Materials Gap in Heterogeneous Catalysis
ACS Catalysis,; Vol. 7(2017)p. 5054-5061
The Site‐Assembly Determines Catalytic Activity of Nanoparticles
Angewandte Chemie - International Edition,; Vol. 57(2018)p. 5086-5089
Strain affects CO oxidation on metallic nanoparticles non-linearly
Topics in Catalysis,; Vol. 62(2019)p. 660-668
Influence of atomic site-specific strain on catalytic activity of supported nanoparticles
Nature Communications,; Vol. 9(2018)
Jørgensen, M. and Grönbeck, H. - Selective acetylene hydrogenation over single-atom alloy nanoparticles by kinetic Monte Carlo
This might seem like a bold statement at first; but did you know that nanoparticles are used in about 90% of the chemical industry, and are the main actors in cleaning exhaust from cars?
The technological devices used for automotive exhaust treatment are called catalysts. Catalysts are composed of nanoparticles that transform toxic gases, such as CO, into less harmful chemicals such as CO2 . Such a transformation takes place on the surface of the nanoparticle, which has been researched for over a 100 years. Catalysts are often composed of scarce metals, such as platinum, palladium, and gold; and even small improvements in efficiency can have huge impacts on a global scale. Despite the efforts to understand what characterizes an efficient catalyst, the technology is often developed by trial and error. This is partly because catalyst-nanoparticles are smaller than the wavelength of visible light and can only be seen using electron microscopes. Therefore, understanding how chemical reactions proceed over nanoparticles is an achievement that can have tremendous consequences for gobal pollution control and chemical technology.
Today, computers are so powerful that it is possible to perform computer experiments solely on the screen. Computer simulations have the advantage that we directly can address causes and effects. Such insights may help answer the tough question: How do we arrange and combine the atoms in nanoparticles to design a cheap, efficient, and sustainable catalyst? In this thesis, I delve into developing and performing computer simulations to understand chemical reactions on the surface of nanoparticles. The computer simulations are performed over simple, yet, realistic nanoparticles, as they appear in an electron microscope. I give examples of methods developed to perform the computer simulations, the information we can extract from them, and how the simulations relate to real experiments.
The simulations revealed that the chemistry of the individual atoms that make up the nanoparticle is quite different from the chemistry of the combined system. That is, the system behaves differently than the sum of its parts. This information helped finding interesting relations between catalytic efficiency/effectiveness and particle shape and size. While the methods and results in this thesis helps understanding chemical reactions on nanoparticles, more investigations are required to disentangle the complex chemistry of nanoparticle catalysis. Ultimately, we may be able to design a sustainable future, atom by atom.
Areas of Advance
Nanoscience and Nanotechnology (SO 2010-2017, EI 2018-)
Condensed Matter Physics
C3SE (Chalmers Centre for Computational Science and Engineering)
Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 4553
PJ lecture hall, Fysikgården 2B, Fysik Origo.
Opponent: Prof. Michail Stamatakis, University College London, United Kingdom.