Open Student Projects in MCC

Available student projects in MCC

It is possible to do your bachelor’s or master’s graduation project within the MCC group. Below, you will find a list of open projects. You will also find topics for literature reviews.

If you are interested in doing a project, you can reach out to your would-be supervisor directly. You can also write to PhD candidates and postdocs who don’t have any open projects, as it may be possible for them to come up with a project for you.

BSc projects

There are currently no open BSc projects. If you are interested in doing a BSc project in our group, you can contact a PhD candidate or postdoc with a project that seems interesting to you directly and ask if he/she can create a project for you.

MSc projects

Support effects in Cu catalysts for CO2 hydrogenation to methanol (Supervisor: Laura Barberis)

Methanol is a chemical building block for hundreds of everyday products, but also an energy carrier.(1) Methanol is produced on an industrial scale using natural gas as the main feedstock. A valid and environmentally friendly alternative for methanol production consists of combining CO2 with H2 from CO2 renewable sources such as wind, hydro and solar power. The methanol formation is unfavourable from a thermodynamic point of view. Thus, control of  parameters such as temperature and pressure are essential. In particular low temperature (200-260 °C) and high pressure are required to enhance the feedstock conversion toward the desired product and minimize the formation of by-products, such as carbon monoxide.

Copper catalysts represent the main class of materials studied for the hydrogenation of CO2 into methanol since copper is one of the few metals that does not adsorb CO dissociatively. To enhance the activity and the selectivity towards methanol formation, reducible oxides are added as promoters and/or support.(2) However the mechanism by which they act is not fully understood.

The aim of this project is to explore metal-support interactions over copper-based catalysts for CO2 hydrogenation. To achieve this, controlled incipient wetness impregnation and deposition precipitation will be employed to synthesize Cu nanoparticles on reducible oxides support, such as ZrO2  and TiO2.(3) Long-term catalytic tests under industrially relevant high-pressure and high-temperature will be performed to evaluated catalysts activity, selectivity, and stability.(4)

1.          Olah GA, Goeppert A, Prakash GKS. Beyond Oil and Gas: The Methanol Economy. Wiley-VCH, editor. 2009.

2.          De S, Dokania A, Ramirez A, Gascon J. Advances in the Design of Heterogeneous Catalysts and Thermocatalytic Processes for CO2Utilization. ACS Catal. 2020; 14147–85.

3.          Munnik P, de Jongh PE, de Jong KP. Recent Developments in the Synthesis of Supported Catalysts. Chem Rev. 2015; 115 (14): 6687–718.

4.          Dalebout R, Visser NL, Pompe CEL, de Jong KP, de Jongh PE. Interplay between carbon dioxide enrichment and zinc oxide promotion of copper catalysts in methanol synthesis. J Catal. 2020; 392 (October): 150–8.

Ultra-pure and structured supports for silver catalysts for ethylene epoxidation (Supervisor: Claudia Keijzer)

In this project we will explore new approaches to design and assemble high surface area metal oxide nanostructures, with high phase and surface purity. These serve as supports for model catalysts that will be tested in industrially relevant reactions to provide insight on the role of the support, which is useful to optimize existing catalysts. Examples of possible design routes for structured supports[1] are the use of replicas, sacrificial templates (such as stacked PMMA spheres[2], Figure 1A), or direct foaming techniques.

A first model reaction that we study is the epoxidation of ethylene, which is very sensitive to impurities, promoters, and non-uniformities, and is highly relevant for industry: with an annual ethylene oxide production of circa 35 · 106 ton, ethylene epoxidation is one of the largest industrial processes worldwide.[3]The formation of ethylene epoxide by mild oxidation of ethylene is catalysed by silver particles deposited on an α-alumina support. Industrial catalysts show selectivities of around 90%, but only at low conversions. The unwanted side reaction is the complete combustion of ethylene to CO2.

For these epoxidation catalysts, the support has a large influence on catalyst stability and selectivity towards the desired product. α-Al2O3 is used as commercial support for ethylene epoxidation catalysts, because it displays a low volumetric density of surface OH groups, due to both a low OH surface group density (< 1 OH nm-2) and a low specific surface area (typically 1 m2 g-1, Figure 1B). The OH groups facilitate the unwanted side-reaction of ethylene oxide to eventually CO2.[4]. While α-alumina reduces side reactions, its low surface area is disadvantageous for the stability of silver particles. Building on our ultra-pure supports with controlled structure, we aim to gain a better understanding of the influence of support, impurities and promoters on the selectivity and stability of these catalysts. Other industrially relevant model reactions will be considered at a later stage of the project.

Students who are interested in a project related to this research are welcome to contact me!

1.         Studart et al., J. Am. Ceram. Soc., 89 (2006). DOI: 10.1111/j.1551-2916.2006.01044.x

2.         Van den Reijen & Keijzer, Materialia, 4 (2018). DOI 10.1016/j.mtla.2018.10.016

3.         Van den Reijen et al., Catal. Tod., 338 (2019). DOI 10.1016/j.cattod.2019.04.049

4.         Özbek & Van Santen, Catal. Lett., 143 (2013). DOI 10.1007/s10562-012-0957-3

Understanding ionic conductivity in solid-state batteries using DFT and machine learning (Supervisor: Dr. Nongnuch Artrith)

Conventional Li-ion batteries contain electrolytes that are based on flammable organic solvents, which leads to the risk of battery fires. In solid-state batteries, the liquid electrolytes are replaced by safer solid ionic conductors. However, one challenge is discovering solid electrolytes that are sufficiently good ionic conductors to build batteries with high charge and discharge rates.

In this project, you will employ computational methods to predict ionic conduction and to understand what determines the conductivity on the atomic scale. Our group has extensive experience in physics-based simulations using density functional theory (DFT) and machine learning (ML) methods for accelerated simulations [1-3], also using our own ML software package ænet (http://ann.atomistic.net). In previous work, we used a combination of DFT and ML to understand the ionic conductivity in amorphous LiPON [4]. You will learn and apply these computational techniques, and you will gain experience in the field of electrochemical energy storage (batteries).

1.          H. Guo, Q. Wang, A. Stuke, A. Urban, and N. Artrith, Front. Energy Res. 9 (2021) 695902.

2.          T. Morawietz and N. Artrith, J. Comput. Aided Mol. Des. 2 (2021) 031001.

3.          N. Artrith, K.T. Butler, F.-X. Coudert, S. Han, O. Isayev, A. Jain, and A. Walsh, Nat. Chem. 13 (2021) 505-508.

4.          V. Lacivita, N. Artrith, and G. Ceder, Chem. Mater. 30 (2018) 7077-7090.

Predicting catalytic reaction mechanisms with DFT and machine learning (Supervisor: Dr. Nongnuch Artrith)

Heterogeneous catalysis is at the core of many processes for energy conversion, such as the electrocatalytic production of synthetic fuels (e.g., hydrogen, methanol, ammonia) using clean electric energy from renewable sources. Both the activity and the selectivity of a catalyst depend sensitively on its composition. On the one hand, this makes it possible to tune catalyst properties, e.g., by modifying the composition of an alloy. On the other hand, the large number of possible compositions makes it challenging to screen materials spaces exhaustively using experimental synthesis and characterization.

In this project, you will use computational methods based on physics (density functional theory, DFT) and machine learning (ML) to predict the catalytic activity and selectivity of alloys. Our group has extensive experience in the computational characterization of catalytic reactions with DFT and ML [1-4]. For example, in previous work, we demonstrated that ML can be used to learn from both computational and experimental data to predict novel catalyst compositions [5]. You will learn, apply, and potentially further develop DFT/ML techniques for catalyst discovery.

1.         N. Artrith, W. Sailuam, S. Limpijumnong, and A.M. Kolpak, Phys. Chem. Chem. Phys. 18 (2016) 29561.

2.          J.S. Elias, N. Artrith, M. Bugnet, L. Giordano, G.A. Botton, A.M. Kolpak, and Y. Shao-Horn, ACS Catal. 6 (2016) 1675-1679.

3.          S. Wannakao, N. Artrith, J. Limtrakul, A.M. Kolpak, J. Phys. Chem. C 121 (2017) 20306.

4.          N. Artrith, Matter (Cell Press) 3 (2020) 985-986.

5.          N. Artrith, Zhexi Lin, Jingguang G. Chen, ACS Catal. 10 (2020) 9438–9444 (Letter).

Analysis on TEM videos of carbon filament growth on Ni-based catalysts during the conversion of methane to hydrogen (Supervisor: Tom Welling)

We study the decomposition of methane into hydrogen while growing carbon materials using Ni-based catalysts. Methane is a readily-available, cheap feedstock, but due to CO2 emissions it is not ideal for use as fuel. Decomposition of methane into the clean fuel hydrogen while creating solid carbon is a promising alternative. In this study, transmission electron microscopy (TEM) is used as the main technique to study this process as it allows us to look at nanomaterials at nanometer spatial resolution on the single particle level. In previous work an environmental TEM was employed to study nanofiber growth at millibar pressures [1]. Using gas-cell in-situ TEM we imaged the nucleation and growth phase of carbon filaments from methane in more relevant conditions, by varying parameters such as temperature, feed composition, pressure, but also catalyst size, composition and shape.

In this Master student project, unique information from in-situ TEM experiments about carbon filaments growth and entanglement will be linked to process conditions and catalyst structure. This means a lot of data analysis is required in this project. For example, we could link the composition or size of single particles to the growth speed and total length of the carbon filament that grows from them. Many real-time TEM videos of the active catalyst are already available for analysis (Figure 1). Depending on the wishes of the Master student, it is also possible to do synthesis of catalysts for use in the TEM experiments. You will directly observe the catalyst at work and find new insights in the processes by systematically analysing real-time and real-space TEM videos. You will find new ways to analyze catalysts on the single particle level and significantly contribute to the understanding of carbon filament growth.

Figure 1: In-situ TEM images of the growth of carbon nanofibers on Nickel-Copper particles (3:1 Ni:Cu ratio) on a carbon support at atmospheric pressure with 10% hydrogen, 30% methane and 60% argon gas.

[1] S. Helveg et al., Nature 427, 426-429 (2004)

MSc literature reviews

There are currently no open literature review projects. If you are interested in doing a literature review project in our group, you can contact a PhD candidate or postdoc with a project that seems interesting to you directly and ask if he/she can create a project for you.

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