Open Student Projects in MCC

Available student projects in MCC

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 she/he has a project for you. You can find an overview of the PhD candidates and postdocs via our Team site. If you cannot find a suitable project, please ask our coordinator Peter Ngene.

Literature thesis: If you are interested in performing a literature thesis in our group, you can contact a PhD candidate or postdoc with a project that seems interesting to you directly and ask if she/he has a project for you. You can find an overview of the PhD candidates and postdocs via our Team site. If you cannot find a suitable project, please ask our coordinator Peter Ngene.

BSc projects

Below are some examples of open BSc projects. 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 advertise any open projects. You can find an overview of the PhD candidates and postdocs via our Team site. If you cannot find a suitable project, please ask our coordinator Peter Ngene.

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.

MSc projects

Below are some examples of open MSc projects. 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 advertise any open projects. You can find an overview of the PhD candidates and postdocs via our Team site. If you cannot find a suitable project, please ask our coordinator Peter Ngene.

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).

Copper-based catalysts for the production of DME from syngas (Supervisor: Yuang Piao)

Dimethyl ether (DME) can be used as an excellent alternative to diesel fuel due to its high cetane number (55–60) and a low emission of CO, NOx in the exhaust gases from a diesel engine as it has no C-C bond structures.[1] The common way in the industrial field is to convert to DME through syngas. To convert syngas to DME, you need two components, one is active metal convert syngas to the methanol and another is acid site to dehydrogenate methanol to the DME.[2] Several parameters about these two components are not been systematically explored such as proximity and acidity. The main goal of this project is exploring the effect of proximity on the selectivity, stability and activity of the catalysts.

In this project, copper is been used as a active metal because copper could adsorb CO but not dissociated it. And we select g-Alumina as the support, not only because we have a mature preparation method but also it is easy to tune.

To achieve the project aim, incipient wetness impregnation, deposition precipitation and self-assemble method will be employed to synthesize different proximity catalysts. After some basic characterization, we will select catalysts that meet the requirements and evaluate it through a fixed bed reactor. You will study the preparation and characterization and analyze of catalysts, some basic electron microscope knowledge and catalyst evaluation methods.

Fig. 1: Different proximity of two components [3].

[1] N. Tsubaki et al., Applied Catalysis B: Environmental, 217, 494–522 (2017)

[2] A. Corma et al., Advanced Materials, 2002927 (2020)

[3] Y. Wang et al., Chemical Science, 9, 4708 (2018)

Pt-promoted reduction of NiO-SiO2 catalysts based on OX-50 support (Supervisor: Min Tang)

Supported non-noble metal catalysts, such as Ni, Co, Fe, are often difficult to obtain or to keep in the metallic state. The addition of noble metal promoters (e.g., Pt) facilitate the reduction of the non-noble metal oxide precursors, which greatly improves the performance of the catalysts. However, how exactly the Pt affects the reduction of metal oxide remains unclear. In this project, by using high resolution ex situ and in situ STEM we want to study the reduction of metal oxide, the effect of Pt addition and the proximity of Pt and metal on the reduction, at the nanometer and the atomic scales. As shown in Figure 1, I mainly do high resolution S/TEM and in situ gas S/TEM to directly investigate the distribution of Pt and the reduction process of metal oxide.

Figure 1. a) The high resolution HAADF-STEM images of Pt-Co3O4-SiO2 showing the distribution of Pt nanoparticles on Co3O4 and SiO2. b) The reduction processes Pt-Co3O4-SiO2 by using in situ atmospheric pressure STEM.

First well dispersed samples with different proximities should be synthesized as shown in Figure 2, to study the effect of the proximity of Pt and Ni on the reduction of NiO. Different methods, such as incipient wetness impregnation, wet impregnation, deposition precipitation, and physical mix will be used. TEM is used to characterize the samples, including the particle size, the distribution of the Pt, and even the atomic structure of NiO and Pt. By using TPR, the reducibility of the catalysts is tested. And we will compare ex situ reduction in the oven and ex situ TEM with in situ reduction in TEM.

Figure 2. The diagram showing the catalyst with different proximities of Pt and Ni.

If you are interested in synthesis and TEM, just contact me without hesitation. I will share my knowledge and experience as much as I can.

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