Open Student Projects in MCC

Available student projects in MCC

On this page, you can find information on BSc, MSc and literature review projects.

BSc projects

If you are interested in doing a BSc project, you can write to any of our PhD candidates and postdocs. 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.

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.

Alloyed Pd-M nanoparticles for selective hydrogenation catalysis (Supervisor: Kristiaan Helfferich)

Heterogeneous catalysts are crucial for the efficient production of chemicals and fuels. These catalysts typically comprise of metal nanoparticles held together on a porous support material. The size, shape, composition and distribution of these nanoparticles is important for the activity, stability and selectivity of a catalyst. Consequently, the ability to control nanoparticle synthesis in a controlled way has both economic and scientific implications.1

Earlier research has demonstrated that bimetallic nanoparticles have modified properties compared to their monometallic constituents. For example, an altered modified electronic surface structure can enhance catalytic activity and selectivity through having more favorable adsorption affinities for key intermediates. Moreover, active but expensive metals like Pd can be partially replaced by more abundant metals like Fe, Ni or Cu. Hence, bimetallic catalysts are interesting to study.

  1. Munnik, P.; De Jongh, P. E.; De Jong, K. P. Recent Developments in the Synthesis of Supported Catalysts. Chem. Rev. 2015, 115 (14), 6687–6718.

Gold-based catalysts for HMF oxidation (Supervisor: Hidde Nolten)

Conversion of biomass into useful chemicals is relevant to decrease our dependency on fossil fuels. 5-hydroxymethyl furfural (HMF), a chemical derived from sugar, can be used to make 2,5-furandicarboxylic acid (FDCA), which again is used to make bio-based polymers. A simplified reaction mechanism with several intermediates is depicted in the figure. Herein, a selective catalyst is required to obtain a high FDCA yield and not end up with side-products, such as diformylfuran (DFF) and 2,5-hydroxymethylfurancarboxylic acid (HMFCA).[1]

Gold catalysts were found to be very active in HMF oxidation[2,3] and to obtain FDCA via the HMFCA pathway, whereas other oxidation catalysts such as platinum make FDCA via DFF. The subsequent conversion of HMFCA to FFCA and FDCA is often the yield-limiting step for gold catalysts. In this project we try to increase the FDCA yield by alloying gold with a second metal. Specifically, AuAg was recently found to perform very well in HMF oxidation[4], but also AuPd has shown potential[5]. Theretofore, we have to develop and evaluate catalyst synthesis procedures that yield uniform, monodisperse catalysts. The catalysts will be characterized using techniques as X-Ray Diffraction, UV-VIS spectroscopy, elemental weight loading determination using ICP-OES and finally Transmission Electron Microscopy. The latter is especially important, since TEM not only gives us information on the particle size distribution, but can also give us information about elemental distribution over the catalyst using EDX. Moreover, this technique is also interesting to apply after catalytic experiments to evaluate catalyst degradation mechanisms as particle growth and atomic redistribution[6,7]. Eventually, we will use these catalysts in HMF oxidation, in which first the activity and selectivity are of interest. Additionally, we can assess its long-term stability, recyclability, study the reaction kinetics or vary reaction parameters, such as added base concentration and temperature.

If you are interested in a project with a focus on catalyst design, characterization and testing, feel free to contact me at We can discuss the project and customize it to your likings and skills accordingly.

[1]          Davis, S. E., Houk, L. R., Tamargo, E. C., Datye, A. K. & Davis, R. J. Oxidation of 5-hydroxymethylfurfural over supported Pt, Pd and Au catalysts. Catal. Today 160, 55–60 (2011).

[2]         Donoeva, B., Masoud, N. & De Jongh, P. E. Carbon Support Surface Effects in the Gold-Catalyzed Oxidation of 5-Hydroxymethylfurfural. ACS Catal. 7, 4581–4591 (2017).

[3]          Masoud, N., Donoeva, B. & de Jongh, P. E. Stability of gold nanocatalysts supported on mesoporous silica for the oxidation of 5-hydroxymethyl furfural to furan-2,5-dicarboxylic acid. Appl. Catal. A Gen. 561, 150–157 (2018).

[4]          Schade, O. R. et al. Selective Aerobic Oxidation of 5-(Hydroxymethyl)furfural over Heterogeneous Silver-Gold Nanoparticle Catalysts. Adv. Synth. Catal. 362, 5681–5696 (2020).

[5]          Villa, A., Schiavoni, M., Campisi, S., Veith, G. M. & Prati, L. Pd-modified Au on carbon as an effective and durable catalyst for the direct oxidation of HMF to 2,5-furandicarboxylic acid. ChemSusChem 6, 609–612 (2013).

[6]          Masoud, N., Partsch, T., de Jong, K. P. & de Jongh, P. E. Thermal stability of oxide-supported gold nanoparticles. Gold Bull. 52, 105–114 (2019).

[7]          Masoud, N. et al. Silica-supported Au-Ag Catalysts for the Selective Hydrogenation of Butadiene. ChemCatChem 9, 2418–2425 (2017).

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 ( 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)

Literature review projects

Below are some examples of open literature review projects in our group. 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.

Addressing the effects of mass-transfer-limitations in selective hydrogenation reactions (Supervisor: Oscar Brandt Corstius)

Hydrogenation reactions are at the basis of a plethora of catalytic processes, such as in the production of fine chemicals or medicine, in the petrochemical industry and in environmental processes.1 In selective hydrogenation, these catalytic transformations are required to be specific to a single functional group or reactant, while leaving others untouched.2 One relevant example from the polymerization industry is the selective hydrogenation of polyolefin impurities from mono-olefin gas streams.3 In this process, relatively high concentrations of polyunsaturates (e.g. alkynes and alkadienes) have to be reduced from 2-5% down to the ppm-level. In my project, this purification reaction is investigated for the selective hydrogenation of traces of 1,3-butadiene in a large excess of propylene, as a model system.

Typical industrial catalysts for selective hydrogenation are supported palladium nanoparticles (Pd NPs), because of Pd its high activity at low temperatures. However, pure Pd NPs are often reported to suffer from poor selectivity towards alkene products. In efforts to improve the selectivity of Pd, modifiers are typically added which eventually decrease the intrinsic activity of Pd. For example, in the commercial Lindlar-catalyst (Pb-modified 5 wt.% Pd/CaCO3) it has been estimated that only 0.02 wt.% of the palladium is active during operation.4 This arises the question whether the reported selectivity of Pd NPs is truly intrinsic to the metal, or a function of the reaction conditions, for example influenced by mass-transfer-limitations.

In this Literature review we would like to understand the effect on selectivity by the extreme activity in selective hydrogenation reactions over Pd, as well as comparing with other metals. This will include a literature survey of papers that study selective hydrogenation reactions, as well as individual assessment of the published work in an comprehensive overview.

If you would like to know more, or have questions regarding this topic at the interface of fundamental science and chemical engineering, feel free to contact me to discuss over a coffee.

Schematic reaction overview of selective hydrogenation of 1,3-butadiene in excess of propylene. In internal mass-transfer-limitations, or diffusion limitations, there is a steep concentration gradient within a catalyst particle. This promotes over-hydrogenation (red arrow), rather than selective hydrogenation (green arrow), because of the absence of butadiene farther away from the catalyst particles’ edges.

1.          Bond, G. C. Metal-Catalysed Reactions of Hydrocarbons. (Springer US, 2005). doi:10.1007/b136857.

2.          Zhang, L., Zhou, M., Wang, A. & Zhang, T. Selective Hydrogenation over Supported Metal Catalysts: From Nanoparticles to Single Atoms. Chem. Rev. 120, 683–733 (2020).

3.          Derrien, M. L. Selective Hydrogenation Applied to the Refining of Petrochemical Raw Materials Produced by Steam Cracking. in Studies in Surface Science and Catalysis vol. 27 613–666 (1986).

4.          Vilé, G., Albani, D., Almora-Barrios, N., López, N. & Pérez-Ramírez, J. Advances in the Design of Nanostructured Catalysts for Selective Hydrogenation. ChemCatChem 8, 21–33 (2016)

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