Internships
Within our group, we are always looking for interested students that want to do an internship with us. Have a look at our research pages to get an idea of the projects you can work on during an internship. We accept students from numerous backgrounds, ranging from physics to chemistry, or (medical) biology.
Analysis of complex GC-MS datasets
Start date: anytime
Level: BSc
Are you interested in data analysis and have a strong affinity with programming language like MATLAB or Python? We currently have an internship available where we are looking to process complex datasets as part of the DEDICATION, OBSeRVeD, BENIGN, and Synergia projects.
If you are interested please contact: dr. Joris Meurs (joris.meurs@ru.nl)
Breath VOCs as a proxy for immunotherapy response
Start date: October 2024 - November 2024
Level: MSc (Medicine, Biomedical Sciences, Human Biology, Molecular Mechanisms of Disease)
We are looking for an enthusiastic MSc student to participate within the DEDICATION project. For this internship we are looking for a student who would like to help us to find out which underlying biological mechanisms underlie the individual components that we can measure in exhaled breath of advanced NSCLC patients treated with ICIs. During your internship you will mainly be performing a literature search to gain background knowledge, collecting clinical data from patients, collecting and analysing exhaled air from patients, and analysing/merging/interpreting results that we have obtained with the different measurements. All tasks will be performed under supervision of a medical doctor and a researcher.
If you are interested please contact: dr. Joris Meurs (joris.meurs@ru.nl)
More background?
- Buma, Alessandra IG, et al. Clinical perspectives on serum tumor marker use in predicting prognosis and treatment response in advanced non-small cell lung cancer. Tumor Biology Preprint (2023): 1-11.
- Buma, Alessandra IG, et al. eNose analysis for early immunotherapy response monitoring in non-small cell lung cancer. Lung Cancer 160 (2021): 36-43.
Multispecies open-path molecular absorption spectroscopy
Start date: anytime
Level: MSc
Are you an enthusiastic student in Physics and Astronomy, Molecular Sciences, or related fields who is looking for a Master’s internship? Are you keen on developing or working with novel scientific instruments? Would you like to learn new concepts of laser spectroscopy and use them in real-life research? Then you have a part to play as an intern in our research group. Join us, learn how things work in laser spectroscopy, and gain hands-on experience in the lab and in the field!
If you are interested, please contact: dr. Amir Khodabakhsh (Amir.Khodabakhsh@ru.nl)
AI for Spectral Analysis
Start date: anytime
Level: MSc
Are you a MSc student who confidently learned AI, machine learning, neural networks, and/or deep learning? Are you interested in applying your knowledge and expertise to solve real-life problems in science? Are you willing to go further than your comfort zone and learn about molecular absorption spectroscopy, its application in breath analysis and disease detection, and face the data analysis challenges in this field? Then, we have a topic for your MSc thesis/internship.
Bear with us and read what is it all about:
The recent development of ultra-broadband and low-noise mid-infrared spectroscopy systems unleashes a great potential for sensitive and simultaneous trace detection of a very long list of molecular species. This is very interesting for applications dealing with complex matrices, such as breath analysis and plasma diagnostics. Traditionally, the classical least squares (CLS) method is used in spectral analysis to decompose interfering spectra of different species and extract their concentrations accurately. In this method, the model spectra of the species in the sample matrix are all calculated using existing databases and then fitted altogether to the measured spectra to retrieve their concentrations (a linear multiline fitting scheme).
However, CLS suffers from some difficulties and disadvantages. Strong absorption features (close to 100%), baseline drifts (i.e. drifts in the spectral power of the laser during the measurement), etalon fringes (i.e. sinusoidal instrument-specific artifacts on the spectra), and unfitted absorption features can seriously affect the accuracy of CLS. Different procedures and methods can be employed to minimize these effects; e.g. removing very strong absorption features from the overall fitting routine as well as modeling and fitting of the baseline drift and the etalon fringes by a low-order polynomial and summation of low-frequency sinewaves, respectively, to name a few. Although often effective, these procedures are time-consuming and sometimes can degrade the accuracy of the fit; e.g. removal of the baseline drift/etalon fringes can also remove some spectral features of absorbing species. Therefore, the quality of the spectral analysis degrades and cannot address demanding applications of e.g. medical data in breath analysis.
We have recently utilized a partial least squares (PLS) method with a novel hybrid dataset approach as an alternative. PLS is a purely statistical model and relies on calibration measurements as training datasets. However, constructing a real training dataset is far too time-consuming and costly, as it would require many different calibrated gas mixtures measured with high precision/accuracy. Our approach is to create a simulated dataset that is tailored to specific instruments by combining simulated absorbance spectra with measured blank (featureless background) intensity spectra. While the simulations provide the absorption spectra, the blank measurements provide the realistic unique features of the spectrometer, such as noise patterns, baseline drifts, and etalon fringes. Combining these two results in an affordable and scalable process. We have achieved encouraging results using this approach. Meanwhile, this workflow is not specific to PLS and can also be applied to other models, such as machine learning using neural networks (deep learning). Therefore, we would like to investigate this opportunity further: In particular, we know that PLS can be sensitive to outliers in real-world data, and so investigating different alternatives like the so-called LASSO (least absolute shrinkage and selection operator), that is known to be less sensitive to outliers than least-squares methods, could be a promising approach. Alternatively, we could explicitly model the (noisy) spectrometer measurement features, and include them as a separate penalty term in the overall metric. Your task will be to establish whether either of these approaches could alleviate some of the drawbacks of existing methods. Of course you could also come up with another idea of your own!
If you are interested, please contact: dr. Amir Khodabakhsh (Amir.Khodabakhsh@ru.nl)
Modelling of emission fluxes from spectroscopic data
Start date: anytime
Level: MSc
Emissions of “nitrogen” species, such as ammonia or nitrogen oxides, or emissions of greenhouse gases, such as carbon dioxide, methane, or nitrous oxide: it is an extremely relevant and hot topic which pops up in the news almost daily. However, substantial data of the actual deposition or emission of these gases in/from an area remain challenging to this data. Within our group, we have worked on a novel system, with which we can measure the concentration of such gases in an open, outdoor area. The system is based on a laser which is sent over a free, outdoor path. The absorption of light can be used to acquire information on the concentration of the molecules in the air, in that area, at that specific moment. While that is a known and proven method for quantifying concentrations in the air, the translation of this data to emission fluxes from an area (so the rate of emission of a certain gas in for example kg/hour) remains challenging. In this project, we will use data acquired by our group at a waste-water facility (see figure) to calculate the emissions of nitrogen gases and greenhouse gases from the plant. Part of the internship could be to find and try out different methods and models for these calculations, such as inverse dispersion modelling, total mass balance calculations, and more.
If you are interested, please contact: dr. Amir Khodabakhsh (Amir.Khodabakhsh@ru.nl)
Detecting VOCs from faecal and caecal samples in relation to poultry intestinal health
Start date: anytime
Level: BSc
Intestinal diseases in poultry are one of the most prevalent and costly problems in poultry production. One of the most prevalent infections is coccidiosis, which is caused by Eimeria parasites. Previous studies have shown that volatile organic compounds (VOCs) from air samples can detect and quantify Eimeria parasites in broilers. Also, it is thought that VOCs from manure samples can be of aid to identify specific biomarkers associated with shifts in the gut microbiota. As part of the OBSeRVeD, faecal and caecal samples will be analyzed with proton transfer reaction – time-of-flight – mass spectrometry (PTR-ToF-MS) and thermal desorption – gas chromatography – mass spectrometry (TD-GC-MS). The aim of these analysis is to identify potential biomarker candidates related to 1) microbiome shift and 2) Eimeria infection.
If you are interested, please contact: Pascalle Deenekamp (pascalle.deenekamp@ru.nl)
More background?
- ter Veen, C., de Bruijn, N. D., Dijkman, R. & de Wit, J. J. Prevalence of histopathological intestinal lesions and enteric pathogens in Dutch commercial broilers with time. Avian Pathol. 46, 95–105 (2017).
- Borgonovo, F. et al. A Data-Driven Prediction Method for an Early Warning of Coccidiosis in Intensive Livestock Systems: A Preliminary Study. Animals 10, 747 (2020).
- Grilli, G. et al. A pilot study to detect coccidiosis in poultry farms at early stage from air analysis. Biosyst. Eng. 173, 64–70 (2018).
Development of a mass spectrometry strategy for detecting formaldehyde in exhaled breath
Start date: September/October 2024
Level: MSc
Formaldehyde exposure is a major problem for human health. Many consumer products as cosmetics, clothing, and furniture are sources of formaldehyde. Studies have shown that longitudinal exposure causes nose cancer and potentially leukaemia. Furthermore, it has been shown in a rat study that formaldehyde can be adsorbed through the skin. However, formaldehyde is a tricky molecule to measure as it is very reactive and can potentially form dimers in biological systems. Current methods for formaldehyde In this project, the aim is to investigate the use of selective reagent ion – time-of-flight – mass spectrometry (SRI-ToF-MS) for detection of formaldehyde in exhaled breath as a non-invasive alternative. During this project, the following questions need to be answered:
- Develop a feasible strategy for formaldehyde detection in exhaled breath
- Determine the detection limit of formaldehyde in exhaled breath
- Establish background levels of formaldehyde in the air
If you are interested, please contact: dr. Joris Meurs (joris.meurs@ru.nl)
More background?
- Bartnik, F. G.; Gloxhuber, C.; Zimmermann, V. Percutaneous Absorption of Formaldehyde in Rats. Toxicology Letters 1985, 25, 167–172.
- Winkowski, M.; Stacewicz, T. Optical Detection of Formaldehyde in Air in the 3.6 Μm Range. Biomedical Optics Express 2020, 11 (12), 7019.
- Riess, U.; Tegtbur, U.; Fauck, C.; Fuhrmann, F.; Markewitz, D.; Salthammer, T. Experimental Setup and Analytical Methods for the Non-Invasive Determination of Volatile Organic Compounds, Formaldehyde and NO in Exhaled Human Breath. Analytica Chimica Acta 2010, 669 (1–2), 53–62.
Microcantilevers for chemical detection
Start date: anytime
Level: MSc
Miniaturized sensors have been in particular interest in the recent years, thanks to the advancement of technology in miniaturization of optical, electrical and mechanical systems.
One of the popular miniaturized platforms for sensing is a microcantilever (MCL). A MCL is a miniaturized beam constrained at one end with the other end extending freely outwards. An external stimulus causes the MCL to bend or oscillate in a static or a dynamic mode, respectively. In the static mode, the displacement of the MCL due to a load or intrinsic stress generated on or within the MCL is measured. In the dynamic mode, an external actuation (piezoelectric, magnetic, or electrostatic actuator) causes the MCL to oscillate at its natural (resonant) frequency. Any change in the load or mass of the MCL results in a change in frequency that is subsequently measured.
To allow chemical sensing, the MCL surface is coated with a probe coating (functionalized surface). Specific analytes are then adsorbed by specific coatings. The amount of target material is measured by monitoring a change in the MCLs natural frequency. For example, as more target analyte attaches to the surfaces, the MCL gain mass and its resonant frequency decreases. The higher the frequency shift, the greater the amount of accumulated mass. The data is then processed and the concentration of analyte is determined.
For this project, we are looking for an enthusiastic Msc student from Physics, Physical Chemistry, or Science who likes to work in the lab for testing and validation of a basic MCL sensor in a proof-of-principle experiment. This includes the external oscillation of a MCL with an activated surface with and without adsorbing a specific analyte. The main goal is to gain insights in how the MCL technology works for biosensing (i.e. detection of relevant biomarkers (in the gas or liquid phase) and potentially bacteria and viruses).
In our group we have a starting kit with several MCLs from commercial suppliers and simple electrical readouts. You will do this research at the Life Science Trace Detection Laboratory (TDLab, www.ru.nl/TDLab), Dept of Analytical Chemistry & Chemometrics (IMM, FNWI). This research is a collaboration between the TDLab and the Saxion Hogeschool (Enschede, Applied Nanotechnology).
If you are interested, please contact: dr. Amir Khodabakhsh (Amir.Khodabakhsh@ru.nl)