Agilent Science Futures – Interview with Tijmen Bos


In this Science Futures article we hear from Tijmen Bos, PhD student at Vrije Universiteit Amsterdam (VU) and University of Amsterdam (UvA). The objective of Tijmen’s research project is to develop and improve multidimensional analytical methods for the characterization of polymer materials. His research is part of the UNMATCHED Project, which is a public-private collaboration of the two universities VU and UvA, three large chemical companies and other industrial partners.

In this interview, we learn about the impact Tijmen’s research could have on society and how interactions with industry helped advance his research and prepare him for the end of his PhD.

Can you tell us more about your research?

Tijmen Bos (VG):
Polymers are often very complex mixtures of molecules comprising one or more distributions of chemical characteristics. The innovation of new polymeric materials is generally accompanied by increased molecular complexity and, therefore, these samples require new separation and quantification strategies. This is what we are working on with a team of five doctoral students and supervisors from both academia and industry.

Control and knowledge of the analytical conditions applied are essential for the accurate interpretation of the measured polymer distributions. We often use gradient liquid chromatography (LC) and one problem we have is that the resulting gradient may deviate from the defined gradient. This gradient deformation by the LC system can cause shifts in the retention times of the polymers. Additionally, it complicates the reliable modeling of retention as applied in method development for two-dimensional (2D) LC.

As such, there was an overlap between the goals of the UNMATCHED project and the framework of the collaboration between Dr. Bob Pirok (UvA) and Agilent on 2D-LC optimization. With the support of my colleagues Léon, Mimi and Stef, I therefore worked on the development of our current algorithm which helps correct the retention parameters for the effects of gradient deformation. I feel that we are going in the right direction with this algorithm and we will continue to work on it. I am personally very interested in automation and chemometrics and so it is great fun to work on such projects. In addition, I believe that the projects which contribute to our main academic research objectives, but which also benefit a number of industrial partners, demonstrate the great synergy that can be achieved when universities and industry actively work together.

What are the main or most important results of your research?

We have successfully developed an algorithm that describes and predicts the strain of LC gradients using mathematical models. Above all, it allows the acquisition of retention parameters that depend less on the instrument and this allows a more reliable retention modeling and better optimization of the method. Hopefully this will lead to more general automated workflows and speed up method development and future research. I will continue to work on improving the algorithm with my colleague Leon Niezen MSc (UvA). Based on our description and correction for gradient strain, we now investigate how we can translate the measured shape of a polymer distribution into its actual shape obtained by LC.

What global or societal challenges does your research address?

Biodegradable polymeric materials are very relevant in modern society. Biodegradable polymers are often based on complex biopolymers, such as cellulose. Cellulose can be obtained from natural sources such as wood which can be harvested sustainably. By modifying cellulose by (hydro) alkylation, cellulose ethers can be obtained. These cellulose ethers exhibit many preferable properties for use in paint, food and pharmaceuticals. To ensure that these complex polymeric materials, as well as their biodegradation, can be studied, precise molecular distributions are essential. In addition, the research is very relevant for the retention modeling used for the development of the LC method. Having the tools to quickly develop analytical methods contributes greatly to the efficiency of analytical laboratories and the customers who rely on them. Taking into account the real shape of the gradient in the retention modeling allows to obtain retention parameters less dependent on the instrumentation, thus making the results more reproducible. This brings us closer to a generation of automatic, instrument-independent workflows.

Ultimately, this will connect with the global challenge of creating circular processes without sacrificing material quality and safety.

Is there a particular issue or issue that your research faces that you are addressing?

The main bottleneck is the lack of knowledge about structure-property relationships. New multidimensional analytical methods help predict which chemical innovations will lead to improved materials. In addition, the industry relies on the use of validated methods. Translating these methods into advanced analytical techniques takes time and is often a barrier to innovation. By using the algorithms that we are developing, new methods can be introduced much more easily and quickly.

Has the technology to which you have access guided / influenced your studies?

Yes, because this is the kind of equipment that will eventually be used in industry. The continued availability of analytical equipment and the development of the underlying technology dramatically accelerates our research capabilities and, perhaps more importantly, poses new questions for scientists.

Have you had the opportunity to interact with industry and businesses to advance your research?

My project is co-sponsored by three leading chemical companies and some aspects of my work are supported by an industry grant. Industry involvement is essential for the project. The equipment and samples allowed me to start my research and thanks to secondments to partner companies, I can interact with industry experts and implement my work directly on site. I really enjoy these interactions. For example, I recently visited and worked at one of Nouryon’s sites in Sweden to learn more about the production and current quality assessment of their polymers.

We are also working closely with our industrial partner, Agilent Technologies, who is providing both the hardware and the software needed to complete part of this project. This means that the project focuses on instrumental or technological developments as well as on strategies to assess the applicability of the technology to relevant research areas.

What opportunities have you had to work in the industry? How has this weather helped you prepare for work in the industry?

As a PhD student my work experiences in industry are obviously still limited, but as stated above I will be doing secondments to Nouryon, BASF and DSM. In addition, during my BSc and MSc studies, I did several internships in industry, one of which led to a part-time job. It gave me a good impression of how the industry works and how it approaches the analysis of very complex samples.

In my experience, the industry often has a different mindset than academia. In my current environment, I get the best of both worlds, academic depth with an industrial focus.

What challenges do you face as a doctoral student in understanding your options at the end of a doctorate?

During my thesis, I am in close contact with many industrial partners. This gives me a pretty clear view of the potential jobs. Still, it makes me wonder what other companies it would be interesting to work for.

As a result of your studies and research, what will be your career destination?

My passion lies in automation and advanced chemometrics. I would like to find a position in industry or academia where I can continue to contribute in this direction.

Find the previous installment of Science Futures, an interview with Rajannya Sen, here.


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