Agilent Science Futures – An Interview With Tijmen Bos
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In this instalment of Science Futures, we hear from Tijmen Bos, a PhD candidate at the Vrije Universiteit Amsterdam (VU) and the University of Amsterdam (UvA). The aim of Tijmen’s research project is to develop and improve multi-dimensional 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 leading chemical companies and other industry partners.
In this interview, we learn about the impact that Tijmen’s research could have on society and how interactions with industry have helped progress his research and prepare him for the end of his PhD.
Can you tell us more about your research?
Tijmen Bos (TB): Polymers are often highly complex mixtures of molecules comprising one or more distributions of chemical features. The innovation of new polymer materials is typically accompanied with an increased molecular complexity and consequently these samples require novel separation and quantification strategies. This is what we are working on with a team of five PhD students and supervisors from both academia and industry.
Crucial for the accurate interpretation of measured polymer distributions is control and knowledge of the applied analytical conditions. We often use gradient liquid chromatography (LC), and one issue we encounter is that the obtained gradient may deviate from the set gradient. This deformation of the gradient by the LC system may cause shifts in polymer retention times. Furthermore, it complicates reliable retention modeling as applied in method development for two-dimensional (2D) LC.
As such, there was an overlap between the goals within 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 Leon, Mimi and Stef, I have therefore been working on the development of our present algorithm that aids in correcting retention parameters for the effects of gradient deformation. I feel that we are moving in the right direction with this algorithm and we will continue to work on it. I am personally quite interested in automatization and chemometrics and thus it is great fun to work on such projects. Furthermore, I think that projects which contribute to our primary academic research goals, yet also benefit a number of industrial partners demonstrate the great synergy that can be achieved when academia and industry actively work together.
What are the main or most important outcomes of your research?
TB: We have successfully developed an algorithm that describes and predicts the deformation of LC gradients using mathematical models. Importantly, this enables the acquisition of retention parameters that are less instrument dependent and it allows more reliable retention modeling and better method optimization. Hopefully, this will lead to more general, automated workflows and speedup method development and future research. I will continue to work on the further improvement of the algorithm together with my colleague Leon Niezen MSc (UvA). Based on our description and correction of the gradient deformation, we are now investigating how we can translate the measured shape of a polymer distribution to its actual shape obtained by LC.
What global or societal challenges does your research address?
TB: Biodegradable polymer materials are highly relevant in modern society. The 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 in a sustainable way. By modifying the cellulose with (hydro)alkylation, cellulose ethers can be obtained. These cellulose ethers introduce 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, accurate molecular distributions are indispensable. In addition, the research is highly relevant for the retention modeling used for LC method development. Having tools available to develop analytical methods rapidly greatly helps the efficacy of analytical labs and the customers relying on them. Taking the actual gradient shape into account in the retention modeling results in retention parameters that are less dependent on the instrumentation, thus making the results more reproducible. This brings us a step closer to a generation of instrument-independent automatic workflows.
Ultimately, this will connect to the global challenge of creating circular processes without sacrificing the quality and safety of materials.
Is there a particular problem or issue that your research faces that you are addressing?
TB: The main bottleneck is a lack of knowledge on structure-property relationships. New multi-dimensional analytical methods help to predict which chemical innovations will lead to improved materials. Also, industry relies on the use of validated methods. Translating such methods to state-of-the-art analytical techniques is time-consuming and often a barrier for innovation. Using the algorithms that we are developing, new methods can be introduced much more smoothly and rapidly.
Has the technology you have access to, steered/influenced your studies?
TB: Yes, because this is the kind of equipment that will ultimately be used in industry. The continued availability of analytical equipment and the development of the underlying technology greatly accelerates our research capabilities and, perhaps more importantly, induces new questions for scientists to look at.
Have you been given opportunities to interact with industry and companies to progress your research?
TB: My project is co-sponsored by three leading chemical companies and aspects of my work are supported by an industry grant. The involvement of industry is essential for the project. The equipment and samples allowed me to start my research and through secondments at partner companies, I can interact with industry experts and implement my work directly on-site. I really value these interactions. For example, I recently visited and worked at one of the sites of Nouryon in Sweden to learn more about the production and current quality assessment of their polymers.
We are also working in close collaboration with our industry partner, Agilent Technologies, who provide both the hardware and software for carrying out part of this project. This means that the project is focusing on the instrumental, or technological, developments as well as strategies to assess the applicability of the technology to relevant research areas.
What opportunities have you had to work within industry? How has this time helped you to prepare for working in industry?
TB: As a PhD student, my experiences of working within industry obviously are still limited, but as indicated above I will do secondments at Nouryon, BASF and DSM. Moreover, during my BSc and MSc studies I performed several internships within industry, one of which led to a part-time job. This provided me with a good impression of how industry works and how they approach the analysis of very complex samples.
In my experience, industry often has a different mindset than academia. In my current environment I get the best from both worlds, the academic depth with industrial focus.
What challenges do you face as a PhD student in understanding your options at the end of a PhD?
TB: During my PhD, I am in close contact with a number of industrial partners. This provides me with quite a clear view on potential jobs. Still, this makes me wonder which other companies would be interesting to work for.
As a result of your studies and research work, what do you envisage your career destination as being?
TB: My passion lies in automation and advanced chemometrics. I would love to find a position in industry or academia where I can continue to contribute in this direction.
Catch up on the previous instalment of Science Futures, an interview with Rajannya Sen, here.
Find out about some of the common themes to come out of the project here.