“Seeing a new technology come to life is what excites me most. Given my focus on novel technologies, particularly new ionization and hyphenation approaches in mass spectrometry, this sense of discovery is deeply embedded in my work.”
What does your research focus on, and what problem are you trying to solve?
My research centers on the use of high-resolution mass spectrometry to unravel ultracomplex mixtures in energy-transition and environmental science. I develop and apply novel approaches that push the boundaries of current technologies, enabling deeper molecular insights into challenging materials such as pyrolysis oils, recycling streams, polymers, and carbonaceous aerosols.
What aspect of your current research excites you most – and why is this an important moment for it?
The most exciting moment in my research is probably seeing the first spectrum from a novel setup. A few years ago, together with Spectroswiss SARL, Photonion GmbH, and Thermo Fisher Scientific, we heavily modified a high-resolution Orbitrap mass analyzer, incorporating vacuum laser-based photoionization for the first time.
I still remember the day very well: my PhD student and I were finishing the prototype in the lab, and after some final tuning of the optics, the first spectrum of the undoubtedly rather boring and simple analytical standard, toluene, appeared on the screen. Seeing a new technology come to life is what excites me most. Given my focus on novel technologies, particularly new ionization and hyphenation approaches in mass spectrometry, this sense of discovery is deeply embedded in my work.
Looking five to 10 years ahead, what emerging trend do you think will have the greatest impact on analytical science?
Seeing the tremendous impact of artificial intelligence on our daily lives, AI may be the only valid answer to this honestly very difficult question.
Five years ago, I probably would have said the democratization of ion mobility spectrometry in combination with high-resolution mass spectrometry. High-resolution and high-mass-accuracy MS itself has already become increasingly accessible through a growing variety of instrument platforms. Now we are seeing a similar development for multidimensional approaches, with technologies such as TIMS being combined with FT-ICR MS and Waters continuing to expand the combination of ion mobility and multireflecting time-of-flight MS. I therefore expect this trend to have much of its impact within the next few years.
AI, however, is moving at a completely different pace. The term is certainly overused in science, and what is called AI sometimes still refers to statistical models or algorithms that have existed for decades. Nevertheless, with increasingly capable AI systems on the horizon, I believe the way we perform science in general, and analytical science specifically, will fundamentally change.
AI is already helping researchers navigate the enormous volume of scientific literature, develop and structure ideas, write code, and interrogate increasingly complex datasets. More importantly, we are beginning to see systems that connect AI reasoning directly with experimental planning and execution. Autonomous laboratories can already operate in closed loops in which experimental results inform what experiment should be performed next. I would be lying if I said I was not also somewhat afraid of the capabilities AI has already demonstrated, but even more so of the incredible speed at which it is developing.
From the perspective of mass spectrometry, I see three major areas where AI could have the largest impact: technology development, including instrument design and construction; experimental design and instrument operation; and data analysis and interpretation. Much has already been written about the latter, so I would rather focus briefly on the first two.
For technology development, I can imagine AI becoming an increasingly powerful partner in exploring the enormous parameter space behind modern analytical instrumentation. Developing new mass spectrometric technology still relies heavily on experience, simulations, trial and error, and sometimes simply good scientific intuition. Having spent a significant part of my career modifying instruments, testing new ionization approaches, and trying configurations that were never intended by the manufacturer, I find the idea fascinating that AI could help us explore possibilities that we might never consider ourselves. It could accelerate the path from an initial idea to simulation, prototype, measurement, and finally a working analytical tool.
The second aspect, experimental design and instrument operation, is perhaps even more exciting. Today, we usually decide in advance what the instrument should do. We select the ionization conditions, acquisition parameters, fragmentation energies, or separation settings and then evaluate the results afterward. Some aspects of data-dependent acquisition already bring this idea of an intelligent, real-time response to life. I can imagine that this will be a big part of many future devices and methodologies.
An intelligent instrument could recognize something unusual during the experiment and adapt its next measurement accordingly. When analyzing something as complex as a pyrolysis oil or a carbonaceous aerosol, it could identify an interesting compositional region, change the ionization conditions, acquire additional fragmentation data, or explore it using another analytical dimension before we even ask it to.
Probably most academics would agree that if AI makes our lives easier by supporting grant writing, handling budgets, or prioritizing the next paper to read, it can be considered a general improvement to our work. However, intelligent automation that goes beyond adapting operational parameters and starts taking away the enjoyable moments of trial and error, or even the process of thinking about the next experimental step, is not what I would want.
Ideally, AI should give us more freedom to explore ideas, perform experiments that would otherwise be too complex or time-consuming, and perhaps even challenge us with observations we might have overlooked. Ultimately, no one knows exactly where AI will take analytical science. I hope, however, that AI will remain a tool that expands our curiosity rather than replacing it.
What is the biggest challenge facing analytical science today?
I think one of the biggest challenges in analytical science today is that our ability to generate data is increasingly exceeding our ability to understand it fully. Modern instruments provide extraordinary sensitivity, resolution, and multidimensional information, particularly for highly complex samples. A single PhD student can easily fill multiple hard drives with terabytes of data within a couple of weeks, and months can be spent applying all kinds of mass spectrometric techniques to a single sample. However, detecting tens of thousands of signals does not automatically mean that we understand the chemistry behind them.
This becomes even more challenging as analytical workflows increasingly combine different separation techniques, ionization methods, mass spectrometry, and advanced data-processing approaches. We need to ensure that this additional complexity really produces additional chemical knowledge and not simply larger datasets.
At the same time, I very much enjoy introducing new methodologies and demonstrating what is possible in proof-of-concept studies. For me, and probably for many colleagues, the challenge is therefore not only to push analytical technology further, but also to understand and clearly communicate where a new methodology fits among the solutions that are already available.
Especially with AI becoming more involved in data interpretation, we need to remain critical about what an algorithm tells us. A very convincing interpretation is still useless if the analytical evidence behind it is not sound.
Do you have any strong opinions with which the rest of the field tends to disagree?
Not that I know of, but one thing my team and colleagues at the University of Rostock often disagree with me about is the need for a tidy lab. Of course, academic research is rarely perfectly polished, but I still like to be able to find the actual mass spectrometer beneath parts that have not been used for a decade, components from last year, and an apparently endless collection of tools.
What decision, opportunity, or unexpected event has had the greatest influence on your career so far?
At the end of my doctoral studies in late 2017, I knew that I wanted to stay in research, so I started looking for promising postdoctoral opportunities. At the time, my research was heavily focused on petroleum chemistry and on how advanced mass spectrometric technologies could help us better utilize these resources while minimizing their environmental impact.
Through conferences such as PetroPhase and joint research projects, I was in contact with Simon Andersen, who was then working for Schlumberger Limited. Simon encouraged me to write a research proposal for a position at the Danish Hydrocarbon Research and Technology Centre. Unfortunately, the proposal was not funded. At the time, that rejection hit me quite hard, as I had already planned the next steps of my career in my head. Today, of course, I know that failure and rejection are simply part of science – and probably life in general.
With only a few months left on my contract at the University of Rostock, I started considering different options. In ultrahigh-resolution mass spectrometry, there are some obvious destinations, such as the National High Magnetic Field Laboratory (NHMFL). However, the decision that probably had the greatest influence on my career was instead to contact a unique duo in analytical science working on energy-related materials: Pierre Giusti at TotalEnergies and Carlos Afonso at the University of Rouen Normandy.
Everything moved very quickly. I still remember being in the hospital for a rather unpleasant wisdom-tooth surgery when their positive response arrived in my inbox. After a few messages back and forth, it turned out that there was a postdoctoral position available at exactly the right time at the University of Rouen Normandy. It would give me the opportunity to analyze carbonaceous aerosols using what was then a 12 T FT-ICR mass spectrometer. Within a few weeks, the formalities were settled and the practical planning of my stay began.
Luckily for me, that was only the beginning. In March 2018, I attended the European FT-MS meeting in Freising, near Munich, organized by Philippe Schmitt-Kopplin, whom I greatly appreciate. Pierre and Carlos were there as well, and after some good German beer at the conference dinner, they asked whether I would like to become involved in testing novel ion mobility prototypes together with Waters and Bruker Daltonics.
That conversation eventually led to several trips to the UK and the US, exploring some of the latest developments in combining ion mobility spectrometry with high-resolution mass spectrometry. Long story short, that postdoctoral year became an extraordinary experience and strongly shaped the researcher I am today. It was also during that time that I became involved with the International Joint Laboratory – Complex Matrices Molecular Characterization (iC2MC), which, somewhat amusingly, ultimately gave me the opportunity to work with Ryan Rodgers from the renowned NHMFL after all.
To this day, my group remains a satellite member of iC2MC, and we actively pursue joint research. Most recently, in March 2026, two of my students and I performed some rather unconventional experiments on the now-installed 18 T FT-ICR MS in Rouen. What started with a rejected proposal ultimately opened a much broader scientific path than I had originally imagined.
Who has changed the way you think about science – or life more broadly – and how?
Over the years, I have been privileged to work with many great minds, including my PhD advisor Ralf Zimmermann at the University of Rostock, Peter O’Connor and Mark Barrow at the University of Warwick, Alexander Makarov at Thermo Fisher Scientific, Yury Tsybin at Spectroswiss, Carlos Afonso at the University of Rouen Normandy, Ryan Rodgers and Martha Aguilera at the NHMFL, and Christopher Wooton at Bruker Daltonics, to name just a few.
However, the person I would truly call a mentor is Pierre Giusti from TotalEnergies and iC2MC. Pierre has a unique skill: even after a full day of meetings and discussions with him, usually ending with a rather impressive number of new tasks, I somehow come away highly motivated and excited about the path ahead.
Like many of us, I experienced a difficult period during COVID and the resulting social restrictions. At that time, I often ended up calling Pierre while going for a walk. I trust him completely and deeply appreciate his strong analytical thinking, but also the fact that he never loses sight of the people behind the science, each with our own individual stories. His strong values and the importance he places on people make him a real role model for me.
One important lesson he taught me came up again during a recent conversation about my application for a full professorship at another university: ultimately, it has to feel right. It is a very simple statement, but one that has stayed with me. And I can say that working with Pierre has always felt right. Thank you!
What interest or skill outside science has made you a better researcher?
I think two things – a skill and an interest – make me a better researcher every day. The first is networking and communication, which I believe are essential in modern research. I genuinely enjoy attending conferences and meetings, exchanging ideas, and exploring how other people think.
For me, networking should not be seen as a quick way to advance a career. It is about getting to know new people with their own unique perspectives, often looking at the same thing in a completely different way – and in my case, that thing is very often a spectrum. A recent example is the friendship I developed with Boniek Vaz from Brazil and Janne Jänis from Finland. Through our shared enthusiasm for high-resolution mass spectrometry, the three of us ended up visiting each other, exchanging scientific ideas and culture, and building a genuine friendship along the way.
The interest that probably influences me most outside science is watching my daughters grow and explore the world with the seemingly unlimited curiosity of children. In research, we are often occupied with budgets, grant writing, and simply keeping expensive laboratory equipment alive. From time to time, I think we should allow ourselves to be curious like children again and simply perform an experiment to see where it takes us.
Christopher Rüger is Research Group Leader for High-Resolution Mass Spectrometry at the University of Rostock, Germany.
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