The energy transition represents a systemic transformation of how we produce, transform, and use energy. It is driven by the urgent need to reduce greenhouse gas emissions, as well as by resource constraints and societal expectations.
We cannot, however, replace one energy system with another overnight. We are building a new system while continuing to operate the existing one, which means introducing new feedstocks and products into established industrial assets while maintaining efficiency, profitability, and, above all, safety.
For me, this makes the energy transition, at its core, a molecular challenge. We are moving from relatively well-defined fossil hydrocarbons to highly diverse and variable mixtures, including bio-oils, plastic pyrolysis oils, and e-fuels containing oxygen, nitrogen, sulfur, and many unknown compounds. If you cannot measure and understand these materials, you cannot scale them.
This challenge sits at the heart of my work as an R&D analytical chemist at TotalEnergies Research & Technology Gonfreville. I develop advanced methods for characterizing materials associated with emerging energy technologies and participate in international standardization committees, helping translate new analytical approaches into methods that can be used more widely by industry. The aim is to make these materials visible, understandable, and comparable.
Analytical science provides the clarity that enables industrial confidence, helping turn the challenges of the energy transition into practical and scalable solutions.
From service provider to strategic partner
Traditionally, analytical laboratories were often seen as service providers, delivering data upon request. When I started my career at TotalEnergies, the mission of the analytical laboratory was relatively straightforward. We worked with well-characterized petroleum streams, standardized fuels, and mature specifications. The principal analytical priorities were reliability, reproducibility, efficiency and debottlenecking – delivering accurate results quickly enough to support industrial operations.
The energy transition gradually changed that landscape. New feedstocks began to enter the system: first bio-based materials, then recycled products, followed by increasingly diverse and complex streams originating from circular and low-carbon pathways. Many of the analytical tools and assumptions that had served us well for decades were no longer sufficient. We found ourselves facing materials with unfamiliar compositions, new chemical functionalities, and levels of variability that challenged existing methodologies.
Rather than being driven by a single breakthrough or history-based assumptions, this transformation emerged progressively as R&D projects explored new energy pathways and began asking questions that had not been asked before. Each new feedstock and process required deeper characterization and, in many cases, entirely new analytical approaches.
As a result, the laboratory now plays several roles. It continues to deliver reliable measurements, but it also develops analytical solutions where none exist, contributes to R&D strategy as a scientific partner, and works with standardization committees to support the wider adoption of new methods. Today, the analytical laboratory is deeply integrated into TotalEnergies’ R&D ecosystem and play a proactive role in the understanding and in the orientation of solution. We work daily with process engineers, chemists, technology developers, business lines, and ultimately the customers who will use the products and technologies emerging from our research. In many ways, the laboratory acts as a crossroads where scientific questions, industrial challenges, and market expectations meet.
Our role connects many different parts of the organization. We support a wide range of projects, from early-stage research to industrial deployment, providing the data and insights that guide scientific exploration and business decisions. Analytical data do not simply describe a product. They help determine whether a new feedstock can be processed, whether a technology is mature enough to scale, and whether a product meets the performance and sustainability requirements expected by customers and regulators.
This changing role reflects TotalEnergies’ broader multi-energy strategy, based on two pillars: Oil & Gas, notably LNG, and electricity: the energy at the heart of the transition. The Company is developing low-cost, low-emission Oil & Gas projects while reducing emissions from its operated activities. In parallel, it is expanding an Integrated Power business combining renewables, flexible generation, storage, sales and trading. For the analytical laboratory, relevant areas also include sustainable aviation fuels and integrating circular and more diverse feedstocks into existing industrial assets. Looking further ahead, TotalEnergies is investing in low-carbon molecules solutions, circular carbon pathways, and low-carbon hydrogen solutions supported by renewable electricity.
The broad direction is similar throughout the industry, with major companies investing in similar solutions. Differences often lie in the speed of execution, the availability of suitable feedstocks, and the ability to integrate new solutions into existing infrastructure.
For TotalEnergies’ R&D teams, this strategy means working with a much wider spectrum of materials and compositions. The analytical laboratory therefore becomes a key enabler of the strategy. In practice, this means answering questions for which established analytical methods may not yet exist. The materials involved may contain oxygen, nitrogen, sulfur, and other heteroatoms that profoundly influence processability and product performance.
Understanding these materials requires advanced characterization techniques such as comprehensive two-dimensional gas chromatography (GC×GC), high-resolution mass spectrometry, and multidimensional analytical workflows. These approaches can reveal information that conventional methods simply cannot capture.
Over the past few years, we have developed advanced GC×GC methods to investigate the molecular complexity of challenging feedstocks, including pyrolysis oils, bio-based streams, and sustainable aviation fuel-related products. The resulting insights directly support process development and technology decisions.
One example is a predictive model that uses high-quality GC×GC data to estimate sustainable aviation fuel yields from renewable feedstocks. By linking molecular fingerprints with process performance, the model can accelerate feedstock screening and reduce the time needed to identify suitable sources.
The pace of innovation adds another challenge. R&D projects need answers rapidly, often long before standardized methods exist. We are therefore not only analyzing samples but also developing the tools needed to understand them. In many projects, method development and scientific investigation happen simultaneously.
Developing these methods is only part of the challenge. They must also be translated into approaches that can support industrial adoption. This requires connections between R&D, analytical science, intellectual property, and standardization. Companies must be able to turn scientific understanding into patented innovations, recognized analytical methods, and, ultimately, product specifications accepted by the market. In this context, analytical science functions as a strategic asset that helps create, protect, and scale innovation.
New Networks and Collaborations
The complexity of the energy transition makes collaboration essential. No single laboratory possesses all the expertise, technologies, and perspectives needed to tackle the challenges posed by emerging feedstocks and new energy pathways. Throughout my career, some of the most valuable advances have emerged from these interactions, where industrial needs meet academic creativity and technological innovation from instrument manufacturers, creating opportunities for mutual learning and discovery. The exchange is genuinely reciprocal. Through collaborations with academic partners, we gain access to fundamentals knowledge, new scientific perspectives and emerging methodologies that challenge established ways of thinking. For instance, academic work on molecular-level characterization and advanced data analysis has helped us develop a deeper understanding of highly complex streams starting from fossil to the latest biomass, plastics, and other alternative feedstocks. In return, academia benefits from access to real industrial samples and practical questions arising from large-scale deployment, helping ensure that research remains connected to societal and industrial needs.
We learn equally from instrument manufacturers. Their deep understanding of instrumentation often reveals possibilities that users may not have considered, while our industrial challenges help define future analytical requirements.
A good exemple of this learning ecosystem where academia, industry, and technology providers continuously advance together could be found iniC2MC (International Complex Matrices Molecular Characterization). Created in 2014, this joint laboratory brings together three analytical groups from the universities of Pau, Rouen, and Florida within a long list of equipment suppliers (LECO, Agilent, Shimadzu, Bruker, Sepsolve, and others) giving us early access to new developments and instrument technologies. It provides a bridge between academia and industry, creating an environment in which fundamental research, industrial challenges, and technological innovation can inform one another.
Researchers can rapidly test new ideas using real industrial samples, while industrial laboratories gain direct access to recent scientific developments and emerging analytical concepts. The collaborative model also allows instrument manufacturers to become involved at an early stage. This interaction gives us early access to new developments, allows us to inform future instrument capabilities, and helps align technological innovation with the needs of researchers and industry.
I also believe in creating broader scientific communities that connect specialists from different disciplines. This was one of the motivations behind the launch of the TAD (Transition Analytics for Decarbonization) Conference in Paris. The first edition addressed six major societal challenges linked to the energy transition, including CO₂ capture and utilization, battery technologies, sustainable fuels, circular carbon, and renewable energy systems.
The program centered on problems to solve. For each challenge, we brought together experts using chromatography, spectroscopy, electrochemistry, imaging, data science, and other analytical approaches. This encouraged discussion beyond methodological silos and demonstrated the value of combining techniques when addressing the complexity of the energy transition.
The response to the first edition confirmed the need for this type of forum. The next conference is now being prepared for Tokyo, Japan, in 2027. Taking the initiative to an international audience will bring together more scientists, industrial researchers, instrument manufacturers, and decision-makers facing similar challenges.
The future analytical laboratory
When I look to the future, I see the analytical laboratory taking a broader role in making sense of an increasingly complex energy landscape.
Over the next few years, new feedstocks, conversion pathways, and products will continue to emerge. Sustainable aviation fuels, recycled carbon streams, bio-based intermediates, and eventually e-fuels will coexist, creating a level of chemical diversity that would have been difficult to imagine just a decade ago. Extracting useful knowledge from analytical data quickly enough to support innovation will become a central challenge.
I believe artificial intelligence will be one of the most transformative developments ever experienced by analytical laboratories. The challenge is no longer generating data; modern instruments already produce vast amounts of information. The challenge is turning that information into knowledge quickly enough to support innovation. AI has the potential to become a powerful accelerator in this process, helping scientists identify hidden patterns, extract meaningful insights from increasingly complex datasets, and connect information that would otherwise remain fragmented across projects, disciplines, and organizations.
At TotalEnergies, broader investments in AI reflect a strong ambition to place knowledge at the center of innovation. AI will not replace scientific expertise; it will amplify it. While advanced instrumentation continues to expand our analytical capabilities, an equally important transformation is taking place in the background: rethinking how experimental data are captured, stored, and exploited. This AI-focused initiative is creating the digital foundation for a new generation of faster, smarter, and more predictive R&D. AI can help researchers learn faster, make more informed decisions, and focus their creativity on solving the most challenging scientific questions. AI could connect information from different disciplines, support decision-making, and help predict the performance of future feedstocks before they reach pilot scale.
The future energy system will draw on renewable electricity, sustainable fuels, circular carbon, hydrogen, and technologies that may not yet have been imagined. Analytical scientists have the opportunity to observe that future at the molecular scale before it becomes visible at an industrial level.
Every major energy breakthrough begins as a signal hidden in complexity. Our job is to find that signal, understand it, and help translate it into industrial impact. Success will depend on how quickly companies can turn data into understanding and understanding into action. Ultimately, you cannot scale what you do not understand.
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