Orchestration and Digital Twins of Lab Process
In laboratory processes, automation ensures precise and repeatable control of experimental procedures as well as seamless coordination between devices, while digital twins enhance this by virtually replicating system behavior.
This project aims to develop an integrated platform for user-friendly protocol orchestration and digital twin creation of an automated experimental setup, representing an important step towards CTC’s mission of an autonomous laboratory.
“We’re building the tools that will let chemistry run itself – so we can focus on what’s next.” – Dr. Samuel Vitor Saraiva
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Database for REE Solvent Extraction
Rare earth elements (REE) are a crucial part of our modern society and are found in a lot of electrical devices, like magnets and motors in cars. As Europe has very little deposits of these elements, they are defined as critical raw materials by the EU and recycling is essential.
This project is collecting data on extraction processes of REE and aims to create sustainable and efficient recycling procedures; a crucial part on CTC’s mission towards the recyclable car.
“Sustainable recycling procedures for rare earth elements should have already existed. Thanks to our project, the topic finally gets the attention it deserves.” – Dr. Thomas Kunze
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Computer-aided Design of Lignocellulose-based Surfactants
Many everyday products – from cleaners and toothpaste to lotions – rely on surfactants, molecules that help ingredients mix, spread, and clean effectively by reducing surface tension.
We use artificial intelligence to design surfactants made from renewable lignocellulosic feedstocks, such as wood. With advanced computational models, we create bio-based, biodegradable surfactants that are ready for a circular economy and meet industrial needs.
“We design molecules to create chemistry that respects planetary boundaries. Our goal is to produce wood-based surfactants that deliver top performance while remaining fully biodegradable. AI helps us generate the ideal molecular structures and accelerates the leap from computer models to real-world industrial impact.” – Dr. Laura König-Mattern
“Turning renewable feedstocks into bio-based surfactants shows how AI can bridge nature and innovation for a truly circular economy.” – Tim Tegtmeier
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Extension of Chemotion ELN for Polymers
The project develops an advanced Chemotion ELN module tailored for polymer and material sciences. It enables structured documentation, visualization, and analysis of complex polymer data, enhancing reproducibility, automation, and FAIR data management.
This digital infrastructure supports data-driven chemistry and contributes directly to CTC’s Moonshot goals on automation and the recyclable car.
“The chemistry of materials is an ocean of possibilities, and we are only at the beginning of exploring the seas.” – Surya Teja Pathakoti
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Towards Recyclable and Durable Molded Fiber Materials
As demand grows for sustainable materials that can replace plastics, our project is developing strong and recyclable molded fiber materials for structural applications.
By combining plant-based fibers with a fully recyclable polymer, we create durable, three-dimensional components that maintain strength and stability while remaining environmentally friendly. This innovation supports the move toward circular, sustainable materials in packaging and beyond.
“It’s exciting to shape materials that are strong enough for industry and gentle enough for the planet.” – Dr. Xue Zhang
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Database for Lignin Chemistry
Lignin is one of the most abundant biopolymers on Earth and a major by-product of the paper industry. It holds great potential for sustainable valorization through depolymerization into valuable chemicals.
The goal of this project is to build a comprehensive database of scientific literature on lignin chemistry, powered by a Natural Language Processing (NLP) pipeline to extract and organize knowledge — paving the way for data-driven innovation in green chemistry.
“We’re teaching machines to read chemistry – so we can unlock lignin’s potential faster than ever.” – Dr. Laura Lintis
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Predictive LCA Model for Analysing the Environmental Impact of Chemical Processes
The rising demand for sustainable and circular innovations in the chemical industry necessitates more effective methods for evaluating the environmental impact of new processes.
This project develops a predictive Life Cycle Assessment (LCA) model to estimate the environmental impacts of converting biomass into valuable products. By assessing these impacts early, the model helps identify more sustainable options and supports informed decision-making in transforming chemistry.
“Measuring impact – Predictive LCA model is a crucial component for the critical evaluation of the impacts of new products, processes, and technologies on our society and environment. It helps us make informed decisions and contribute to a more sustainable future for the chemical industry.” – Pooja Dwivedi
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