The Bioinorganic and Analytical Chemistry group investigates transition-metal catalysis and advanced analytical methodologies at the interface of coordination chemistry, mechanistic understanding, and high-resolution chemical analysis. Our research centres on the development of bispidine-based ligand architectures, whose rigid backbone and precisely tunable substituents enable both the stabilisation of metals in unusual and highly reactive oxidation states and their functionalisation for diverse catalytic applications. A key focus lies on the activation and transformation of small molecules, where we explore structure–reactivity relationships and design catalytic platforms capable of mediating challenging bond-forming and bond-breaking processes.

In parallel, the group maintains a strong profile in analytical chemistry, with particular expertise in mass spectrometry. We cover the full spectrum of mass-spectrometric analysis, from targeted quantification to untargeted characterisation of complex mixtures, and routinely engage in method development for chemically demanding or highly heterogeneous systems. A central component of our analytical infrastructure is our Self-Driving Laboratory (SDL), which integrates automated sample preparation, high-throughput measurement-including mass spectrometry-and data processing. The SDL is being developed towards autonomous experimental workflows, enabling reproducible, data-driven chemical exploration with minimal human intervention.

In this context, we are expanding into liquid chromatography–based purification and the construction of digital twins of chromatographic processes, developing QSPR-driven retention-prediction models for biomolecules to accelerate and rationalise LC method development.

Team

From left to right: Dipl.Ing Michael Nusser, B.Sc. Nadjana Schneider, Dr. Katharina Bleher, M.Sc. Sebastian Putz,                                              M.Sc. Sebastian Castro, Dipl. Ing. Frank Kirschhöfer 

List of staff members

A current list of possible thesis topics can be found here.

Catalyst-assisted electrochemical conversion of CO2 to acetate in a particle electrode reactor under overpressure.

CO₂ is to be made usable as a resource, for example in the form of C₂ molecules as platform chemicals or as a nutrient basis for microbial conversions, in order to re-enter the value chain. To this end, a reactor is to be developed in which CO₂ dissolved under overpressure is converted to acetate at a particle electrode. The electrode surface area, which is greatly increased by the particles, is intended to raise the production rate and efficiency to a level suitable for application and allow scaling to an industrially usable scale. The selectivity of the copper particles used will be improved by adding a suitable catalyst in order to achieve higher acetate concentrations.

The focus of this project is on developing a suitable electrode and optimizing the catalyst. The necessary experiments will be carried out in specially designed and 3D-printed electrochemical cells on a mL scale. The test setup and parts of the analysis are to be automated and linked to enable parallel testing. The catalysts and catalyst variants to be investigated must be synthesized and characterized, and the resulting C-products analyzed.

Autonomous experimentation can dramatically accelerate the development of biotechnological processes by turning labor-intensive screening into a closed-loop workflow of plan → execute → analyze → learn. In this research topic, self-driving labs are developed and used to optimize biosorption and biocatalysis processes by systematically high-dimensional parameter design spaces and continuously updating the next experiments based on measured performance. The goal is to accelerate discovery and optimization by efficiently navigating the experimental design space with minimal experimental effort, while ensuring traceability and reproducibility through end-to-end automation, analytics, and ELN-connected metadata.

A key emphasis is user-friendliness and flexibility enabled by agentic AI workflows and a modern SDL web app that make autonomous labs easier to operate and—crucially—much faster to extend to new processes. AI agents support tasks such as translating experimental intent into device-ready protocols, assisting with data evaluation and reporting, interfacing with the ELN, and coordinating optimization campaigns across heterogeneous instruments via a unified interface. By combining standardized workflow descriptions, multi-vendor device integration, and intuitive user interaction, the approach aims to move SDLs beyond expert-only “island solutions” toward broadly deployable infrastructure for biotechnological R&D.

Figure 1: (a) Schematic of the self-driving lab platform for solid-phase extraction optimization (SDL 1). (b) Schematic of the self-driving lab platform for biochemical reaction optimization (SDL 2). (c) Photograph of SDL 1. (d) Photograph of SDL 2.

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Liquid chromatography (LC) is a high-resolution separation technique that plays a central role in downstream processing, particularly in the purification of pharmaceuticals and biopharmaceuticals. LC can be applied to a broad range of molecular classes, from oligosaccharides and peptides to oligonucleotides and monoclonal antibodies, enabling the isolation and purification of target compounds from complex mixtures. In most LC methods, separation arises from differences in how analytes partition between the mobile phase and the stationary phase, leading to distinct retention times. Depending on the chromatographic mode, the stationary phase may consist of functionalized porous particles or polymer-based resins, and analyte–stationary phase interactions can include hydrophobic, electrostatic, hydrogen-bonding, and other specific contributions.

Developing LC purification processes typically requires extensive experimental screening and optimization, which can be time-consuming and resource-intensive. Consequently, there is growing interest in predictive approaches that can estimate retention behavior in advance to accelerate process development. One widely used framework is quantitative structure–property relationships (QSPR), which combines molecular descriptors with statistical or machine-learning methods to relate chemical structure to experimentally observed properties such as retention time.

In this PhD project, we will extend this approach by developing QSPR models tailored to biomolecules of high pharmaceutical relevance, with a focus on peptides, oligonucleotides, and proteins. These models will aim to support retention prediction and method development in LC-based purification workflows.

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