Abschiedsvorlesung von Herrn Prof. Matthias Kind und Antrittsvorlesung von Herrn Prof. Tim Zeiner;  Professoren für Thermische Verfahrenstechnik
 
Um eine Teilnahmebestätigung wird bis 24. November 2024  unter folgendem Link gebeten: https://nuudel.digitalcourage.de/dwfyvUtPPcPJ4cb1
 
 

Drug delivery systems and biosensor technologies are at the forefront of health innovation, transforming disease treatment, diagnostics, and monitoring. In this lecture, Dr. Kabay will share her research journey in biomedical technology development, which started with her initial focus on controlled drug delivery systems, safe medical implants, and advanced biosensors.
 
Building on this foundation, this lecture will further explore the Kabay research team’s endeavors to develop innovative detection strategies and biosensor recognition components to provide efficient, accessible, affordable, and patient-centered care. Accordingly, impedimetric sensing and molecularly imprinted polymer-based biosensors will be introduced as the most notable examples. Moreover, an overview of the team’s parallel research on bioactive-loaded nanoformulation synthesis for treating chronic inflammatory diseases, alongside robotic process automation and machine learning, will explain their ongoing efforts to improve the reproducibility of analytical assays and streamline the materials manufacturing process. Ultimately, insights into a prospective vision for integrating this interdisciplinary expertise to shape the future of healthcare solutions will be given.

Our laboratory investigates how cells within a population respond to external stimuli and coordinate their behavior. To achieve this, we employ cutting-edge single-cell analytical tools, including automated flow cytometry, microfluidics, and other innovative technologies. We've also developed a concept called cellular entropy to quantify the degree of diversification in cell populations.
Our research has shown that the switching cost, or the loss of growth fitness for cells that decide to switch, is a key driver of cell population diversification. Interestingly, we've observed that cellular systems with high switching costs exhibit a Fitness-Entropy (F-E) compensation mechanism. This process allows the cell population to cope with complex decisions by increasing cell-to-cell heterogeneity. However, this compensation mechanism takes time to establish. To address this, we designed a cell-machine interface called the Segregostat, which enables the stimulation of cell populations in a timing-compatible manner. This technology has allowed us to control gene expression in various cell populations, including bacteria and yeast. Recently, we've applied this approach to stabilize cell populations for continuous bioprocessing. Building on this research, we're also investigating the stabilization of microbial co-cultures and controlling more complex phenotypes, such as general stress response and protein secretion for controlled delivery.
Ultimately, we believe that the F-E compensation mechanism is at the heart of cell collective behavior, and our research aims to understand and harness this phenomenon to develop innovative biotechnological applications.

Acid catalysis is the workhorse in conventional petroleum refining and, at present, assumes a crucial role in circular chemistry. Skeletal rearrangement and cracking reactions, together with hetero atom removal, are at the basis of hydrocarbon stream quality upgrading. Streams from a fossil (VGO) as well as from a circular (pyrolysis oils) origin require adequate treatment prior to being sent for base chemicals and (sustainable aviation) fuel production. The accurate prediction of product yields and selectivities obtained in such kind of treatments presents a significant asset. The numerous species and elementary steps interconverting them in a multiphase environment are harnessed within the PR1ME software framework. With routines for feedstock reconstruction, intrinsic kinetics and multi-scale reactor modeling, PR1ME is a forefront tool to assess and steer commercial production data and operation.

Reactor design and optimization are crucial aspects of chemical and biochemical engineering. With the advent of additive manufacturing, advanced reactor geometries are now possible, offering improved operational efficiency and cost-effectiveness. However, due to this extra flexibility, optimizing over these designs is even more challenging. In this talk, we discuss work that integrates computational fluid dynamics (CFD) simulations with a multi-fidelity Bayesian optimization. We introduce an approach that not only recommends optimal reactor configurations and operating conditions but also determines fidelity (level of accuracy of the CFD simulator) levels based on statistical likelihoods and information content, optimizing accuracy and computational efficiency.
The methodology discussed focuses on plug-flow reactors but can be extended to various reactor types. By maximizing plug-flow performance, we identify crucial design characteristics and validate two novel geometries through 3D printing and experimental validation. Through this data-driven optimization of highly parameterized reactors, we aim to establish a framework for next-generation reactors, highlighting how machine learning and advanced manufacturing processes can revolutionize the performance and sustainability of future chemical processes.