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Item type:Publication, Access status: with files , Limits of Knallgas bacteria for autotrophic CO₂ conversion: physiology, H₂ kinetics and reactor constraintsAerobic hydrogen-oxidizing bacteria, commonly referred to as Knallgas bacteria, are chemoautotrophic microorganisms that oxidize molecular hydrogen (H₂) while fixing carbon dioxide (CO₂) into cellular material. Historically studied as model systems for lithoautotrophic metabolism, these organisms have regained attention due to their potential for renewable H₂ utilization, land-independent biomass production, and electrochemically coupled CO₂ conversion. Among them, Cupriavidus necator has emerged as a promising industrial platform, while thermophilic and high-affinity H₂-oxidizers such as Kyrpidia spormannii are increasingly explored in bioelectrochemical systems. Despite growing interest, substantial gaps remain between laboratory demonstrations and scalable industrial performance. This review analyzes the physiological characteristics of Knallgas bacteria, including growth kinetics and H₂ oxidation parameters, and evaluates their translation into gas fermentation and oxic microbial electrosynthesis systems. Particular emphasis is placed on key engineering limitations, including H₂ mass transfer, electron partitioning during aerobic metabolism, reactor safety constraints, and electrochemical surface scaling.Publicationtype: Review Article10 1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Shaping the electric field: overcoming geometric inhomogeneities in the electrochemical fabrication of mesoporous materialsIntegrating porous systems such as mesoporous silicon (pSi) into reliable macroscopic devices for sensing, photonics and energy storage requires stringent control over their structural homogeneity. While radial variations in layer thickness and porosity are frequently attributed to edge sealing effects or pinned gas, the macroscopic impact of the electrochemical cell’s internal geometry is largely overlooked. Using pSi as a model system, we demonstrate that the insulating cell body acts as a significant field-shaping component. Combining 2D axisymmetric finite element simulations of the current distribution with spatially resolved near-infrared (NIR) spectrometry, we investigate distortions caused by standard cell features. We reveal that the internal container widening step, commonly used to accommodate counter electrodes, strongly compresses the electric field and generates localized, ring-shaped zones of accelerated etching. Optimizing this step height reduced the radial thickness deviation of a pSi layer 6-fold to just 2%. Furthermore, predictive simulations demonstrate that internal obstacles, such as stirring rods, can be deliberately positioned to either prevent unwanted current shadows or actively manipulate the current distribution for precise three-dimensional structuring. Ultimately, mindful hardware design that prioritizes spatial current distribution is a critical and efficient strategy to maximize the yield of highly homogeneous porous media, directly enhancing their scalability and performance in functional nanomaterial applications.Publicationtype: Journal Article9 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Approximation of the Basset force in the Maxey-Riley-Gatignol equations via universal differential equations(Institute of Physics (IOP), 2026-10-27); ; ; The Maxey-Riley-Gatignol equations (MaRGE) model the motion of spherical inertial particles in a fluid. They contain the Basset force, an integral term which models history effects due to the formation of wakes and boundary layer effects. This causes the force that acts on a particle to depend on its past trajectory and complicates the numerical solution of MaRGE. Therefore, the Basset force is often neglected, despite substantial evidence that it has both quantitative and qualitative impact on the movement patterns of modelled particles. Using the concept of universal differential equations, we propose an approximation of the history term via neural networks which approximates MaRGE by a system of ordinary differential equations that can be solved with standard numerical solvers like Runge-Kutta methods. In particular, for long-term and high-resolution simulations of trajectories, where the number of required time-steps is large, the UDE approach can reduce time-to-solution.Publicationtype: Journal Article54 1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Powering mobile sensor nodes in fluidized beds: A non-invasive RF energy harvesting proof-of-concept(American Chemical Society (ACS), 2026-10-16); ; ; ; Real-time monitoring within industrial processing vessels is essential for advancing process analytical technology. Flow-following sensors represent an ideal approach for delivering continuous, in-line data from the process stream. However, deploying these mobile sensors is constrained by the physical barriers of sealed metallic vessels and harsh, battery-hostile environments. To overcome these limitations, a non-invasive, retrofittable wireless power transfer system is presented. The metallic vessel is structurally repurposed into a 5.23 GHz multi-mode microwave cavity resonator by coupling microwave energy through existing standard flanges. Electromagnetic simulations validate that this method generates continuous energy coverage throughout dynamic flow zones, demonstrated using a fluidized bed granulator as an application example. To harness this energy, an omnidirectional antenna and a compact RF-to-DC rectifier prototype were developed. By enabling the continuous operation of autonomous, battery-free mobile sensor nodes, this framework provides a practical, scalable foundation for high-density spatial analytics and data-driven industrial optimization.Publicationtype: Journal Article11 8 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Lagrangian μ-particle tracking velocimetry analysis of microscale flow over structured surfaces(American Chemical Society (ACS), 2026-10-08); ; ; ; This work aims to provide an experimental basis for the improvement of (bio)reactors by studying flow over micro- and nanostructured surfaces, with a focus on microscale mass transport toward and away from the surface. μ-Particle tracking velocimetry is employed in a microreactor to experimentally obtain Lagrangian trajectories of flow-following tracer particles. The method also resolves three-dimensional, time-dependent velocity fields. The derived mean velocity profiles agree well with the theory of flow over porous walls. Beyond averaged Eulerian velocities, the Lagrangian framework captures near-surface interactions. The Lagrangian analysis of different surfaces indicates that rectangular structured microgrooves increase the wall contact of the tracer particles, especially when the flow is oriented perpendicular to the microgrooves. Particularly, this approach enables resolution of trapping phenomena and localized recirculation dynamics. Nanostructuring by growing a carbon nanotube forest increases surface area and modifies local fluid dynamics, thereby enhancing contact with (bio)catalysts that are potentially functionalized on the nanotubes in future applications.Publicationtype: Journal Article39 12 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , A cellular automata framework to study the diffusion in single and binary solvent systems(Nature Publishing Group, 2026-10-06); ; ; In this work, we present a two-dimensional cellular automata (CA) model to study mass transfer by diffusion of molecules in single and binary solvent systems. The method builds on Vanag’s probabilistic cellular automata (PCA), where diffusion is simulated through stochastic exchange of molecules between neighboring cells. While the original PCA framework effectively models single component diffusion driven by concentration gradients, it could not account for variable diffusion coefficients. We address this limitation by generalizing the PCA approach to handle binary systems, enabling the simulation of diffusion in heterogeneous materials with varied transport properties. This extension broadens the applicability of CA-based methods for capturing complex diffusion dynamics.Publicationtype: Journal Article11 11 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Finite-time scalar retention and slow mixing tails in uncertain passive-scalar transportMixing of a passive scalar in a time-dependent flow is usually judged from the variance decay of a nominal flow. In many applications, though, stirring protocols and flow parameters are uncertain, and the more relevant question is whether the required level of homogenization is reached within a finite time. We study this reliability question for incompressible advection–diffusion with uncertain forcing parameters, using a time-periodic double-gyre flow as the main benchmark and a periodically stirred cavity as a secondary test. Direct numerical simulations show that delayed mixing is not a fluctuation around the mean variance decay curve. Instead, slow realizations retain coherent scalar remnants near weakly exchanging regions of the flow, which produces a persistent upper tail in the mixing time distribution. To identify these slow events without running a full simulation for every parameter sample, we build a parameter- and phase-dependent reduced transport operator by combining a proper orthogonal decomposition-based Koopman representation with a polynomial-chaos expansion in the uncertain parameters. From this operator, we compute a finite-time scalar-retention score based on the leading singular direction of the learned propagator in a scalar-energy metric. The diagnostic ranks parameter realizations by their tendency to retain scalar energy, identifies the realizations contributing to the upper tail of the mixing time distribution, and recovers the scalar structures responsible for delayed homogenization. Reliable mixing under uncertainty is therefore controlled not only by the mean variance decay, but also by the least-damped scalar directions of the uncertain transport operator, which suggests that designs targeting reliable mixing should act on these directions directly.Publicationtype: Journal Article8 16 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Parameter estimation for model-based sensing of magneto-mechanical resonatorsMagneto-mechanical resonators (MMRs) represent a recently proposed type of passive sensor that enables the estimation of its pose as well as sensing other parameters in its environment. The working principle of MMRs entails an excitation of the sensors by oscillating magnetic fields, followed by a readout process facilitated by inductive receiver coils. The sensing technology relies on real-time parameter estimation. This encompasses the solution of a nonlinear inverse problem, with the induced signals and a suitable forward model as inputs. The aim of this paper is twofold: first, to introduce a reference model and simplified models for the MMR dynamics and inductive readout, and second, to provide robust and real-time capable methods to estimate themodel parameters. The effectiveness of the presented methods is evaluated in terms of their real-time potential, precision, and accuracy. All presented methods demonstrate the capacity to estimate the measured signal, with the simplified methods reducing the corresponding parameter estimation time by up to two orders of magnitude at the expense of less than 4% deviation for large maximum deflection angles.Publicationtype: Journal Article21 9 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Tracking in-silico Lagrangian sensors in a lab-scale stirred tank reactor.(Elsevier, 2026-09-28); ; ; ; Lagrangian sensors have shown promise to improve operator awareness of conditions inside a chemical reactor but three-dimensional tracking remains a mostly unsolved challenge. We explore a setup where in-silico sensors, based on a recently proposed real-world design, are tracked using data from an accelerometer and magnetometer available from a built-in inertial measurement unit. Filtering algorithms, using a bespoke dynamical model, are used to process these readings into position estimates. We compare tracking performance of an extended Kalman filter, a particle filter and the unscented Kalman filter implemented in the pykalman library. Our numerical experiments track in-silico particles moving in an analytically given threedimensional vortex as well as in the experimentally measured flow-field of a lab-scale stirred tank reactor. Using the Maxey-Riley-Gatignol equations for the movement of inertial particles as ground-truth, we demonstrate that the extended Kalman filter and the particle filter reconstruct trajectories from noisy synthetic data with mean relative errors below 3%.Publicationtype: Journal Article51 26 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Grammar-guided symbolic regression as tool for model development of enzymatic reactions(Elsevier, 2026-09-23); ; ; The transition of the chemical industry toward renewable biocatalysis requires robust kinetic models to scale and optimize enzymatic processes. However, deriving these models typically relies on manual, trial-and-error fitting of predefined equations, while data-driven machine learning approaches often produce accurate but physically meaningless “black-box” models. Here, we present an adaptive kinetic model evaluation framework that automates the discovery of enzymatic rate laws directly from batch progress curves. By employing Grammar-Guided Symbolic Regression (GGSR) the algorithm autonomously constructs rate equations using only physically permissible kinetic building blocks. The framework’s reliability was first validated against 13 synthetic kinetic mechanisms, confirming structural and parametric identifiability. The pipeline was then applied to a real experimental case study of glucose-fructose isomerization using Streptomyces murinus isomerase. The algorithm successfully recovered a reversible kinetic equation featuring an apparent uncompetitive-type interaction term providing specific activities and Michaelis constants that align well with literature. This work demonstrates that GGSR can bridge the gap between data-driven automation and mechanistically interpretable biochemical engineering.Publicationtype: Journal Article15 11