Publications Repository - Helmholtz-Zentrum Dresden-Rossendorf

Magnonics is a research field that has gained an increasing interest in both the fundamental and applied sciences in recent years. This field aims to explore and functionalize collective spin excitations in magnetically ordered materials for modern information technologies, sensing applications and advanced computational schemes. Spin waves, also known as magnons, carry spin angular momenta that allow for the transmission, storage and processing of information without moving charges. In integrated circuits, magnons enable on-chip data processing at ultrahigh frequencies without the Joule heating, which currently limits clock frequencies in conventional data processors to a few GHz. Recent developments in the field indicate that functional magnonic building blocks for in-memory computation, neural networks and Ising machines are within reach. At the same time, the miniaturization of magnonic circuits advances continuously as the synergy of materials science, electrical engineering and nanotechnology allows for novel on-chip excitation and detection schemes. Such circuits can already enable magnon wavelengths of 50 nm at microwave frequencies in a 5G frequency band. Research into non-charge-based technologies is urgently needed in view of the rapid growth of machine learning and artificial intelligence applications, which consume substantial energy when implemented on conventional data processing units. In its first part, the 2024 Magnonics Roadmap provides an update on the recent developments and achievements in the field of nano-magnonics while defining its future avenues and challenges. In its second part, the Roadmap addresses the rapidly growing research endeavors on hybrid structures and magnonics-enabled quantum engineering. We anticipate that these directions will continue to attract researchers to the field and, in addition to showcasing intriguing science, will enable unprecedented functionalities that enhance the efficiency of alternative information technologies and computational schemes.

Antiferromagnetic materials have unique properties due to their alternating spin arrangements. Their compensated magnetic order, robust against external magnetic fields, prevents long-distance crosstalk from stray fields. Furthermore, antiferromagnets with combined parity and time-reversal symmetry enable electrical control and detection of ultrafast exchange-field enhanced spin manipulation up to THz frequencies. Here we report the experimental realization of a nonvolatile antiferromagnetic memory mimicking an artificial synapse, in which the reconfigurable synaptic weight is encoded in the ratio between reversed antiferromagnetic domains. The non-volatile memory is “written” by spin-orbit torque-driven antiferromagnetic domain wall motion and “read” by nonlinear magnetotransport. We show that the absence of long-range interacting stray magnetic fields leads to very reproducible electrical pulse-driven variations of the synaptic weights.

The present work reports a systematic study of the potential degradation of metals and dielectric thin films in different space environments. The mono- and bilayers selected are made of materials commonly used for the realization of optical components, such as reflective mirrors or building blocks of interferential filters. More than 400 samples were fabricated and irradiated with protons at different energies on ground-based facilities. The fluences were selected as a result of simulations of the doses delivered within a long-term space mission considering different orbits (Sun close, Jovian, and Geostationary orbits). In order to stress the samples at different depths and layer interfaces, experiments were carried out with a range of proton energies within 1 and 10 MeV values. An estimate of a safe maximum fluence has been provided for each type of sample at each energy. The damage mechanism, when present, has been investigated with different optical and structural techniques.

Time-dependent density functional theory (TDDFT) is a widely used method to investigate electron dynamics under various external perturbations such as laser fields. In this work, we present a novel approach to accelerate real time TDDFT based electron dynamics simulations using autoregressive neural operators as time-propagators for the electron density. By leveraging physics-informed constraints and high-resolution training data, our model achieves superior accuracy and computational speed compared to traditional numerical solvers. We demonstrate the effectiveness of our model on a class of one-dimensional diatomic molecules. This method has potential in enabling real-time, on-the-fly modeling of laser-irradiated molecules and materials with varying experimental parameters.

In today's era of digitalization, managing the lifecycle of research projects demands efficient navigation through a myriad of data sources and services. This presentation delves into the pivotal role of HELIPORT, a web browser-based guidance system, in streamlining research project lifecycle management. HELIPORT serves as a comprehensive platform, seamlessly connecting disparate services and systems to facilitate the smooth flow of digital data throughout the entire research process. By harnessing HELIPORT's capabilities, researchers can effectively track, organise, and share data and workflows with colleagues, thereby enhancing collaboration. The embedding of computational workflows to automate processes and provide comprehensible and reproducable workloads on HPC clusters is an essential part of this process. In the talk, we explore how HELIPORT is expanding the digital horizon and empowering researchers to push new boundaries in scientific exploration.

This repository entails the data and Pythoncode for the publication "Dynamics of Lagrangian Sensor Particles: The Effect of Non-Homogeneous Mass Distribution" in the journal "Processes".

In the following a brief introduction and guide based on the folders in the repository is laid out. More code specific instructions can be found in the respective codes.

01 --> The tracking always begins with the same 01_milti[...] folder in which the python code with OpenCV algorithm is located. For tracking the tracking to work certain directories are required in which the raw images are to be stored (separate from anything else) as well as a directory in which the results are to be save (not the same directory as the raw data).

After tracking is completed for all respective experiments and the results directories are adequately labelled and stored any of the other code files can be used for respective analyses. The order of folders beyond the first 01 directory has no relevance to the order of evaluation however can ease the understanding of evaluated data if followed.

02 --> Evaluation of amount of circulations and respective circulation time in experimental vat. (code can be extended to calculate the circulation time based on the various plains that are artificially set)

03 --> Code for the calculation of the amount of contacts with the vat floor. Code requires certain visual evaluations based on the LP trajectories, as the plain/barrier for the contact evaluation has to be manually set.

04 --> Contains two codes that can be applied to results data to combine individual results into larger more processable arrays within python

05 --> Contains the code to plot the trajectory of single experiments of Lagrangian particles based on their positional results and velocity at respective position, highlighting the trajectory over the experiment.

06 --> Condes to create 1D histograms based on the probability density distribution and velocity distributions in cumulative experiments.

07 --> Codes for plotting the 2D probability density distribution (2D Histograms) of Lagrangian Particles based on the cumulative experiments. Code provides values for the 2D grid, plotting is conducted in Origin Lab or similar graphing tools, graphing can also be conducted in python whereby the seaborn (matplotlib) library is suggested.

08 --> Contain the code for the dimensionless evaluation of the results based on the respective Stokes number approaches and weighted averages. 2D histograms are also vital to this evaluation, whereby the plotting is again conducted in Origin Lab as values are only calculated in code.

09 --> Directory does not contain any python codes but instead contains the respective Origin Lab files for the graphing, plotting and evaluation of results calculated via python is given. Respective tables, histograms and heat maps are hereby given to be used as templates if necessary.

The project used the Origin 2023 (64-bit) version, if no Origin license is available then Origin Lab provides a free Origin Viewer with which the projects can be opened and viewed. (https://www.originlab.com/viewer/)

The diameter distribution of bubbles in foam is one of the most important features in foam-based separation processes like foam fractionation and froth flotation. In this study the bubble size at different radial positions of pneumatically produced foams without coalescence and coarsening of bubbles is investigated in a cylindrical column by employing an invasive sampling probe. It is shown that pronounced differences of the local Sauter-mean diameter of the bubbles can appear in radial direction. Oftentimes a parabolic profile with the largest mean bubble diameter in the center of the column is found. The difference of the Sauter-mean diameter between wall- and center region is in the order of up to 60%. Experiments on foams produced with different spargers, gas flow rates and liquid filling levels reveal that the actual degree of the inhomogeniety depends on the specific bubble size distribution that is produced by the sparger, and becomes more pronounced if the range of bubble diameters in the foam increases. As an explanation for the observations, hydrodynamic interactions in the liquid phase, as well as the behavior of different sized bubbles close to the liquid/foam interface are proposed. The observed existence of local differences of the bubble diameters can have a strong influence the dynamic behavior, like liquid drainage, and measurement methods of pneumatic foams. In particular it can limit the applicability of surface-based bubble size measurements.

In this lecture I will show how neural networks can be used to solve differential equations. We will consider the basic example of the quantum harmonic oscillator. After reviewing some basic concepts, we will implement two machine learning methods to solve the time-dependent Schrödinger equation for the harmonic oscillator. First, we will consider a data-driven approach where a fully connected neural network is used to learn the solutions of the differential equation based on input labels. In the second approach, we will consider physics-informed neural networks. In contrast to the data-driven approach, the solution of the differential equation is not learned by mapping input features to outputs, but by minimizing a loss term related to the form of the differential equation. The lecture will be both formal and interactive using Jupyter notebooks.

In this lecture I will introduce the concept of neural networks. We will begin with a brief overview of the development of artificial neural networks. We will look at the basic perceptron model from a mathematical point of view and implement it to solve a simple classification problem. In the last part of the lecture, I will provide a gentle interactive introduction to deep learning using a simple toy problem about digital colors. We will learn how to build neural network pipelines and develop a qualitative understanding. The lecture will be both formal and interactive using Jupyter notebooks.

I will talk about two recent efforts to apply advanced machine learning methods to the electronic structure problem at the density functional theory (DFT) level. First, I will present a machine learning approach based on physics-informed neural networks and neural operators for inverting the Kohn-Sham equations for the exchange-correlation (XC) potential; neural networks provide a new way to perform DFT inversions at scale by learning the mapping from density to potential [1]. Second, I will present a very recent development in which we use neural operators to predict the electron dynamics of systems driven by a laser field. This approach complements conventional numerical solvers and has the potential to enable real-time, on-the-fly modeling of laser-irradiated molecules and materials with varying experimental parameters [2]. Both methods are illustrated on a conceptual level using one-dimensional models of diatomic molecules, but the approach can be readily applied to realistic systems in three dimensions.

[1] V. Martinetto, K. Shah, A. Cangi, A. Pribram-Jones, Mach. Learn.: Sci. Technol. 5, 015050 (2024).
[2] K. Shah, P. Stiller, N. Hoffmann, A. Cangi, arXiv:2210.12522 (2022).

I will present our recent progress in significantly scaling up density functional theory calculations with machine learning [1], for which we have developed the Materials Learning Algorithms (MALA) framework [2]. We have demonstrated the transferability of our machine learning model across phase boundaries, such as metals at their melting point [3] and electronic temperature [4]. In addition, our use of automated machine learning has led to a significant reduction in the computational resources required to identify optimal neural network architectures [5]. Most importantly, I will present our recent breakthrough in enabling fast neural-network driven electronic structure calculations for ultra-large systems unattainable by conventional density functional theory calculations [6]. I will mention in passing our other efforts in solving the Kohn-Sham equations of time-dependent density functional theory in terms of physics-informed neural networks [7], and in developing a robust framework for inverting the Kohn-Sham equations in terms of Fourier neural operators [8].

[1] L. Fiedler, K. Shah, M. Bussmann, A. Cangi, Phys. Rev. Materials, 6, 040301 (2022).
[2] A. Cangi, S. Rajamanickam, B. Brzoza, T. J. Callow, J. A. Ellis, O. Faruk, L. Fiedler, J. Fox, N. Hoffmann, K. D. Miller, D. Kotik, S. Kulkarni, N. Modine, P. Mohammed, V. Oles, G. A. Popoola, F. Pöschel, J. Romero, S. Schmerler, J. A. Stephens, H. Tahmasbi, A. P. Thompson, S. Verma, D. J. Vogel, Materials Learning Algorithms (MALA), doi.org/10.5281/zenodo.5557254, (2023).
[3] J. Ellis, L. Fiedler, G. Popoola, N. Modine, J. Stephens, A. Thompson, A. Cangi, S. Rajamanickam, Phys. Rev. B, 104, 035120 (2021).
[4] L. Fiedler, N. A. Modine, K. D. Miller, A. Cangi, Phys. Rev. B 108, 125146 (2023).
[5] L. Fiedler, N. Hoffmann, P. Mohammed, G. Popoola, T. Yovell, V. Oles, J. Austin Ellis, S. Rajamanickam, A. Cangi, Mach. Learn.: Sci. Technol., 3, 045008 (2022).
[6] L. Fiedler, N. Modine, S. Schmerler, D. Vogel, G. Popoola, A. Thompson, S. Rajamanickam, A. Cangi, npj. Comput. Mater., 9, 115 (2023).
[7] K. Shah, P. Stiller, N. Hoffmann, A. Cangi, Physics-Informed Neural Networks as Solvers for the Time-Dependent Schrödinger Equation, NeurIPS Workshop Machine Learning and the Physical Sciences, arXiv:2210.12522 (2022).
[8] V. Martinetto, K. Shah, A. Cangi, A. Pribram-Jones, Inverting the Kohn-Sham equations with physics-informed machine learning, arXiv:2312.15301 (2023).

The computational design of ionic materials such as ceramics relies on accurate enthalpies. While standard electronic structure approaches based on density functional theory can provide quantitatively accurate results for intermetallic compounds, they fail to yield a proper description of the thermodynamics of ionic materials such as oxides with mean absolute errors for formation enthalipies on the order of several hundred meV/atom. This hinders the materials design of for instance high-entropy ceramics or lower dimensional systems such as 2D oxides.
To address this pressing issue, we have recently developed the coordination corrected enthalpies (CCE) method based on the number of cation-anion bonds and the cation oxidation states. This correction scheme founded on the bonding topology decreases the prediction errors by almost an order of magnitude down to the room temperature thermal energy scale of ~25 meV/atom for oxides, halides, and nitrides. It is also capable of correcting the relative stability of crystal polymorphs. The efficient implementation of this scheme into the AFLOW framework for materials design in the form of the AFLOW-CCE module enables now the correction of enthalpies in large materials databases as well as for the construction of convex hull phase diagrams. These computational advances are thus an important enabler for the design of novel high-entropy ceramics.

Radiotherapy treatment-response of cancer patients can vary considerably, even in patients sharing the same diagnosis. Enhancing the degree of treatment personalization might offer a way towards improving curation rates. The recent advancements in the field of deep neural networks provide new directions for the non-invasive extraction of patient-individual biomarkers when applied on diagnostic imaging data.
Within this thesis, we explored the potential of image-based deep learning as an enabler for individualized therapy.
In a cohort of head and neck cancer patients, we first assessed the suitability of applying convolutional neural networks (CNNs) on pre-treatment computed tomography imaging data for the prediction of loco-regional tumor control in the presence of censored outcomes.
We further investigated whether the predictive performance can be improved through the adoption of multitask learning strategies that combine multiple outcome prediction models and a tumor segmentation task, both for CNNs and the recently emerged vision transformer-based network architectures.
Subsequently, we applied neural networks on multimodal and longitudinal imaging data collected during the course of radiotherapy and evaluated their potential to further improve outcome models.
Finally, in the context of proton-beam radiotherapy of primary brain tumor patients, we applied CNNs for the prediction of the linear energy transfer and examined the feasibility of this approach for estimating treatment-related side-effects considering a variable biological effectiveness of protons.

In order to improve energy and carbon dioxide efficiency of energy intensive industrial processes
the required design optimisations demand simulation tools for reliable predictions of dynamics in gas-
liquid flows. Appropriate modelling strategies always come with a trade-off between precision and
computational cost. In order to account for that, several individual numerical approaches are combined
in hybrid models, one of which is the MultiMorph model [1]. The latter comprises a Volume-of-Fluid
like treatment of large-scale interfaces united with an Euler-Euler model for small-scale dispersed
multiphase structures. The goal is to obtain consistent results across a wide range of spatial resolutions.
In that context, gas-liquid interfaces, being characterised by a shear boundary layer at each of both
sides, inevitably have to be depicted on computational grids with coarse resolution. This requires
adaptive modelling of the momentum exchange across the interface. Coste [2] proposes a model for
interfacial momentum exchange with the interface being smeared across exactly three grid cells by
definition. We propose an extension of the former approach and apply it to the MultiMorph model, in
which the smearing of the interface is not limited to a certain number of grid cells a-priori. The necessary
information from each side of the interface is transported across the interfacial region by means of the
interface transport algorithm of Meller et al. [3]. Results are assessed in several co-current horizontal
channel flows of Fabre et al. [4]. This contributes to numerical modelling with an enhanced predictive
power of gas-liquid interface dynamics in general and of interfacial momentum exchange on coarse
computational grids in particular.

In this presentation, we will discuss on our activities on magnetic soft robots. The focus will be on the possibility to actuate these objects using on-board magnetic coils and sense their state using on-board skin-conformal magnetic field sensors.

The design of novel materials for various scientific and technological purposes has in recent years benefitted from the introduction of data-driven methods. Here, this will be demonstrated for two exemplary materials classes.
While two-dimensional (2D) materials are traditionally derived from bulk layered compounds bonded by weak van der Waals (vdW) forces, the recent surprising experimental realization of non-vdW 2D compounds obtained from non-layered crystals [1] foreshadows a new direction in 2D systems research. We present several dozens of candidates of this novel materials class derived from applying data-driven research methodologies in conjunction with autonomous ab initio calculations [2,3]. Surface passivation of these systems can be used to control their magnetic state and eventually even to induce ferromagnetism [4]. The candidates thus exhibit a wide range of appealing electronic, optical, and magnetic properties making them an attractive platform for fundamental and applied nanoscience.
Also high-entropy materials have recently attracted significant interest due to their favorable mechanical, catalytic, and electronic properties. For the actual design of high-entropy materials, predictive synthesizability descriptors such as the disordered enthalpy-entropy descriptor (DEED) [5] are crucial prerequisites. We present an extensive validation of the predictive power of this approach and its prospective combination with enthalpy corrections for ionic materials [6]. These findings might thus pave the way towards an efficient computational design of high-entropy compounds for extreme conditions.

[1] A. Puthirath Balan et al., Nat. Nanotechnol. 13, 602 (2018).
[2] R. Friedrich et al., Nano Lett. 22, 989 (2022).
[3] T. Barnowsky et al., Adv. Electron. Mater. 9, 2201112 (2023).
[4] T. Barnowsky et al., Nano Lett. 24, 3874 (2024).
[5] S. Divilov et al., Nature 625, 66 (2024).
[6] R. Friedrich et al., npj Comput. Mater. 5, 59 (2019).

The design of novel materials for various scientific and technological applications has in recent years benefitted from the introduction of data-driven methods. Here, this will be demonstrated for two exemplary materials classes.

While two-dimensional (2D) materials are traditionally derived from bulk layered compounds bonded by weak van der Waals (vdW) forces, the recent surprising experimental realization of non-vdW 2D compounds obtained from non-layered crystals [1] foreshadows a new direction in 2D systems research. We present several dozens of candidates of this novel materials class derived from applying data-driven research methodologies in conjunction with autonomous ab initio calculations [2,3]. Surface passivation of these systems can be used to control their magnetic state and eventually even to induce ferromagnetism [4]. The candidates thus exhibit a wide range of appealing electronic, optical and magnetic properties making them an attractive platform for fundamental and applied nanoscience.

Also high entropy materials have recently attracted significant interest due to their favorable mechanical, catalytic, and electronic properties. High-entropy ceramics consist of an ordered anion sublattice of carbon, nitrogen or oxygen and a disordered cation sublattice maximizing configurational entropy by randomly occupying it by five or more cation species (transition metal elements). The reliable computational modelling of such systems can be realized by expanding it into a large ensemble of ordered structures [5]. For the actual realization of high-entropy materials, predictive synthesizability descriptors such as the entropy-forming ability (EFA) [6] and the disordered enthalpy-entropy descriptor (DEED) [7] are crucial prerequisites. We present here extensive results validating the predictive power of these approaches. These findings thus pave the way towards an efficient computational design of high-entropy compounds for extreme conditions.

[1] A. Puthirath Balan et al., Nat. Nanotechnol. 13, 602 (2018).
[2] R. Friedrich et al., Nano Lett. 22, 989 (2022).
[3] T. Barnowsky et al., Adv. Electron. Mater. 9, 2201112 (2023).
[4] T. Barnowsky et al., Nano Lett. 24, 3874 (2024).
[5] K. Yang et al., Chem. Mater. 28, 6484 (2016).
[6] P. Sarker et al., Nat. Commun. 9, 4980 (2018).
[7] S. Divilov et al., Nature 625, 66 (2024).

Mineral dissolution is an important process that occurs in both natural as well as anthropogenic processes. The kinetics of such dissolution processes are influenced not only by the characteristics of the solution but also by the characteristics of the minerals, such as crystal defects on the microscopic scale or macroscopic features such as the intersection of crystal planes to form edges and corners. Macroscopic features are known to increase the population of steps and kinks that may, in turn, affect the dissolution rate over time. Hence, this study presents a 3D empirical dissolution model aimed at examining the time-series evolution of macroscopic features together with the corresponding changes in the dissolution rate under far from equilibrium batch reactor conditions. The developed empirical model is based on the mineral geometry (surface topography and volume) derived from X-ray computed tomography (CT) measurements. The macroscopic features are identified using surface curvature which are then used to generate reactivity maps for dissolution model. As a study case, the dissolution of monomineralic galena (PbS) in ethaline and iodine as oxidizing agent is experimentally observed and then modelled. The model is then applied to seven particles of various shapes and sizes. The finding suggests that the surface reactivity increases over time as the particle shrinks and the macroscale steps and edges become dominant over the initial terraces. This implies that the persistent highly reactive surface sites defined by a particle’s geometry may play a dominant role in the overall particle dissolution in addition to the dissolution mechanisms typically studied on near atomic-flat surfaces. The model developed in this investigation offers the opportunity to be extended providing the possibility of simulating the dissolution of multi-mineral particles during batch dissolution experiments.

This contribution will demonstrate an interactive open-access GUI that aims to standardize a protocol to plan and to assess the feasibility of CT experiments. This standardization promotes quality assurance and improves comparability of laboratory source CT images obtained in different facilities.
The planning of a CT experiment consists of converging the preliminary knowledge about the sample with the technique requirements in order to answer specific scientific questions. This often involves combining the expertise of a “User” and a “CT expert”. The User is an expert in a specific field of science related to the sample and has formulated specific scientific questions or hypothesis that may be answered using CT. The CT expert is a person with advanced knowledge of CT, who does not necessarily have an in-depth knowledge about the specific field of science related to the experiment.

In 1889 the German poet and novelist Theodor Fontane wrote the popular literary ballad “Herr von Ribbeck auf Ribbeck im Havelland.” The Squire von Ribbeck is described as a gentle and generous person, who often gives away pears from his pear trees to children passing by and continued donating pears after his death. Now, 135 years later the rock called Ribbeck is giving us insight into processes that happened 4.5 billion years ago.
The meteorite Ribbeck (official find location: 52°37’15"N, 12°45’40"E) fell January 21, 2024, and has been classified as a brecciated aubrite. This meteoroid actually entered the Earth's atmosphere at 00:32:38 UTC over Brandenburg, west of Berlin, and the corresponding fireball was recorded by professional all sky and video cameras. More than 200 pieces (two proved by radionuclide analysis to belong to this fresh fall) were recovered totaling about 1.8 kg. Long-lived radionuclide and noble gas data are consistent with long cosmic ray exposure (55-62 Ma) and a preatmospheric radius of Ribbeck between 10 and 30 cm.
The heavily brecciated aubrite consists of major (76±3 vol%) coarse-grained FeO-free enstatite (En99.1Fs<0.04Wo0.9), with a significant abundance (15.0±2.5 vol%) of albitic plagioclase (Ab95.3 An2.0Or2.7), minor forsterite (5.5±1.5 vol%; Fo99.9) and 3.5±1.0 vol% of opaque phases (mainly sulfides and metals) with traces of nearly FeO-free diopside (En53.2Wo46.8) and K-feldspar (Ab4.6Or95.4). The rock has a shock degree of S3 (U-S3), and terrestrial weathering has affected metals and sulfides, resulting in the brownish appearance of rock pieces and the partial destruction of certain sulfides already within days after the fall.
The bulk chemical data confirm the feldspar-bearing aubritic composition. Ribbeck is closely related to the aubrite Bishopville. Ribbeck does not contain solar wind implanted gases and is a fragmental breccia. Concerning the Ti- and O-isotope compositions, the data are similar to those of other aubrites. They are also similar to E chondrites and fall close to the data point for the bulk silicate Earth (BSE).
Before the Ribbeck meteoroid entered Earth’s atmosphere, it was observed in space as asteroid 2024 BX1. The aphelion distance of 2024 BX1’s orbit lies in the innermost region of the asteroid belt, which is populated by the Hungaria family of minor planets characterized by their E/X-type taxonomy and considered as the likely source of aubrites. The spectral comparison of an average large-scale emission spectrum of Mercury converted into reflectance and of the Ribbeck meteorite spectrum does not show any meaningful similarities.

Using advanced in situ transmission electron microscopy, we study the lithiation and delithiation processes into graphene sheets and detect significant differences in the structural evolution of the system. Thin fcc lithium crystals with faceted shapes are formed between
graphene sheets during lithiation, but are transformed into irregular patches during delithiation. We find that defects such as vacancies in graphene and impurity atoms play the key role in these processes. Specifically, during intercalation the lithium crystals nucleate at vacancies in graphene, while upon delithiation the impurity oxygen atoms initially embedded at octahedral interstitial positions inside the lithium crystals agglomerate at the edges of the crystals, thus giving rise to the formation of amorphous lithium oxide patches, where lithium ions are trapped.

Femtosecond laser-induced ultrafast magnetization dynamics are all-optically probed for different remanent magnetic domain states of a [Co/Pt]22 multilayer sample, thus revealing the tunability of the direct transport of spin angular momentum across domain walls. A variety of different magnetic domain configurations (domain wall origami) at remanence achieved by applying different magnetic field histories are investigated by time-resolved magneto-optical Kerr effect magnetometry to probe the ultrafast magnetization dynamics. Depending on the underlying domain landscape, the spin-transport-driven magnetization dynamics show a transition from typical ultrafast demagnetization to being fully dominated by an anomalous transient magnetization enhancement (TME) via a state in which both TME and demagnetization coexist in the system. Thereby, the study reveals an extrinsic channel for the modulation of spin transport, which introduces a route for the development of magnetic spin-texture-driven ultrafast spintronic devices.

2D magnetic materials hold substantial promise in information storage and neuromorphic device applications. However, achieving a 2D material with high Curie temperature (T_C), environmental stability, and multi-level magnetic states remains a challenge. This is particularly relevant for spintronic devices, which require multi-level resistance states to enhance memory density and fulfil low power consumption and multi-functionality. Here, the synthesis of 2D non-layered triangular and hexagonal magnetite (Fe₃O₄) nanosheets are proposed with high T_C and environmental stability, and demonstrate that the ultrathin triangular nanosheets show broad antiphase boundaries (bAPBs) and sharp antiphase boundaries (sAPBs), which induce multiple spin precession modes and multi-level resistance. Conversely, the hexagonal nanosheets display slip bands with sAPBs associated with pinning effects, resulting in magnetic-field-driven spin texture reversal reminiscent of “0” and “1” switching signals. In support of the micromagnetic simulation, direct explanation is offer to the variation in multi-level resistance under a microwave field, which is ascribed to the multi-spin texture magnetization structure and the randomly distributed APBs within the material. These novel 2D magnetite nanosheets with unique spin textures and spin dynamics provide an exciting platform for constructing real multi-level storage devices catering to emerging information storage and neuromorphic computing requirements.

In dieser Bachelorarbeit wurde ein Testprogramm für die Validierung der Stromdichteberechnung in der Current-Deposition-Phase des Particle-in-Cell-Codes PIConGPU [13] entwickelt. Ausgehend von der (diskretisierten) Kontinuitätsgleichung und unter Nutzung des Prinzips von Cloud-Shapes nach Hockney sowie Esirkepovs Methode und dem daraus hergeleiteten Konstrukt des Current-Deposition-Vektors wird eine rekursive Methode zur Berechnung der Stromdichte eines geladenen Teilchens in einem Gitter konstruiert. Diese berechnete Stromdichte wird in einem Vergleichsprogramm genutzt, um sie bei gleicher Teilchenbewegung mit dem Ergebnis von PIConGPU zu vergleichen. Anhand der relativen Abweichung des simulierten Ergebnisses der Stromdichte verifiziert oder falsifiziert das Vergleichsprogramm die Current-Deposition von PIConGPU. Damit eine standardisierte Verwendung des Tests zu ermöglicht wird, wird er durch zwei Bash-Skripte automatisch ausgeführt, sobald eine Änderung am Code von PIConGPU vorgenommen wurde. Dadurch können Fehler frühzeitig erkannt werden und ein möglichst einfacher und schneller Arbeitsfluss wird gewährleistet.

Plasma acceleration processes have garnered extensive research interest in recent years due to the versatile applications of high-energy X-rays, including medical diagnostics and treatment, semiconductor manufacturing, and the hardening of material surfaces. However, simulating these processes demands a complex workflow and considerable computational time, rendering in-situ analysis impractical. To mitigate these challenges, simulation-based inference and data-driven methods for one-step inversion can be utilized to recover the matching phase-space representation and elucidate the underlying physics.

This work presents the adaptation of a surrogate model specifically for plasma physics processes, such as beam transport in a Free Electron Laser (FEL). A previously proposed model, which uses a mixture of normalizing flows to learn multi-modal distributions, is systematically investigated. Conditionality as a means of surrogate modeling in plasma physics is investigated and trained on simulation results of plasma physics processes.

Initial experiments involved using various custom test distributions to derive key insights into the model’s operation. Conditionality was incorporated into the model by utilizing the single-view reconstruction provision appropriately, allowing it to handle a wider range of input conditions. This conditioning has been introduced in two forms, scalar conditioning, and one-hot vector conditioning, and the effectiveness of each method is studied. This redefined architecture was rigorously tested on various test distributions with differing characteristics to validate its performance. The reconstruction quality was then compared to point clouds generated by single normalizing flow-based models with similar network sizes. Additionally, a significant reduction in trainable parameters was achieved, making the model more computationally feasible. Custom training protocols were introduced to further reduce overall training time.

The enhanced architecture was subsequently applied to simulation data from a free electron laser. A comprehensive analysis of the results demonstrated the surrogate model’s capability to accurately capture the complex dynamics of plasma acceleration processes. This work highlights the potential of advanced surrogate models in reducing computational demands and providing deeper insights into plasma physics, paving the way for more efficient and practical applications in high-energy X-ray technologies.

Es wird der Cowan-Cole-Kärkkäinen (CCK) Maxwell Solver in PIConGPU implementiert und validiert. Der CCK Solver hat gegenüber dem standard Yee Solver den Vorteil, dass er entlang der Achse mit dem geringsten Gitterabstand dispersionsfrei ist. Dadurch wird beispielsweise bei Laser Wakefield Acceleration Simulationen keine numerische Cherenkov Strahlung erzeugt. Dabei wird die in der Simulation beobachtbare Phasengeschwindigkeit einer Welle mit den theoretischen Werten aus den numerischen Dispersionsrelationen verglichen.

Change detection (CD) from remote sensing (RS) images using deep learning has been widely investigated in the literature. It is typically regarded as a pixel-wise labeling task that aims to classify each pixel as changed or unchanged. Although per-pixel classification networks in encoder-decoder structures have shown dominance, they still suffer from imprecise boundaries and incomplete object delineation at various scenes. For high-resolution RS images, partly or totally changed objects are more worthy of attention rather than a single pixel. Therefore, we revisit the CD task from the mask prediction and classification perspective and propose MaskCD to detect changed areas by adaptively generating categorized masks from input image pairs. Specifically, it utilizes a cross-level change representation perceiver (CLCRP) to learn multiscale change-aware representations and capture spatiotemporal relations from encoded features by exploiting deformable multihead self-attention (DeformMHSA). Subsequently, a masked cross-attention-based detection transformers (MCA-DETR) decoder is developed to accurately locate and identify changed objects based on masked cross-attention and self-attention mechanisms. It reconstructs the desired changed objects by decoding the pixel-wise representations into learnable mask proposals and making final predictions from these candidates. Experimental results on five benchmark datasets demonstrate the proposed approach outperforms other state-of-the-art models. Codes and pretrained models are available online (https://github.com/EricYu97/MaskCD).

Positron Profilometry . Can we overcome expensive facilities with new concepts?

I will discuss possible applications of RFQ-type 8Radio-Frequency Quadrupole) particle accelerators for efficient positron collection, bunching and post-acceleration im combination with radio-isotope based positron sources.

A transverse deflecting cavity is being developed for the electron linac ELBE to separate the bunches into two or more beamlines so that multiple user experiments can be carried out simultaneously. A normal conducting double quarter-wave cavity has been designed to deliver a transverse kick of 300 kV when driven by an 800 W solid-state amplifier at 273 MHz. The main challenges in fabrication were machining the complex cavity parts with high precision, pre-tuning the cavity frequency, and the final vacuum brazing within the tolerances, which are described in this paper. The reason for a low intrinsic quality factor measured during the low power test was investigated, and suitable steps were taken to improve the quality factor. The cavity field profiles obtained from the bead-pull measurement matched the simulation results. Further, the cavity was driven up to 1 kW using a modified pick-up antenna, and eventually, vacuum conditioning of the cavity was accomplished. The cavity fulfils the design requirements and is ready for beam tests.

Ute₂ is a spin-triplet superconductor candidate for which high quality samples with long mean free paths have recently become available, enabling quantum oscillation measurements to probe its Fermi surface and effective carrier masses. It has recently been reported that UTe₂ possesses a 3D Fermi surface component [Phys. Rev. Lett. 131, 036501 (2023)]. The distinction between 2D and 3D Fermi surface sections in triplet superconductors can have important implications regarding the topological properties of the superconductivity. Here we report the observation of oscillatory components in the magnetoconductance of UTe₂ at high magnetic fields.We find that these oscillations are well described by quantum interference between quasiparticles traversing semiclassical trajectories spanning magnetic breakdown networks. Our observations are consistent with a quasi-2D model of this material’s Fermi surface based on prior dHvA-effect measurements. Our results strongly indicate that UTe₂—which exhibits a multitude of complex physical phenomena—possesses a remarkably simple Fermi surface consisting exclusively of two quasi-2D cylindrical sections.

The linear electron accelerator, ELBE (Electron Linac for beams with high Brilliance
and low Emittance) at Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany,
is a versatile machine that drives six distinct secondary particle and radiation sources
used in a wide range of experiments related to health, matter, transmutation, and
accelerator development. The accelerator can efficiently handle 1 mA beam current
at a 13 MHz bunch repetition rate in continuous-wave mode with a maximum beam
energy of 40 MeV. Currently, it is not possible to simultaneously operate more
than one ELBE secondary source. In this work, a suitable beam separator device
for ELBE was developed to overcome the limitation of single beamline operation.
The developed kicker can distribute the bunches from the existing single beam into
two or more beamlines, which will enable the simultaneous operation of multiple
downstream secondary sources, significantly enhancing the accelerator’s capabilities.
The state-of-the-art transverse deflecting structures suitable for beam separation were
reviewed. Subsequently, pulsed magnet, stripline kicker, and radio-frequency (RF)
cavity designs were adapted for the current requirements, and RF cavities were found
suitable. Furthermore, the cavity operating frequency was set to 273 MHz, reducing
both the differential kick voltage error and projected emittance growth and providing
a better field homogeneity. The cavity can be easily integrated into the ELBE’s
existing low-level RF control system. Six deflecting cavity designs were shortlisted,
and the cavity geometries were scaled and adapted to match the requirements. Then,
a cavity design was selected based on lower power loss, peak electric field and surface
power loss density, as well as better field homogeneity.
Subsequent to the cavity design, the cavity components were adapted from the
existing designs. Next, beam loading and multipacting in the cavity were analyzed,
and the effect of higher-order modes on the cavity was studied. A multiphysics
analysis was carried out to aid in the engineering design of the cavity. Thereafter,
the copper cavity parts were machined, and the cavity frequency was pre-tuned
before the final vacuum brazing was performed.
Finally, RF measurements were performed to validate the simulation. A thorough
investigation was carried out to determine the cause of the low intrinsic quality factor of the cavity. Consequently, the quality factor was improved by eliminating the RF
filter present at the vacuum port. A bead-pull measurement setup was built, and
the measured field profiles matched the simulation results. Further, the cavity was
driven up to 1 kW using the modified pick-up antenna, and eventually, the vacuum
conditioning of the cavity was accomplished. The cavity’s performance meets the
design requirements and is ready to be installed in the beamline for further testing.

In the present study, organic acids - oxalic acid, citric acid, tartaric acid and siderophore Desferrioxamine B were evaluated for their efficiencies to selectively recover Cu from its mold production waste. XRD analysis showed that copper mold production waste mainly consisted of Fe and Cu. The complete dissolution of this waste in aqua regia and subsequent analysis via ICP-MS revealed metal contents of 355.3 mg/g Fe and 293.9 mg/g Cu. Among all the organic acids, citric acid had the highest leaching efficiency (58.5 %) for Fe while leaching <1 % of Cu. Whereas, the leaching of Cu and Fe was poor in oxalic acid medium and Cu leaching was also negligible in tartaric acid medium. Only Fe showed 11.2 % leaching efficiency at 2 mol/L tartaric acid. The step-by-step leaching of production waste with citric acid lead to 100 % leaching of Fe while leaving 93.1 % of Cu with a yield of >99 % in the solid residue in the 4th step. Further, the siderophore Desferrioxamine B could effectively leach Fe (91.2 %) while 21.4 % leaching of Cu in 30 days. The presence of Fe impedes the leaching of Cu from the waste as demonstrated by leaching and DFT calculations due to higher stability of Fe-citrate and Fe-desferrioxamine B complex compared to Cu-organic complexes. This recycling technique described herein is simple, reliable and environmentally friendly for recovery of Cu from copper mold production waste.

The first Sino-German workshop on "Deep-sea mining of massive sulfides: balancing impacts
on biodiversity and ecosystem, technological challenges and law of the sea" took place from
September 17 to September 23, 2023 in Changsha, Hunan Province, China. Four themes were
covered by 20 impulse talks, (1) seabed resources and mineralization systems, marine geology
and geochemistry, (2) microbiology and marine ecology, (3) deep-sea mining technology,
mineral processing and extractive metallurgy, and (4) law of the sea and international law
applicable to the marine environment in Areas Beyond National Jurisdiction, respectively.
In round table discussions, the interdisciplinary understanding deepened regarding (i) the
distribution of submarine massive sulfides (SMS), their formation mechanisms and the
abundance of SMS resources, (ii) the biodiversity linked to SMS and hydrothermal vents and
the environmental protection requirements, (iii) the challenges of mining technology and
processing of SMS, as well as (iv) the legal framework, the regulatory challenges, the
international environmental liability regime and due diligence obligations for commercial
seabed mining.
The participants covered all four themes and are affiliated with various universities, research
institutions, and governmental geoscientific authorities from China (seven) and Germany (six).
During the workshop, a number of recommendations to ISA were defined regarding
environmental, methodological, technical, and legal issues.

Siderophores are a diverse group of small of iron-chelating compounds which are secreted by a plethora of bacteria and fungi. In nature, their purpose is the sequestration of iron. However, due to their chemical characteristrics they are able to bind various other metals as well, making them promising compounds for the utilization in future green recycling technologies.
This work aims to find siderophores, that selectively complex the critical elements indium and germanium. As there are more than 500 different siderophores reported to this day, exhaustive experimental evaluation is highly impractical, though. Therefore, density functional theory (DFT) was utilized to model the complexation reactions and as a result estimate the affinities of the respective siderophores towards the metals of interest. With this in silico approach, siderophores that exhibited favourable binding energies were found and evaluated experimentally in order to verify the results obtained by theoretical means.
Proofing the suitability of siderophores for the selective recovery of indium and germanium from low-concentrated sources would pose as a first step in the creation of future applications of the compounds in a variety of bio-based recycling technologies, as they could aid to secure the supply of a multitude of strategic metals.

In KONA 2022, the fundamentals of two- and multidimensional particle size distributions were introduced. The next question in the field of two- and multidimensional distributions addresses their application to describe a particle process, e.g., agglomeration or separation. A multidimensional separation can be seen as retrieving only particles with a specific set of properties from a multidimensionally distributed system, e.g., retrieving only small particles (below a certain threshold in size) with a compact spherical shape (above a certain threshold in sphericity). The multidimensional separation allows the generation of functional particle systems with specific properties, e.g., semiconducting, optical, or electronic properties, which are required for high-technology applications. Starting from so-called particle-discrete information, i.e., an information vector for each particle containing its compositional, geometrical, and physical properties, it is possible to describe a multidimensional separation in full detail based on various properties. Each particle can be evaluated according to different separation properties, e.g., size, shape, and material composition. With this database, it is possible to define and work with separation functions to describe the multidimensional separation and quantify the separation results. For example, in the two-dimensional case, the median cut size becomes a median cut line, where the probability for a particle to belong to the concentrate is 0.5. Some case studies and examples show different approaches and possibilities to achieve a multidimensional separation in one or several connected process steps.

Understanding laser-solid interactions is important for the development of laser-driven particle and photon sources, e.g., tumor therapy, astrophysics, and fusion. Currently, these interactions can only be modeled by simulations that need to be verified experimentally. Consequently, pump-probe experiments were conducted to examine the laser-plasma interaction that occurs when a high intensity laser hits a solid target. Since we aim for a femtosecond temporal and nanometer spatial resolution at European XFEL, we employ Small-Angle X-ray Scattering (SAXS) and Phase Contrast Imaging (PCI) that can each be approximated by an analytical propagator. In our reconstruction of the target, we employ gradient descent (GD) to iteratively minimize the error between experimental and synthetic patterns propagated from proposed target structures. By implementing the propagator in PyTorch, we leverage the automatic differentiation and GPU acceleration for the GD fit and at the same time obtain a differentiable physically-based loss function for unsupervised training of inversion or surrogate models. For a classical fit, we sample many different initial values for parameters, such as target asymmetry, to find the global minimum, leveraging batch-parallelism. A data-driven model to predict initial conditions close to actual minima can be trained in an unsupervised manner using our pipeline.

Accurate estimation of contaminant transport in cementitious material using numerical tools plays a key role in the risk assessments of nuclear waste disposal. At the pore scale, the increase of microbial activity, such as microbially induced calcite precipitation on cementitious material, causes changes in solid surface topography, pore network geometry, and pore water chemistry, which affect contaminant transport at the core scale and beyond. Consequently, a meaningful estimation of contaminant migration in the subsurface requires a pore-scale investigation of the influence of microbial activity on transport processes. In this study, a pore-scale reactive transport model is presented to simulate the physicochemical processes resulting from microbially induced calcite precipitation on a cement surface. Numerical investigations focus on modeling the reactive transport in a two-dimensional flow-through cell. The model results are validated by experimental data showing an increase in pH and a decrease in calcium concentration due to microbially induced calcite precipitation. Our results show heterogeneous calcite precipitation under transport-limited conditions and homogeneous calcite precipitation under reaction-limited conditions, resulting in non-uniform and uniform changes in the material surface topography. Moreover, power spectral density analysis of the surface data demonstrates that microbially induced calcite precipitation affects the surface topography via both general changes over the entire frequency and local modifications in the high-frequency region. The sensitivity studies provide a comprehensive understanding of the evolution of surface topography due to the microbially induced calcite precipitation at the pore scale, thus contributing to an improved predictability of contaminant transport at the core scale and beyond.

Competing magnetic interactions, frustration-driven quantum fluctuations, and spin correlations offer an ideal route for the experimental realization of emergent quantum phenomena with exotic quasiparticle excitations in three-dimensional frustrated magnets. In this context, trillium lattice, wherein magnetic ions decorate a three-dimensional chiral network of corner-shared equilateral triangular motifs, provides a viable ground. Herein, we present the crystal structure, dc and ac magnetic susceptibilities, specific heat, electron spin-resonance (ESR), muon spin-relaxation (μSR) results on the polycrystalline samples of K₂CrTi(PO₄)₃ wherein the Cr³⁺ ions form a two-coupled trillium lattice. The Curie-Weiss fit of the magnetic susceptibility data above 100 K yields a Curie-Weiss temperature θ_CW = −23 K, which indicates the presence of dominant antiferromagnetic interactions between S = 3/2 moments of Cr³⁺ ions. For temperatures below 40 K, the Curie-Weiss temperature is reduced to θCW = −3.5 K, indicative of the appearance of subdominant ferromagnetic interactions. The specific heat measurements reveal the occurrence of two consecutive phase transitions, at temperatures T_L = 4.3 K and T_H = 8 K, corresponding to two different magnetic phases. Additionally, it unveils the existence of short-range spin correlations above the ordering temperature T_H. The power-law behavior of ESR linewidth suggests the persistence of short-range spin correlations over a relatively wide critical region (T – T_H)/T_H > 0.25 in agreement with the specific heat results. The μSR results provide concrete evidence of two different phases corresponding to two transitions, coupled with the critical slowing down of spin fluctuations above T_L and persistent spin dynamics below T_L, consistent with the thermodynamic results. Moreover, the μSR results reveal the coexistence of static and dynamic local magnetic fields below T_L, signifying the presence of complex magnetic phases owing to the entwining of spin correlations and competing magnetic interactions in this three-dimensional frustrated magnet.

We have, in-situ, prepared and measured the temperature dependence of thermopower S(T) and resistance R(T) of Bi₂Te₃ topological insulator (TI) thin films in the amorphous and crystalline phase. Samples were prepared by sequential flash-evaporation at liquid ⁴He temperature. The S(T) in the amorphous phase is negative and much larger compared to other known amorphous materials, while in the crystalline phase it is also negative and behaves linearly with the temperature. The resistivity ρ(T) in the amorphous phase shows a semiconducting like behavior that changes to a linear metallic behavior after crystallization. S(T) an ρ(T) results in the crystalline phase are in good agreement with results obtained both in bulk and thin films reported in the literature. Linear behavior of the ρ(T) for T >15 K indicates the typical metallic contribution from the surface states as observed in other TI novel materials. The low temperature conductivity T <10 K exhibits logarithmic temperature dependent positive slope κ ≈ 0.21, indicating the dominance of electron-electron interaction (EEI) over the quantum interference effect, with a clear two dimensional nature of the contribution. Raman spectroscopy showed that the sample has crystallized in the trigonal R3m space group. Energy-dispersive x-ray spectroscopy reveales high homogeneity in the concentration and no magnetic impurities introduced during preparation or growth.

Structure and magnetic properties of layered GdMn₂(Ge_1-xSix)₂ (0 ≤ x ≤ 1) compounds were studied. All the compounds crystallize in the tetragonal ThCr₂Si₂-type structure. It was shown by magnetization measurements at low temperature on quasi-single crystals that, with increasing Si concentration, the easy magnetization direction reorients from the c-axis to the basal plane. The spin reorientation occurs via an angular phase. A model of three magnetic sublattices coupled by negative intersublattice exchange interactions was used to describe the field dependences of the magnetization. For GdMn₂Ge₂ and GdMn₂(Ge_0.9Si_0.1)₂ in the fields applied along the c-axis, seven different magnetic structures were predicted, including two angular structures considered for the first time. The model explains formation of angular magnetic structures in zero field in GdMn₂(Ge_1-xSi_x)₂ system by taking into account magnetic anisotropy of Mn sublattices with a positive anisotropy constant K₁ and negative K₂.

Magnetic refrigeration (MR) based on the magnetocaloric effect (MCE) has been recognized as an environmentally benign and energy-efficient cooling technology. Exploring suitable magnetocaloric materials is a crucial prerequisite for practical MR applications. We have herein provided a systematic investigation of the crystal structure, microstructure, electronic structure, magnetic phase transition, critical behavior, and MCE of the GdCoC compound featuring excellent cryogenic magnetocaloric performance by means of experimental determination and theoretical calculation. The GdCoC compound is crystallized in a simple layered tetragonal crystal structure with a P4₂/mmc space group and undergoes two successive ferromagnetic (FM) transitions along with a low-temperature weak antiferromagnetic (AFM) transition under low magnetic fields. Density functional theory calculations confirms the FM coupling of the Gd and Co intra-sublattice interactions, whereas AFM coupling for their inter-sublattice interaction. The magnetic transitions are merged in to one under high magnetic fields which has been confirmed to be second-order type and its critical behavior can be understood in the framework of tricritical mean-field model, whereas the low-temperature weak AFM transition is belonging to the first-order type. The excellent magnetocaloric performance of the GdCoC compound was identified by the parameters of magnetic entropy change, adiabatic temperature change, temperature-averaged entropy change, relative cooling power, and refrigerant capacity, which are superior to most of the well-known magnetocaloric materials with similar working temperatures, making it attractive for practical cryogenic MR applications.

Water electrolysis offers a way to produce hydrogen from renewable electrical energy.
However, the details of gas evolution have a major impact on the energy efficiency of the process,
as gas bubbles growing at the electrodes or floating in the electrolyte cause overvoltages and losses.
It is therefore desirable to improve our understanding of gas evolution in order to further improve
electrolysis processes.
Gas evolution is influenced by a number of aspects, including electrolyte supersaturation and constitution,
nucleation and wetting at the electrode, electrolyte flow, interfacial flow and capillary effects,
coalescence with neighboring gas bubbles as well as temperature and electric fields.
The presentation will summarize the knowledge gained in recent years, based on own work and recent literature.

Electrochemical system requires catalysts based on critical raw materials to improve their performances, especially high temperature water electrolyzers (HTELs) contain valuable fine particles such as rare earth elements, strontium, scandium, and nickel.
While many studies are working towards the scale-up of hydrogen production using water electrolysis and it is therefore important to investigate the recycling process of catalyst materials used in HTELs, there has been a lack of studies on mechanical recycling for fine particles.
In this study, we characterized the model particle mixture consisting of Nickel oxide (NiO), Lanthanum strontium manganite (LSM), Yttria stabilized zirconia (YSZ), and Zirconium oxide (ZrO2). Different ceramic materials used in a HTEL cell have a similar wettability. The various ceramic materials used in HTEL cells generally have similar wettability (hydrophilicity) and it can be selectively changed by exploiting their surface charge and adding surfactants. There is a specific pH range (pH 9 - 11) where the surface charge of NiO and LSM is opposite to that of ZrO2 and YSZ, and the modification of its hydrophobicity is successfully reached by using cationic and anionic surfactants. During our research, we observed that the LSMs exhibited ferromagnetism that was different from the rest of the material and they could be selectively separated later by magnetic separation. With different combinations of surfactant, dispersant, and pH, particles can be selectively separated by using particle liquid-liquid extraction.
This study would provide the design of the mechanical separation study for the end-of-life HTEL recycling stream.

Self-diffusion in amorphous germanium is studied at temperatures between 325 and 370 °C utilizing amorphous isotopically controlled germanium multilayer structures. The isotope multilayer is epitaxially grown on a single crystalline germanium-on-insulator structure by means of molecular beam epitaxy and subsequently amorphized by self-ion implantation. After heat treatment, the diffusional broadening of the isotope structure is measured with time-of-flight secondary ion mass spectrometry. The temperature dependence of self-diffusion is accurately described by the Arrhenius equation with the activation enthalpy Q = (2.21 ± 0.12) eV and pre-exponential factor D0 = (2.32 +20.79 −2.10 ) cm2 s−1. The activation enthalpy equals the activation enthalpy of solid phase epitaxial recrystallization (SPER). This agreement suggests that self-diffusion in amorphous germanium is similar to SPER, also mainly mediated by local bond rearrangements. Classical molecular dynamics simulations with a modified Stillinger–Weber-type interatomic potential yield results that are consistent with the experimental data and support the proposed atomic mechanism.

We design a novel class of Janus structures PbXY (X,Y = F, Cl, Br, I) and propose it for the solar mediated photocatalytic water splitting hydrogen production and for the bulk photovoltaic effect. The relaxed layers show a strong variation of the structural parameters which is due to the electronegativity of the halide atoms. The stability of the Janus structures is investigated using formation energy, phonon spectra, elastic constants and Ab-Initio Molecular Dynamics simulations. Using differential charge density calculations and Bader charge analysis, it is found that the atomic bonds may have covalent or ionic character, which depends on the halide atoms in top and bottom layers of the Janus structure. Electronic structure calculations are performed using the GGA functional and the more precise HSE functional. From the band structure, band gap and effective masses of electrons and holes are determined. The large difference between the mobility of both charge carriers as well as the built-in electrical dipole indicate beneficial conditions for charge separation and suppression of charge recombination. The calculated optical absorption spectra show that the Janus structures are suitablefor UV-visible light absorption. Based on VBM and CBM calculation using the HSE functional it is demonstrated that the novel PbXY Janus layers are suitable for water splitting reaction, i.e. for the use as a photocatalyst.

As one of the promising hydrogen production technologies, the development of water electrolysis systems including recycling of their functional components is actively investigated. However, the focus lies on energy and chemicals intensive metallurgical operations and less on mechanical separation processes in most studies. Here, an innovative surfactant-based separation process (using CTAB and SDS) is being investigated to contribute to developing a selective physical separation process for ultrafine particles used in high temperature water electrolyzers (composed of NiO, LSM, ZrO2, and YSZ). Their different surface charge in alkaline solutions influences the adsorption of surfactants on particle surfaces as well as the modification of the particulate wettability, which is a key separation feature. Through the observations of changes in surface charge and wetting behavior in the presence of surfactants, a feasibility of liquid-liquid particle separation (LLPS) is evaluated. The performance of LLPS with model particle mixtures shows a potential of selective separation with recovery of NiO in the organic phase, while the rest of the particles remain in the aqueous phase. The perovskite LSM is not considered in this system because it shows a high possibility of being recovered by magnetic separation. The proposed process can be further optimized by increasing the phase separation stages, and further research is needed on the NiO phase, which showed exceptional behaviors in presence of the surfactants.

Recent decades have seen enormous progress in the experimental investigation of fundamental processes that are relevant to geophysical and astrophysical fluid dynamics. Liquid metals have proven particularly suited for such studies, partly owing to their small Prandtl numbers that are comparable to those in planetary cores and stellar convection zones, partly owing to their high electrical conductivity that allows the study of various magnetohydrodynamic phenomena. After introducing the theoretical basics and the key dimensionless parameters, we discuss some of the most important liquid-metal experiments on Rayleigh–Bénard convection, Alfvén waves, magnetically triggered flow instabilities such as the magnetorotational and Tayler instability, and the dynamo effect. Finally, we summarize what has been learned so far from those recent experiments and what could be expected from future ones.

Lanthanides and actinides are emerging contaminants, but little is known about their uptake and distribution by plants and their interactions in the rhizosphere. To better understand the fate of these metals in plants, we assessed the bioassociation of 2, 20 and 200 µM Eu(III) by five hydroponically grown crops endemic to Europe. The metal’s concentration and its speciation was monitored by inductively coupled plasma mass spectrometry and laser spectroscopy, whereas root exudation was investigated by chromatographic methods. It has been shown, that Eu(III) bioassociation is a two-stage process, involving rapid biosorption followed by accumulation in root tissue and distribution to the stem and leaves. Within 96 h of exposure time, the plant induces a change of Eu(III) speciation in the liquid medium, from a predominant Eu(III) aquo species, as calculated by thermodynamic modelling, to a species with longer luminescence lifetime. Root exudates such as citric, malic, and fumaric acid were identified in the cultivation medium and affect Eu(III) speciation in solution, as was shown by a change in the thermodynamic model. These results contribute to a comprehensive understanding of the fate of lanthanides in the biosphere and provide a basis for further investigations with the chemical analogues Cm(III) and Am(III).

Laser-driven plasma accelerators can produce high-energy, high peak current ion beams by irradiating solid materials with ultra-intense laser pulses. This innovative concept attracts a lot of attention for various multidisciplinary applications as a compact and energy-efficient alternative to conventional accelerators. The maturation of plasma accelerators from complex physics experiments to turnkey particle sources for practical applications necessitates breakthroughs in the generated beam parameters, their robustness and scalability to higher repetition rates and efficiencies.
This thesis investigates viable optimisation strategies for enhancing ion acceleration from thin foil targets in ultra-intense laser-plasma interactions. The influence of the detailed laser pulse parameters on plasma-based ion acceleration has been systematically investigated in a series of experiments carried out on two state-of-the-art high-power laser systems. A central aspect of this work is the establishment and integration of laser diagnostics
and operational techniques to advance control of the interaction conditions for maximum acceleration performance. Meticulous efforts in continuously monitoring and enhancing the temporal intensity contrast of the laser system, enabled to optimise ion acceleration in two different regimes, each offering unique perspectives for applications.
Using the widely established target-normal sheath acceleration (TNSA) scheme and adjusting the temporal shape of the laser pulse accordingly, proton energies up to 70 MeV were reliably obtained over many months of operation. Asymmetric laser pulses, deviating significantly from the standard conditions of an ideally compressed pulse, resulted in the highest particle numbers and an average energy gain ≥ 37 %. This beam quality enhancement is demonstrated across a broad range of parameters, including thickness and material of the target, laser energy and temporal intensity contrast.
To overcome the energy scaling limitations of TNSA, the second part of the thesis focuses on an advanced acceleration scheme occurring in the relativistically induced transparency (RIT) regime. The combination of thin foil targets with precisely matched temporal contrast conditions of the laser enabled a transition of the initially opaque targets to transparency upon main pulse arrival. Laser-driven proton acceleration to a record energy of 150 MeV is experimentally demonstrated using only 22 J of laser energy on target. The low-divergent high-energy component of the accelerated beam is spatially and spectrally well separated from a lower energetic TNSA component. Start-to-end simulations validate these results and elucidate the role of preceding laser light in pre-expanding the target along with the detailed acceleration dynamics during the main pulse interaction. The ultrashort pulse duration of the laser facilitates a rapid succession of multiple known acceleration regimes to cascade efficiently at the onset of RIT, leading to the observed beam parameters and enabling ion acceleration to unprecedented energies. The discussed acceleration scheme was successfully replicated at two different laser facilities and for different temporal contrast levels. The results demonstrate the robustness of this scenario and that the optimum target thickness decreases with improved laser contrast due to reduced pre-expansion. Target transparency was found to identify the best-performance shots within the acquired data sets, making it a suitable feedback parameter for automated laser and target optimisation to enhance stability of plasma
accelerators in the future.
Overall, the obtained results and described optimisation strategies of this thesis may become the guiding step for the further development of laser-driven ion accelerators.

Aim: Gaussian filters are commonly used to improve signal to noise ratio (SNR) of PET images but reduce spatial resolution, thus increasing partial volume effects. Edge preserving filters can alleviate this problem. Especially, the bilateral filter (BF) has shown to have very good performance but requires manual tuning of its two parameters (σS, σI) for optimal results. This is time consuming and hampers clinical use. In this work we, therefore, investigated an automatic method for parameter optimization.

Methods: PET-data from 69 patients were included: 18F-FDG(N=33), 18F-LDOPA(N=25), 68Ga-DOTATATE(N=11). All scans were performed with respiratory gating (8 gates), resulting in 552 low SNR PET volumes. Four 3D ROIs were placed in each volume: one within the liver (to assess noise) and three in areas with elevated focal uptake and various SNR levels. Optimal parameters were determined by a grid search in the (σS,σI) plane aiming at parameters that simultaneously leave SUV_max of the focal uptake mostly unaltered while yielding a noise level comparable to that seen in the sum over all gates. BF with the optimized parameters was then applied and images were visually inspected and analyzed regarding ΔSUV_max and ΔNoise differences (BF vs. unfiltered) in the respective ROIs.

Results: In 19/69 datasets our method failed (over-smoothed background or artifacts). For these images the parameters had to be manually tuned. Overall, optimal parameter values varied over a substantial range (mean±sd: σI=(1.4±1.5) SUV and σS=(5.5±1.7) mm) with σI exhibiting a pronounced tracer dependance. ΔSUV_max of the focal uptake ROIs across all datasets was small (-0.5±0.8) while substantial noise reduction was achieved by (-12.3±3.5) percentage points although detailed behaviour differed between tracers.

Conclusions: Our results demonstrate inter-individual and tracer-specific variability of optimal BF parameters and thus underline the need for careful parameter optimization. In 72% of all investigated cases our automated method was able to perform this optimization without any user intervention. More work is needed to further improve the success rate. However, already in its current form our method does notably reduce workload imposed on the user when considering BF for routine use.

Aim: PET images can exhibit high noise levels which adversely affects qualitative and quantitative image evaluation. Especially challenging are respiratory gated studies and dynamic studies. In such cases, Gaussian filtering is routinely used to improve the signal to noise ratio. However, this degrades the spatial resolution and leads to reduced contrast recovery (CR) in small lesions. Edge preserving bilateral filtering is able to overcome this shortcoming but requires careful tuning of its 2 parameters on a per case basis in order to produce optimal results. In this work we evaluate the potential of using a deep neural network for automatic edge preserving image filtering utilizing a training set of manually filtered PET images.

Methods: We collected unfiltered gated PET data from clinical PET/MR (Philips PET/MR) and PET/CT (Siemens PET/CT) systems and interactively optimized bilateral filtering to achieve the best combination of noise reduction and preservation of spatial resolution. The set of pairs of corresponding unfiltered and filtered images was randomly split into training, validation, and testing sets. The convolutional neural network (CNN) was trained to generate the filtered images from the unfiltered ones. The resulting network model was then evaluated using the ROVER software package regarding its denoising and CR performance and also for presence of artifacts.

Results: With the preliminary data available so far, evaluation of the images filtered with CNN yielded results closely resembling these obtained with manually tuned bilateral filtering in terms of noise level and CR. No apparent image artifacts were found.

Conclusions: Our initial results indicate that the CNN-based post-filtering produces images comparable to interactively optimized filtering. However, more thorough analyses with more image data for testing and training is required to draw definite conclusions about reliably of the proposed solution and will be performed in the coming months. Furthermore, integration of the derived network into a new respiratory motion compensation framework is planned.

Aim: Spatial resolution is one of the key parameters for assessment of PET scanner performance. However, spatial resolution is usually determined with point or line sources, not allowing to study the finite object size and contrast effects known to affect iterative image reconstruction results. We present an approach to determine the spatial resolution at finite background for extended objects. The method was applied to preclinical PET/CT systems (Bruker PET/CT Si78, Mediso PET/CT).

Methods: Spatial resolution is assessed as the full width at half maximum (FWHM) of the point spread function (PSF, approximated by a 3D Gaussian). FWHM is determined from a fit of the convolution of the considered object (homogeneous sphere or rod) with the PSF to the reconstructed image data. In this process, the full 3D vicinity of each sphere/rod is evaluated by transforming the data to spherical/cylindrical coordinates relative to the respective object center/axis. F-18 measurements were performed with a cylindrical phantom (diameter 3.5cm) with a cylindrical insert (diameter 1cm). Measurements were performed without background and at contrast ratio 3:1, respectively.

Results: Without background, we obtained FWHM=1.3mm for the Mediso system, but severe Gibbs artefacts are present, indicating a too aggressive resolution recovery approach. The Bruker system achieves FWHM=2.1mm while avoiding any Gibbs artefacts. At 3:1 contrast, resolution of both systems decreases (to FWHM=2.6mm and 3.2mm, respectively) while Gibbs artefacts are not visible for the Mediso system, too.

Conclusions: Our preliminary results show that both investigated systems have a strongly contrast dependent spatial resolution. Optimizations of reconstruction parameters are currently underway with the aim of reducing the adverse effects of Gibbs artefacts on quantification and improving reconstructed image resolution at finite background while avoiding any negative effects on potential quantification.

Background: Residual image noise is substantial in positron emission tomography (PET) and one of the factors limiting lesion detection, quantification, and overall image quality. Thus, improving noise reduction remains of considerable interest. This is especially true for respiratory-gated PET investigations. The only broadly used approach for noise reduction in PET imaging has been the application of low-pass filters, usually Gaussians, which however leads to loss of spatial resolution and increased partial volume effects affecting detectability of small lesions and quantitative data evaluation. The bilateral filter (BF) – a locally adaptive image filter – allows to reduce image noise while preserving well defined object edges but manual optimization of the filter parameters for a given PET scan can be tedious and time-consuming, hampering its clinical use. In this work we have investigated to what extent a suitable deep learning based approach can resolve this issue by tasking a suitable network with reproducing the results of manually adjusted case-specific bilateral filtering.

Methods: Altogether, 69 respiratory-gated clinical PET/CT scans with three different tracers ([¹⁸F]FDG, [¹⁸F]L-DOPA, [⁶⁸Ga]DOTATATE) were used for the present investigation. Prior to data processing, the gated data sets were split, resulting in a total of 552 single-gate image volumes. For each of these image volumes, four 3D ROIs were delineated: one ROI for image noise assessment and three ROIs for focal uptake (e.g. tumor lesions) measurements at different target/background contrast levels. An automated procedure was used to perform a brute force search of the two-dimensional BF parameter space for each data set to identify the “optimal” filter parameters to generate user-approved ground truth input data consisting of pairs of original and optimally BF filtered images. For reproducing the optimal BF filtering, we employed a modified 3D U-Net CNN incorporating residual learning principle. The network training and evaluation was performed using a 5-fold cross-validation scheme. The influence of filtering on lesion SUV quantification and image noise level was assessed by calculating absolute and fractional differences between the CNN, manual BF, or original (STD) data sets in the previously defined ROIs.

Results: The automated procedure used for filter parameter determination chose adequate filter parameters for the majority of the data sets with only 19 patient data sets requiring manual tuning. Evaluation of the focal uptake ROIs revealed that CNN as well as BF based filtering essentially maintain the focal SUVmax values of the unfiltered images with a low mean±SD difference of δSUV_max^CNN,STD =(-3.9±5.2)% and δSUV_max^BF,STD =(-4.4±5.3)%. Regarding relative performance of CNN vs. BF, both methods lead to very similar SUV_max values in the vast majority of cases with an overall average difference of δSUV_max^CNN,BF =(0.5±4.8)%. Evaluation of the noise properties showed that CNN filtering mostly satisfactorily reproduces the noise level and characteristics of BF with δNoise^CNN,BF=(5.6±10.5)%. No significant tracer dependent differences between CNN and BF were observed.

Conclusions: Our results show that a neural network based denoising can reproduce the results of a case by case optimized BF in a fully automated way. Apart from rare cases it led to images of practically identical quality regarding noise level, edge preservation, and signal recovery. We believe such a network might proof especially useful in the context of improved motion correction of respiratory-gated PET studies but could also help to establish BF-equivalent edge-preserving CNN filtering in clinical PET since it obviates time consuming manual BF parameter tuning.

We propose an efficacious approach to derive the generalized parton distributions for the pion and proton, based upon prior knowledge of their respective parton distribution functions (PDFs). Our method leverages on integral representations of the electromagnetic form factors derived from the light-front holographic QCD (LFHQCD) formalism, coupled with PDFs computed from continuum Schwinger functional methods at the hadronic scale. Using these techniques, we calculate gravitational form factors and associated mass distributions for each hadron. Remarkably, our calculations yield results that closely match recent lattice QCD simulations conducted near the physical pion mass. This work not only deepens our understanding of hadronic structure but also highlights the efficacy of the LFHQCD approach in modeling fundamental properties of hadrons.

A recent study by Wang {\it et al.}(arXiv:2308.14871) proposed a novel connection between the nature of the parton distribution function (PDF) and the characteristics of its moments. In this study, we apply these findings to analyze the evolution of the pion valence quark PDF, garnering valuable qualitative insights. Firstly, we validate the non-negativity and continuity of the PDF across a wide range of scales, indicating the logical consistency of our chosen evolution scheme. Subsequently, we examine the unimodality of both the PDF and its transformed counterpart, the xPDF, i.e., the parton distribution function multiplied by the momentum fraction. We observe a smooth evolution of the peak position of the xPDF towards the small-x region with increasing scale, while intriguingly, the PDF undergoes a phase of bimodal competition as the energy scale evolves.

The design of multiphase reaction and separation processes, such as for example catalytic hydrogenations, distillation and absorption processes, extraction processes, wastewater treatment, and many more, require a profound understanding of the multiphase fluid dynamics inside reactors and contactors. As Computational Multiphase Fluid Dynamics is still not fully mature to simulate complex two-phase and three-phase flow with overlaying heat and mass transfer as well as chemical reaction with sufficient accuracy there is a constant need for advanced experimental and measurement techniques; may it be for the provision of operational, design or thermodynamic parameters or for the validation of codes. In this talk we report on three different novel measurement techniques for this purpose. The presentation shall exemplarily demonstrate how advanced measurement and imaging techniques can be used to study opaque two-phase and three-phase flows in lab environment and potentially also in the field. We report on a) the use of ultrafast X-ray tomography and wire-mesh sensors for the study of reactive bubbly flow in a bubble column, b) the application of a large flow profiler in studying two-phase flow on a distillation column tray, and c) a method to obtain gas dispersion parameters in gas-liquid contactors using modulated gas flow.

Electronic biosensors have found numerous applications in point-of-care (POC) diagnostics thanks to their affordability and facile integration into portable devices, enabling rapid digital display of measured data. However, this class of biosensors still did not reach the stability and reliability required for demanding healthcare applications, such as the diagnostics of complex diseases or therapy monitoring, where multiple biomarkers need to be measured simultaneously with high accuracy and sensitivity. In these application scenarios, multiplexing represents a promising practical solution enabling simultaneous and reproducible measurements at many sensing points, as well as robust statistics. Extended gate (EG) field-effect transistor (FET) biosensor systems are excellent candidates for multiplexed sensing of various physiologically relevant (bio)chemical analytes, from ions to biomolecules. The FET transducer endows the system with exceptional sensitivity and straightforward interfacing with readout electronics, while the physical separation of the gate electrode from the transducer facilitates the integration of multiple individually tailored sensing points into the compact, disposable, and cost-effective sensing interface with versatile architectures [1]. We have demonstrated multiplexed, portable, and standalone EG-FET biosensing platforms combining the optimized design of conventional electronics based on off-the-shelf components and different innovative assay strategies, thereby achieving remarkable detection limits for biomolecules, improved by several orders of magnitude compared to clinical gold standard ELISA assays. Using gold nanoparticle analyte labels as nanoantennae, we realized a highly sensitive POC immunosensor [2]. Moving beyond the traditional POC diagnostics applications, we implemented an indirect assay methodology enabling the detection of target molecules relevant for monitoring cancer immunotherapy [3]. Our EG-FET platforms offer a great opportunity for advanced digitalized healthcare screening and monitoring by quickly providing more comprehensive information to clinicians. They can be easily upgraded to support data connectivity and effective incorporation of artificial intelligence. We envision EG-FET biosensing platforms as important components of future digital health ecosystems.

References
[1] Ž. Janićijević, T.-A. Nguyen-Le, and L. Baraban, ‘Extended-gate field-effect transistor chemo- and biosensors: State of the art and perspectives’, Next Nanotechnology, vol. 3–4, p. 100025, Sep. 2023, doi: 10.1016/j.nxnano.2023.100025.
[2] Ž. Janićijević, T.-A. Nguyen-Le et al., ‘Multiplexed extended gate field-effect transistor biosensor with gold nanoantennae as signal amplifiers’, Biosensors and Bioelectronics, vol. 241, p. 115701, Dec. 2023, doi: 10.1016/j.bios.2023.115701.
[3] T.-A. Nguyen-Le et al. ‘Towards precision immunotherapy: FET biosensors for immunotherapeutic drug monitoring in UniCAR T-cell therapy’, Manuscript in preparation

Long-wavelength free-electrons lasers are unique sources of intense, narrowband THz radiation. I will discuss here time-resolved experiments, where intense THz radiation strongly drives and excites charge carriers in two different types of nanomaterials.
In the first experiment a single GaAs/InGaAs core-shell nanowire with a strained GaAs core and a highly doped InGaAs shell is excited with 12-THz radiation near the tip of a Neaspec scattering scanning near-field microscope (s-SNOM). Subsequently the spectrally resolved mid-infrared response (20-60 THz) is probed using a difference-frequency mixing source. Resulting from this intraband pumping we observe a red shift of the nanowire plasma resonance both in amplitude and phase spectra, which is ascribed to a heating of the electron distribution in the nonparabolic band and to electron transfer into the side valleys, resulting in an increase of the average effective mass.
In the second experiment we excite a single 2D layer of MoSe2 with THz radiation of photon energy in the vicinity of the trion binding energy (here 26 meV). A trion is an exciton that binds a second electron; it is known, even from the hydrogen atom, that its binding energy is roughly an order of magnitude smaller than the exciton binding energy. Subsequently the time-resolved photoluminescence is monitored to observe exciton and trion populations for different excitation photon energies. We clearly identify the resonant ionization of the trion and its conversion to an exciton.

Acoustic focusing of particle flow in microfluidics has been shown to be an efficient tool for particle separation for various chemical and biomedical applications. The mechanism behind the method is the selective effect of the acoustic radiation force on distinct particles. In this way, they can be selectively focused and separated. The technique can also be applied under stationary conditions, i.e., in the absence of fluid flows. In this study, the manipulation of self-propelled particles, such as Janus particles, in an acoustofluidic setup was investigated. In experiments with self-propelled Janus particles and passive beads, we explored the interplay between self-propulsion and the acoustic radiation force. Our results demonstrated unusual and potentially useful effects such as selective trapping, escape, and assisted escape in binary mixtures of active and passive particles. We also analyzed various aspects related to the behavior of Janus particles in acoustic traps in the presence and absence of flows.

The talk gives a short overview about the combination of flash lamp annealing and roll-to-roll applications including the application fields of inkjet printing with nanoparticle inks, transparent conduction oxides, and energy materials.

METABOLATOR is a web application for automated analysis of microcalorimetric metabolic data using Monod's equation. The software was developed in collaboration between the Institute of Resource Ecology and the Department of Information Services and Computing at Helmholtz-Zentrum Dresden - Rossendorf (HZDR), and is now offered as a web service for the community. In addition to publishing the software under an open source license, we made the service, which is hosted on HZDR infrastructure, citable by registering its metadata with DataCite and minting a dedicated Digital Object Identifier (DOI). In this talk, we will present the results of our collaboration from the point of view of a Research Software Engineer (RSE). We will introduce the METABOLATOR software, and discuss its development from initial trials into an installable package and web service. Moreover, we will debate the importance of persistent identifiers (PIDs) for reproducible, citable, and overall FAIR data analysis workflows.

We reveal and analyze an efficient magnetic dynamo action due to precession-driven hydrodynamic turbulence in the local model of a precessional flow, focusing on the kinematic stage of this dynamo. The growth rate of the magnetic field monotonically increases with the Poincaré number Po, characterizing precession strength, and the magnetic Prandtl number Pm, equal to the ratio of viscosity to resistivity, for the considered ranges of these parameters. The critical Po for the dynamo onset decreases with increasing Pm. To understand the scale-by-scale evolution (growth) of the precession dynamo and its driving processes, we perform spectral analysis by calculating the spectra of magnetic energy and of different terms in the induction equation in Fourier space. To this end, we decompose the velocity field of precession-driven turbulence into two-dimensional (2D) vortical and three-dimensional (3D) inertial wave modes. It is shown that the dynamo operates across a broad range of scales and exhibits a remarkable transition from a primarily vortex-driven regime at lower Po to a more complex regime at higher Po where it is driven jointly by vortices, inertial waves, and the shear of the background precessional flow. Vortices and shear drive the dynamo mostly at large scales comparable to the flow system size, and at intermediate scales, while at smaller scales it is mainly driven by inertial waves. This study can be important not only for understanding the magnetic dynamo action in precession-driven flows, but also in a general context of flows where vortices emerge and govern the flow dynamics and evolution.

Industrial multiphase flows are typically characterized by coexisting morphologies. Modern simulation methods are well established for dispersed (e.g., Euler-Euler) or resolved (e.g., Volume-of-Fluid) interfacial structures. A simulation method that requires less knowledge about the flow in advance would be desirable and should allow describing both types of interfacial structures – resolved and dispersed – in a single computational domain. Such methods that combine interface-resolving and non-resolving approaches are called hybrid models. A morphology adaptive multifield two-fluid model, named MultiMorph Model, is proposed, which is able to handle dispersed and resolved interfacial structures coexisting in the computational domain with the same set of equations. For large interfacial structures an interfacial drag formulation is used to describe them in a volume-of-fluid-like manner. For the dispersed structures, the baseline model developed at Helmholtz-Zentrum Dresden - Rossendorf e.V. (HZDR) is applied. The functionality of the framework is demonstrated by several test cases, including a single rising gas bubble in a stagnant water column. Recent developments focus on the transition region, where bubbles are over- or under-resolved for Euler-Euler or for Volume-of-Fluid, respectively. The contribution will focus on an overview about the fundamentals of the MultiMorph Model and recent simulation results for a plunging jet, a stratified counter-current air-water flow and a column tray of a distillation column.

The variable energy availability in the future energy system requires a certain flexibility of energy consumption by energy-intensive industrial processes. The raw materials industry traditionally has high energy demands and low flexibility. This contribution is concerned with the flexibility potential of the copper recycling industry. The current study utilized FactSage and HSC Chemistry software to simulate secondary copper production and OpenLCA, to quantify the environmental impacts of using various flexibility options, such as change in throughput, temporary shutdown of unit operations, and temporarily switching to hydrogen as an alternative energy source.

The folded structure and stability of proteins emanates from their interaction with the water solvent. Water at the protein surface is strongly impacted, resulting in a region with a perturbed hydrogen bonding network. This region, the solvation shell, has distinct properties from that of bulk water, including retarded dynamics and fewer hydrogen bonds. Changes in the structure and dynamics of solvation water can be both perturbed and reported on by Terahertz radiation. Yet, some fundamental properties of solvation water, such as energy transfer within the hydrogen bonding network, remain largely unexplored.
Here, we utilize the TELBE free electron laser source to investigate the nonlinear transmission of lysozyme protein solutions. Z-scan experiments were performed at 0.5 THz, revealing a large nonlinear transmission of water. The nonlinear transmission of lysozyme solutions had a concentration dependent effect, showing that the amount of available water has a role. For the largest protein concentration measured, an inversion in the sign of the nonlinear transmission was observed. These results indicate that the nonlinear properties of protein solutions depend on the fraction of bulk and solvation water, and suggest that the mechanism of energy transfer changes at a threshold value. This work has implications for the study of nonlinear properties in biological systems.

Biomolecular condensates are membrane-less organelles formed via liquid-liquid phase separation of intrinsically disordered proteins. Here, THz spectroscopy is utilized to reveal the structure and dynamics of hydration water in these liquid-like protein environments.

Investigations on how the froth height is influencing the flotation of ultrafine particles using the newly developed separation apparatus MultiDimFlot

We prepared two-dimensional concentric lateral heterostructures of the monolayer transition metal dichalcogenides (TMDCs) MoS₂ and TaS₂ by reactive molecular beam epitaxy (MBE) on chemically inert and weakly interacting Au(111). The heterostructures are in a size regime where quantum confinement can be expected. Despite large lattice mismatch a seamless interconnection of the two materials has been achieved, confirming that the semiconducting core is fully enclosed by a metallic border around its circumference. The resulting strain is analyzed on the atomic scale using scanning tunneling microscopy (STM), corroborated by calculations based on empirical potentials and compared to results from finite elements simulations.

Considering the increasing demand for Li-ion batteries, there is a need for sophisticated recycling strategies with both high recovery rates and low costs. Applying optical sensors for automating component detection is a very promising approach because of the non-contact, real-time process monitoring and the potential for complete digitization of mechanical sorting processes. In this work, mm-scale particles from shredded end-of-life Li-ion batteries are investigated by five different reflectance sensors, and a range from the visible to long-wave infrared is covered to determine the ideal detection window for major component identification as relevant input signals to sorting technologies. Based on the characterization, a spectral library including Al, Cu, separator foil, inlay foil, and plastic splinters was created, and the visible to near-infrared range (400–1000 nm) was identified as the most suitable spectral range to reliably discriminate between Al, Cu, and other battery components in the recycling material stream of interest. The evaluation of the different sensor types outlines that only imaging sensors meet the requirements of recycling stream monitoring and can deliver sufficient signal quality for subsequent mechanical sorting controls. Requirements for the setup parameters were discussed leading to the setup recommendation of a fast snapshot camera with a sufficiently high spectral resolution and signal-to-noise ratio.

Our aim is to obtain a process understanding of the interaction between Ln and plants, from the initial exposure and the cellular uptake until the translocation into and aboveground parts.
Therefore, we use hydroponically grown Avena strigosa, a grass cultivated as animal feed in many countries, to investigate the uptake of Eu(III), as analogue for trivalent Ln and the actinides Cm(III) and Am(III), and its
distribution throughout the plant.
Laser spectroscopy, chemical microscopy and data deconvolution by iterative factor analysis were combined with autoradiography, liquid chromatography, biochemical methods and ICP-MS to elucidate Eu(III) bioassociation and translocation behavior and to identify the involved physico-chemical binding forms of the metal in the different plant organs.

While two-dimensional (2D) materials are traditionally derived from bulk layered compounds bonded by weak van der Waals (vdW) forces, the recent surprising experimental realization of non-vdW 2D compounds obtained from non-layered crystals [1,2] foreshadows a new direction in 2D systems research. To elucidate their structure and properties, electron microscopy and first-principles calculations are indispensable tools.

Contributing to the predictive design of these novel nanoscale compounds, here, we present several dozens of candidates derived from applying data-driven research methodologies
in conjunction with autonomous first-principles calculations [3,4]. We find that the oxidation state of the surface cations of the 2D sheets as well as accounting for strong surface relaxations upon exfoliation are crucial factors determining their stabilization.
The candidates exhibit a wide range of appealing electronic, optical and in particular magnetic properties owing to the (magnetic) cations at the surface of the sheets. Despite of several ferromagnetic candidates, even for the antiferromagnetic representatives, the surface spin polarizations are diverse ranging from moderate to large values modulated in addition by ferromagnetic and antiferromagnetic in-plane coupling [3]. These features can be accessed by experimental techniques such as (spin-polarized) scanning tunnelling microscopy. At the same time, chemical tuning by surface passivation provides a valuable handle to further control the magnetic properties of these novel 2D compounds [5] thus rendering them an attractive platform for fundamental and applied nanoscience.

[1] A. Puthirath Balan et al., Nat. Nanotechnol. 13, 602 (2018).
[2] A. Puthirath Balan et al., Mater. Today 58, 164 (2022).
[3] R. Friedrich et al., Nano Lett. 22, 989 (2022).
[4] T. Barnowsky et al., Adv. Electron. Mater. 9, 2201112 (2023).
[5] T. Barnowsky et al., Nano Lett. in press (2024).

This repository contains data to test, develop and debug MALA and MALA based runscripts. If you plan to do machine-learning tests ("Does this network implementation work? Is this new data loading strategy working?"), this is the right data to test with. It is NOT production level data!

We report on defects dynamics during heat treatment in plastically deformed metallic materials using positron annihilation lifetime spectroscopy carried out on the intense pulsed positron beam. The conducted experiment allowed us to observe the changes in the concentration and sizes of vacancy-like defects observed during in-situ annealing. We monitored heat treatments up to 300 oC in hydrostatic extruded Ti and cavitation peened V-4Cr-4Ti alloy. We were able to track the recovery processes in Ti and redistribution of large voids at the surface of cavitation peened V-4Cr-4Ti alloy. The relaxation time during recovery was about 20 minutes. Performed experiments show that in cold-worked metallic materials significant changes in vacancy clusters concentrations occur at mildly elevated temperatures. The presented results give opportunity to the application of in-situ observation of defects dynamic to similar problems related to thermomechanical processing of metallic materials.

Matter at extreme densities and temperatures displays a complex quantum behavior that is characterized by Coulomb interactions, thermal excitations, and partial ionization. Such warm dense matter (WDM) is ubiquitous throughout the universe and occurs in a host of astrophysical objects such as giant planet interiors and white dwarf atmospheres. A particularly intriguing application is given by inertial confinement fusion, where both the fuel capsule and the ablator have to traverse the WDM regime in a controlled way to reach ignition.

In practice, rigorously understanding WDM is highly challenging both from experimental measurements and numerical simulations [1]. On the one hand, interpreting and diagnosing experiments with WDM requires a suitable theoretical description. One the other hand, there is no single method that is capable of accurately describing the full range of relevant densities and temperatures, and the interpretation of experiments is, therefore, usually based on a number of de-facto uncontrolled approximations. The result is the vicious cycle of WDM diagnostics: making sense of experimental observations requires theoretical modeling, whereas theoretical models must be benchmarked against experiments to verify their inherent assumptions.

In this work, we outline a strategy to break this vicious cycle by combining the X-ray Thomson scattering (XRTS) technique [2] with new ab initio path integral Monte Carlo (PIMC) capabilities [3,4,5]. As a first step, we have proposed to interpret XRTS experiments in the imaginary-time (Laplace) domain, which allows for the model-free diagnostics of the temperature [6] and normalization [7]. Moreover, by switching to the imaginary-time, we can directly compare our quasi-exact PIMC calculations with the experimental measurement [5]. This opens up novel ways to diagnose the experimental conditions, as we have recently demonstrated for the case of strongly compressed beryllium at the National Ignition Facility.

Our results open up new possibilities for improved XRTS set-ups that are specifically designed to be sensitive to particular parameters of interest [8]. Moreover, the presented PIMC capabilities are important in their own right and will allow for a gamut of applications, including equation-of-state calculations and the estimation of structural properties and linear response functions.

[1] T. Dornheim et al., Phys. Plasmas 30, 032705 (2023) [2] S. Glenzer and R. Redmer, Rev. Mod. Phys. 81, 1625 (2009) [3] T. Dornheim et al., J. Phys. Chem. Lett. 15, 1305-1313 (2024) [4] T. Dornheim et al., arXiv:2403.01979 [5] T. Dornheim et al., arXiv:2402.19113 [6] T. Dornheim et al., Nature Commun. 13, 7911 (2022) [7] T. Dornheim et al., arXiv:2305.15305 [8] Th. Gawne et al., arXiv:2403.02776

This study explores defect engineering in 2D materials using ion beam irradiation to modify the electrical and optical properties with potential in advancing quantum electronics and photonics. Helium and neon ions ranging from 5 to 7.5 keV are employed to manipulate charge transport in monolayer molybdenum disulfide (MoS2). In situ electrical characterization occurs without vacuum breakage post-irradiation. Raman and photoluminescence spectroscopy quantify ion irradiation’s impact on MoS2. Small doses of helium ion irradiation enhance monolayer MoS2 conductivity in field-effect transistor geometry by inducing doping and substrate charging. Findings reveal a strong correlation between the electrical properties of MoS2 and the primary ion used, as well as the substrate on which the irradiation occurred. Using hexagonal boron nitride (h-BN) as a buffer layer between MoS2 flake and SiO2 substrate yields distinct alterations in electrical behavior subsequent to ion irradiation compared to the MoS2 layer directly interfacing with SiO2. Molecular dynamics simulations and density functional theory provide insight into experimental results, emphasizing substrate influence on measured electrical properties post-ion irradiation.

This work details a Compton-scattering-based methodology, referred to as Backscatter Gating (BSG), for characterizing the time, energy, and position resolutions of long form factor organic scintillators using a single, fairly minimal measurement setup. Such a method can ease the experimental burden in scenarios where many such scintillator elements may need to be individually characterized before assembly into a larger detector system. A thorough theoretical exploration of the systematic parameters is provided, and the BSG method is then demonstrated by a series of experimental measurements. This “complete” characterization via the BSG method is novel, having previously been used primarily for energy resolution characterization. The method also allows for determination of the assembled scintillator’s technical attenuation length and provides a means of verifying the presence or absence of flaws within the scintillator or its optical coupling.

Transition metal oxynitrides are a promising class of functional materials for photoelectrochemical (PEC) applications. Although these compounds are most commonly synthesized via ammonolysis of oxide precursors, such synthetic routes often lead to poorly controlled oxygen-to-nitrogen anion ratios, and the harsh nitridation conditions are incompatible with many substrates, including transparent conductive oxides. Here, we report direct reactive sputter deposition of a family of zirconium oxynitride thin films and the comprehensive characterization of their tunable structural, optical, and functional PEC properties. Systematic increases of the oxygen content in the reactive sputter gas mixture enable access to different crystalline structures within the zirconium oxynitride family. Increasing oxygen contents lead to a transition from metallic to semiconducting to insulating phases. In particular, crystalline Zr2ON2-like films have band gaps in the UV−visible range and are n-type semiconductors. These properties, together with a valence band maximum position located favorably relative to the water oxidation potential, make them viable photoanode candidates. Using chopped linear sweep voltammetry, we indeed confirm that our Zr2ON2 films are PEC-active for the oxygen evolution reaction in alkaline electrolytes. We further show that high-vacuum annealing boosts their PEC performance characteristics. Although the observed photocurrents are low compared to state-of-the-art photoanodes, these dense and planar thin films can offer a valuable platform for studying oxynitride photoelectrodes, as well as for future nanostructuring, band gap engineering, and defect engineering efforts.

The growing demand for bio-pharmaceuticals necessitates improved methods for the
characterization of stirred tank reactors (STR) and their mixing heterogeneities. Traditional Eulerian
measurement approaches fall short, culminating in the use of Lagrangian Sensor Particles (LSP) to
map large-scale STRs and track the lifelines of microorganisms such as Chinese Hamster Ovary cells.
This study investigates the hydrodynamic characteristics of LSPs, specifically examining the effects
that size and position of the Center of Mass (CoM) has on their flow-following capabilities. Two
Lagrangian Particle (LP) designs are evaluated, one with CoM and Geometric Center aligned, and
another with a shifted CoM. The experimental study is conducted in a rectangular vessel filled with
deionized water featuring a stationary circular flow. Off-center LPs exhibit higher velocities, an
increased number of floor contacts, and moreover, a less homogeneous particle probability of presence
within the vessel compared to LPs with CoM and Geometric center aligned. Lattice-Boltzmann Large
Eddy Simulations provide complementary undisturbed fluid velocity data for the calculation of the
Stokes number St. Building upon these findings, differences in the Stokes number St between the
two LP variants of ΔSt = 0.01 (25 mm LP) and ΔSt = 0.13 (40 mm LP) are calculated, highlighting
the difference in flow behavior. Furthermore, this study offers a more representative calculation
of particle response time approach, as the traditional Stokes number definition does not account
for non-homogeneous particles, resulting in an alternative Stokes number (ΔStalt = 0.84 (25 mm
LP) and ΔStalt = 2.72 (40 mm LP)). This study contributes to the improved characterization of STRs
through the use of Lagrangian Sensor Particles. Results highlight the implications the internal mass
distribution has on LSP design, offering crucial considerations for researchers in the field.

Froth flotation is an efficient separation process for particles with sizes between 10 μm and 200 μm, which is based on differences in the particle wettabilities, or more precisely dewettingability. Therefore, a fundamental understanding of the interfacial properties is required. Within the project MultiDimFlot, which is part of the priority programme SPP2045, funded by the German research foundation (DFG), the selective separation of ultrafine particles (< 10 μm) according to multiple particle properties (e.g. wettability, shape, size) by flotation is investigated. For this purpose, two ultrafine glass particle fractions with different shapes are used, i.e. glass spheres and fragments, and their wettability is modified via an esterification reaction using alcohols, where the wettability of the esterified particles is controlled by the length of the alkyl chain.
In order to investigate the influence of wetting heterogeneities on the separation via flotation, glass slides with the same chemical composition as the glass particles and that were esterified in the same way, were analysed via atomic force microscopy (AFM). By applying colloidal probe AFM in dry and liquid mode, information on the hydrophobic interactions on the surface of the glass slides with different levels of wettability are obtained. Furthermore, the esterified glass slides are analysed by measuring static and dynamic contact angles against water using the sessile drop method. This information is set into context with the surface energy results of the glass particles, obtained via inverse gas chromatography as well as results obtained by liquid-liquid extraction of particles, which is used to study the behaviour of the particles at the interface.
The correlation of the various methods shed light on the (de)wetting(ability) heterogeneities, how these are changed through esterification and how these results can be transferred to flotation.

Stable composite objects (e.g. hadrons, nuclei, atoms, molecules, and superconducting pairs) formed by attractive forces are ubiquitous in nature. In contrast, composite objects stabilized via repulsive forces were long thought to be theoretical constructions due to their fragility in naturally occurring systems. Surprisingly, the formation of bound atom pairs by strong repulsive interactions has been demonstrated experimentally in optical lattices1. Despite this success, repulsively bound particle pairs were believed to have no analogue in condensed matter due to strong decay channels. Here, we present spectroscopic signatures of repulsively bound three-magnon states and bound magnon pairs, in the Ising-like chain antiferromagnet BaCo2V2O8. In large transverse fields, below the quantum critical point, we identify repulsively bound magnon states by comparing terahertz spectroscopy measurements to theoretical results for the Heisenberg-Ising chain antiferromagnet, a paradigmatic quantum many-body model2–5. Our experimental results show that these high-energy repulsively bound magnon states are well separated from continua, exhibit significant dynamical responses and, despite dissipation, are sufficiently long-lived to be identified. As the transport properties in spin chains can be altered by magnon bound states, we envision such states could serve as resources for magnonics based quantum information processing technologies6–8.

To improve the quality of photocathodes is one of the critical issues in enhancing the stability and reliability of photo-injector systems. Magnesium has a low work function (3.6 eV) and shows high quantum efficiency (QE) after proper surface cleaning. This paper presents the investigation of alternative surface cleaning procedures, such as ps laser cleaning, thermal cleaning and ion beam cleaning. The QE is able to be improved two magnitudes after the treatment.