Publications Repository - Helmholtz-Zentrum Dresden-Rossendorf

We analyze the evolution of current driven kink instabilities of a highly magnetized relativistic plasma column, focusing in particular on its dissipation properties. The instability evolution leads to the formation of thin current sheets where the magnetic energy is dissipated. We find that the total amount of dissipated magnetic energy is independent of the dissipation properties. Dissipation occurs in two stages: a peak when the instability saturates, which is characterized by the formation of a helicoidal current sheet at the boundary of the deformed plasma column, followed by a weaker almost flat phase, in which turbulence develops. The detailed properties of these two phases depend on the equilibrium configuration and other parameters, in particular on the steepness of the pitch radial profile, on the presence of an external axial magnetic field and on the amount of magnetization. These results are relevant for high energy astrophysical sources, since current sheets can be the sites of magnetic reconnection where particles can be accelerated to relativistic energies and give rise to the observed radiation.

We present recent progress on the development of standard MRI and its variants -- helical and azimuthal MRI -- in a liquid sodium cylindrical Taylor-Couette (TC) flow in connection with a new large-scale experimental campaign planned at Helmholtz-Zentrum Dresden-Rossendorf (HZDR), which is devoted to the detection of this instability in the lab. Currently, a new TC experimental device is under construction and will be put in operation in 2022. In preparation for these experiments, we performed targeted linear and nonlinear study of MRI for those values of key parameters (Lundquist, Reynolds, magnetic Prandtl numbers, etc) relevant for the upcoming experiments and show its feasibility under new experimental conditions. After that we will present the current status and preparatory work for these experiments at HZDR. Due to very small magnetic Prandtl numbers of liquid metals used, the Reynolds numbers needed to excite MRI in experiments are extremely high, of the order of million. For this reason the instability has always remained evasive in the previous TC experiments. The large size of the experimental device and a wide range of rotation rates are among the main advantages of our new TC device, which offer a unique possibility to reach such high Reynolds numbers and hence to capture MRI in the lab. However, a careful further analysis is required in this case to correctly disentangle MRI modes from other possible instabilities at high Reynolds numbers arising in a finite-length TC flow under the effect of the top and bottom endcaps.

The talk will summarize our application-oriented activities on flexible and printable magnetic field sensors.

Motion sensing is the primary task in numerous disciplines including industrial robotics, prosthetics, virtual and augmented reality appliances. In rigid electronics, rotations, displacements and vibrations are typically monitored using magnetic field sensors. Here, we will discuss the fabrication of flexible [1-3], stretchable [4,5] and printable [5] magnetoelectronic devices. The technology platform relies on high-performance magnetoresistive and Hall effect sensors deposited or printed on ultrathin polymeric foils. These skin conformal flexible and printable magnetosensitive elements enable touchless interactivity with our surroundings based on the interaction with magnetic fields [6], which is relevant for electronics skins [3,5], smart wearables [1,4,5], soft robotics [2] and human-machine interfaces [1,3-5,7].

[1] P. Makushko et al., “Flexible Magnetoreceptor with Tunable Intrinsic Logic for On-Skin Touchless Human-Machine Interfaces”, Adv. Funct. Mater. 31, 2101089 (2021).

[2] M. Ha et al., “Reconfigurable Magnetic Origami Actuators with On-Board Sensing for Guided Assembly”, Adv. Mater. 33, 2008751 (2021).

[3] G. S. Canon Bermudez et al., “Electronic-skin compasses for geomagnetic field driven artificial magnetoreception and interactive electronics”, Nature Electronics 1, 589 (2018).

[4] G. S. Canon Bermudez et al., “Magnetosensitive e-skins with directional perception for augmented reality”, Science Advances 4, eaao2623 (2018).

[5] M. Ha et al., “Printable and Stretchable Giant Magnetoresistive Sensors for Highly Compliant and Skin-Conformal Electronics”, Adv. Mater. 33, 2005521 (2021).

[6] G. S. Canon Bermudez et al., “Magnetosensitive E-Skins for Interactive Devices”, Adv. Funct. Mater. 31, 2007788 (2021).

[7] J. Ge et al., “A bimodal soft electronic skin for tactile and touchless interaction in real time”, Nature Communications 10, 4405 (2019).

I had a pleasure and honor to work with Yuri Gaididei on the topic of curvature effects in magnetism, which is now emerged in a new research field known as curvilinear magnetism. Our cooperation started back in 2013 with a visit of Prof. Gaididei and his team to the Leibniz Institute for Solid State and Materials Research Dresden. The outcome of numerous discussions, which we had during that visit, was the foundational work on the description of curvature effects in magnetic thin films [1]. This work pushed the understanding of the experimental data to the qualitatively new level and predicted numerous effects stemming from the geometry induced anisotropic and chiral interactions. In my talk, I will discuss the experimental realisations of geometrically curved low-dimensional architectures and their characterization, which among others resulted in the experimental confirmation of the geometrically induced chiral effects [2] predicted by Yuri Gaididei. Geometrically curved magnetic thin films are interesting not only fundamentally. They are the key component of mechanically flexible magnetic field sensors. I will briefly outline our activities on the so-called shapeable magnetoelectronics, which includes flexible, stretchable and printable magnetic field sensors for the realisation of human-machine interfaces [3,4], interactive electronics for virtual [5] and augmented [6] reality applications and soft robotics [7] to mention just a few. The presence of the geometrical curvature in a magnetic thin film influences pinning of magnetic domain walls and in this respect it affects the sensitivity of mechanically flexible magnetic field sensors. This is an intimate link between the fundamental topic of curvilinear magnetism and application-oriented activities on shapeable magnetoelectornics. This link will be discussed in the presentation as well.

[1] Y. Gaididei et al., “Curvature Effects in Thin Magnetic Shells”, Physical Review Letters 112, 257203 (2014).
[2] O. Volkov et al., “Experimental observation of exchange-driven chiral effects in curvilinear magnetism”, Physical Review Letters 123, 077201 (2019).
[3] P. Makushko et al., “Flexible Magnetoreceptor with Tunable Intrinsic Logic for On-Skin Skin Touchless Human-Machine Interfaces”, Advanced Functional Materials 31, 2101089 (2021).
[4] J. Ge et al., “A bimodal soft electronic skin for tactile and touchless interaction in real time”, Nature Communications 10, 4405 (2019).
[5] G. S. Canon Bermudez et al., “Electronic-skin compasses for geomagnetic field driven artificial magnetoception and interactive electronics”, Nature Electronics 1, 589 (2018).
[6] G. S. Canon Bermudez et al., “Magnetosensitive e-skins with directional perception for augmented reality”, Science Advances 4, eaao2623 (2018).
[7] M. Ha et al., “Reconfigurable Magnetic Origami Actuators with On-Board Sensing for Guided Assembly”, Advanced Materials 33, 2008751 (2021).

In this presentation, I reviewed our activities on flexible and printable magnetic field sensors for the realization of human-machine interfaces, interactive virtual and augmented reality applications and soft robotics.

The outbreak of coronavirus disease 2019 (COVID-19), caused by the virus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has already created emergency situations in almost every country of the world. The disease spreads all over the world within a very short period of time after its first identification in Wuhan, China in December, 2019. In India, the outbreak, starts on 2nd March, 2020 and after that the cases are increasing exponentially. Very high population density, the unavailability of specific medicines or vaccines, insufficient evidences regarding the transmission mechanism of the disease also make it more difficult to fight against the disease properly in India. Mathematical models have been used to predict the disease dynamics and also to assess the efficiency of the intervention strategies in reducing the disease burden. In this work, we propose a mathematical model to describe the disease transmission mechanism between the individuals. Our proposed model is fitted to the daily new reported cases in India during the period 2nd March, 2020 to 12th November, 2020. We estimate the basic reproduction number, effective reproduction number and epidemic doubling time from the incidence data for the above-mentioned period. We further assess the effect of implementing preventive measures in reducing the new cases. Our model projects the daily new COVID-19 cases in India during 13th November, 2020 to 25th February, 2021 for a range of intervention strength. We also investigate that higher intervention effort is required to control the disease outbreak within a shorter period of time in India. Moreover, our analysis reveals that the strength of the intervention should be increased over the time to eradicate the disease effectively.

Drill-core analysis is paramount for the characterization of deposits in mineral exploration. Over the past years, the use of hyperspectral (HS) sensors has rapidly increased to improve the reliability and efficiency of core logging. However, scanning drill-core samples of an entire mineral deposit entails several complex challenges, from transport logistics to large scale data management and analysis. Hence, academic studies on new applications of drill-core HS data at a mineral deposit scale remain rare.
We present a semi-automated workflow for large scale interpretation of HS data, founded on a novel approach of mineral mapping based on a supervised dictionary learning technique. This approach exploits the complementary information from scanning electron microscopy based automated mineralogy and hyperspectral imaging techniques for estimating mineral quantities along all boreholes. We propose that it is effectively possible to propagate the mineral quantification to the entire borehole from small samples with high resolution miner- alogical information strategically selected throughout the deposit.
We showcase this approach on data acquired in the Elvira shale-hosted volcanogenic massive sulphide (VMS) deposit located at the Iberian Pyrite Belt (IPB), where 7000 m of drill-core were acquired along 80 boreholes. Resulting maps provide insights on the controls on the mineral assemblages and chemical composition of specific minerals across the whole volume at several spatial scales, from large scale variations within apparently ho- mogeneous black shales to small scale mineral composition variations, of potential use as vectors towards mineralization. This approach adds value to the core data, allowing for a better understanding of the geological setting of the Elvira deposit and providing valuable insights for future exploration targeting in the region.
This approach based on machine learning can easily be transposed to different ore deposits with a limited input from a geologist.

The interaction of overdense plasma with ultra-intense laser pulses presents a promising approach to enable the development of very compact ion sources. Prospective applications of high-energetic protons and ions include, but are not limited to, medical applications (in particular ion beam radiotherapy), laboratory astrophysics and nuclear fusion. However, current records for maximum proton energies (94 MeV, 2018) are still below the required values for the aforementioned applications (typically in the range of 150-250 MeV), and especially challenges such as stability and spectral control remain unsolved to this day. In particular, significant effort per experiment and a high-dimensional design space renders naive sampling approaches ineffective. Furthermore, due to the strong nonlinearities of the underlying laser-plasma physics, synthetic observations by means of particle-in-cell (PIC) simulations are computationally very costly, and the maximum distance between two sampling points is strongly limited as well. Consequently, in order to build useful surrogate models for future data generation and experimental understanding and control, a combination of highly optimized simulation codes (where we employ PIConGPU), powerful data-based methods, such as artificial neural networks (ANNs), and modern sampling approaches are essential.
Specifically, we employ invertible neural networks for bidirectional learning of input (parameter) and output (observables) and convolutional autoencoder to reduce intermediate field data to a lower-dimensional latent representation.

Interaction of an overdense plasma with ultra-intense laser pulses represents a promising route to enable the development of compact ion sources. Prospective applications of high-energetic protons and ions include, but are not limited to, medical applications, materials science and nuclear fusion. However, current records for maximum proton energies (94 MeV, Higginson Nat Commun 9, 724 2018) are still well below the required values for many applications (typically 150-250 MeV) and many challenges remain unsolved to this day. In particular, a high-dimensional parameter space, as well as considerable effort per observation, make it impossible to uniformly sample the parameter space by means of simulations, let alone experimentally, while simultaneously strong nonlinearities limit the coarseness of the grid. Consequently, a combination of modern sampling approaches, optimized simulation codes and powerful data-based methods are essential for building realistic surrogate models. More specifically, we want to employ invertible neural networks (Ardizzone arXiv:1808.04730, 2018) for bidirectional learning of input and output, and convolutional autoencoder (Vincent J. Mach. Learn. Res. 11, 12 2010) to reduce intermediate field data to a lower-dimensional latent representation.

The structural and magnetic properties of Zn/Al doped nickel ferrite thin films can be adjusted by changing the Zn and Al content. The films are epitaxially grown by reactive magnetron sputtering using a triple cluster system to sputter simultaneously from three different targets. Upon the variation of the Zn content the films remain fully strained with similar structural properties, while the magnetic properties are strongly affected. The saturation magnetization and coercivity as well as resonance position and linewidth from ferromagnetic resonance (FMR) measurements are altered depending on the Zn content in the material. The reason for these changes can be elucidated by investigation of the x-ray magnetic circular dichroism spectra to gain site and valence specific information with elemental specificity. Additionally, from a detailed investigation by broadband FMR a minimum in g-factor and linewidth could be found as a function of film thickness. Furthermore, the results from a variation of the Al content using the same set of measurement techniques is given. Other than for Zn, the variation of Al affects the strain and even more pronounced changes to the magnetic properties are apparent.

The magnetization dynamics in periodic magnetic stripe domain patterns in thin ferromagnetic films is summarized. First, a brief theoretical background of magnetization dynamics and spin wave dynamics in the presence of a single domain wall for various configuration of magnetic domains (in-plane and out-of-plane) and domain walls (Bloch- and Néel-type domain walls) is introduced. Then, spin wave dynamics in periodic stripe magnetic domain pattern is studied on an example of a multilayer system composed of Co/Pd. The considered magnetization configuration is non-collinear across both the domain walls and the film thickness. It has the form of a “corkscrew”-like structure that consists of a Bloch wall in the film's center with two Néel caps at the film's surfaces. All domain walls have the same polarity. The Brillouin light scattering measurements were performed to study magnetization dynamics experimentally, and the results were interpreted with the use of micromagnetic simulations. The periodic arrangement of the magnetization increases the number of spin wave bands similarly like a one-dimensional magnonic crystal. The properties of the dynamical excitation related to translational motion of the domain wall (zero-frequency Goldstone modes) are shown. Further, the dynamics of the magnetization configurations with the same and alternating polarities of the neighboring walls are compared. The magnetization dynamics for the propagation along the domain walls direction is analyzed, as well. Here, the interaction between the walls and nonreciprocal properties result in the formation of unidirectional channels, where waves travel in every second wall in the opposite direction.

Warm dense matter is of high current interest for many applications, including astrophysics, material science, and fusion research. Yet, the accurate description of electronic correlation effects at these conditions is most difficult, and often computationally intensive ab-initio methods have to be used. Here we present the effective static approximation (ESA) [1] to the local field correction (LFC) of the electron gas, which enables highly accurate calculations of electronic properties like the dynamic structure factor S(q,ω), the static structure factor S(q), and the interaction energy v with no computational extra cost compared to the random phase approximation (RPA).
More specifically, the ESA combines the recent neural-net representation of ab-initio path integral Monte Carlo results [2] of the temperature-dependent LFC in the exact static limit with a consistent large wave-number limit. It is suited for a straightforward integration into existing codes. We demonstrate the importance of the LFC for practical applications by re-evaluating the recent x-ray Thomson scattering experiment on aluminum by Sperling et al. [3]. We find that an accurate incorporation of electronic correlations within the ESA leads to a different prediction of the inelastic scattering spectrum than obtained from state-of-the-art models like linear-response time-dependent density functional theory. Furthermore, the ESA scheme is particularly relevant for the development of advanced exchange-correlation functionals in density functional theory, or for the computation of material properties like the thermal/electrical conductivity, stopping power, etc.
Finally, the ESA is now readily available as an analytical representation [4] and can be easily incorporated into existing codes.

[1] T. Dornheim et al., Phys. Rev. Lett. 125, 235001 (2020)
[2] T. Dornheim et al., J. Chem. Phys. 151, 194104 (2019)
[3] P. Sperling et al., Phys. Rev. Lett. 115, 115001 (2015)
[4] T. Dornheim et al., Phys. Rev. B 103, 165102 (2021)

Warm dense matter is of high current interest for many applications, including astrophysics, material science, and fusion research. Yet, the accurate description of electronic correlation effects at these conditions is most difficult, and often computationally intensive ab-initio methods have to be used. Here we present the effective static approximation (ESA) [1] to the local field correction (LFC) of the electron gas, which enables highly accurate calculations of electronic properties like the dynamic structure factor S(q,ω), the static structure factor S(q), and the interaction energy v with no computational extra cost compared to the random phase approximation (RPA).
More specifically, the ESA combines the recent neural-net representation of ab-initio path integral Monte Carlo results [2] of the temperature-dependent LFC in the exact static limit with a consistent large wave-number limit. It is suited for a straightforward integration into existing codes. We demonstrate the importance of the LFC for practical applications by re-evaluating the recent x-ray Thomson scattering experiment on aluminum by Sperling et al. [3]. We find that an accurate incorporation of electronic correlations within the ESA leads to a different prediction of the inelastic scattering spectrum than obtained from state-of-the-art models like linear-response time-dependent density functional theory. Furthermore, the ESA scheme is particularly relevant for the development of advanced exchange-correlation functionals in density functional theory, or for the computation of material properties like the thermal/electrical conductivity, stopping power, etc.

[1] T. Dornheim et al., Phys. Rev. Lett. 125, 235001 (2020)
[2] T. Dornheim et al., J. Chem. Phys. 151, 194104 (2019)
[3] P. Sperling et al., Phys. Rev. Lett. 115, 115001 (2015)

Warm dense matter (WDM)---an extreme state that is characterized by extreme densities and temperatures---has emerged as one of the most active frontiers in plasma physics and material science. In nature, WDM occurs in astrophysical objects such as giant planet interiors and brown dwarfs. In addition, WDM is highly important for cutting-edge technological applications such as inertial confinement fusion and the discovery of novel materials.
In the laboratory, WDM is studied experimentally in large facilities around the globe, and new techniques have facilitated unprecedented insights into exciting phenomena like the formation of nano diamonds at planetary interior conditions [1]. Yet, the interpretation of these experiments requires a reliable diagnostics based on accurate theoretical modeling, which is a notoriously difficult task [2].
In this talk, I give an overview of recent ground-breaking developments in WDM theory, including its static [3], dynamic [4], and nonlinear [5] properties. Finally, I will present a road map towards a true ab-initio description of WDM.

[1] D. Kraus et al., Nature Astronomy 1, 606-611 (2017)
[2] M. Bonitz et al., Physics of Plasmas 27, 042710 (2020)
[3] T. Dornheim et al., Physics Reports 744, 1-86 (2018)
[4] T. Dornheim et al., Physical Review Letters 121, 255001 (2018)
[5] T. Dornheim et al., Physical Review Letters 125, 085001 (2020)

Over the last decades, there has emerged a growing interest in warm dense matter (WDM), an exotic state with extreme densities and temperatures. These conditions are relevant for, e.g., the description of astrophysical objects and laser-excited solids, but a theoretical description is notoriously difficult.

In this work, we focus on the uniform electron gas (UEG), one of the most fundamental model systems in physics and quantum chemistry. Although most ground state properties of the UEG have been known for decades, a full thermodynamic description at WDM conditions has only been achieved recently [1,2]. In this contribution, we extend these considerations to the response of the UEG to an external perturbation, which is of key relevance both for theory and the interpretation of experiments.

More specifically, we have carried out extensive path integral Monte Carlo simulations of the UEG going from WDM conditions to the strongly correlated electron liquid regime to compute an imaginary-time density—density correlation function. The latter is subsequently used as input for a new reconstruction procedure, which allows to obtain ab initio results for the dynamic structure factor including all exchange-correlation effects [3,4]. Interestingly, we find nontrivial shapes around intermediate wave vectors, which manifest in a negative dispersion relation at strong coupling.

Moreover, we present extensive new results and a subsequent machine-learning representation of the static local field correction [5], which is of high importance for many applications, and new results for the electron liquid regime [6].

[1] S. Groth et al., Phys. Rev. Lett. 119, 135001 (2017)
[2] T. Dornheim et al., Phys. Reports 744, 1-86 (2018)
[3] T. Dornheim et al., Phys. Rev. Lett. 121, 255001 (2018)
[4] S. Groth et al., Phys. Rev. B 99, 235122 (2019)
[5] T. Dornheim et al., J. Chem. Phys. 151, 194104 (2019)
[6] T. Dornheim et al., Phys. Rev. B 101, 045129 (2020)

In this contribution, the process intensification potential of the advanced reactor concept is demonstrated and a hybrid modeling approach is proposed, which allows assessing the STY and identifying optimal operating conditions. The model consists of a 3D Eulerian-Eulerian model and a 1D heterogeneous continuum model to predict hydrodynamics (i.e. gas-liquid interface position and pressure drop), mass transfer and reaction.

The applications being developed within the U.S. Exascale Computing Project (ECP) to run on imminent Exascale computers will generate scientific results with unprecedented fidelity and record turn-around time. Many of these codes are based on particle-mesh methods and use advanced algorithms, especially dynamic load-balancing and mesh-refinement, to achieve high performance on Exascale machines. Yet, as such algorithms improve parallel application efficiency, they raise new challenges for I/O logic due to their irregular and dynamic data distributions. Thus, while the enormous data rates of Exascale simulations already challenge existing file system write strategies, the need for efficient read and processing of generated data introduces additional constraints on the data layout strategies that can be used when writing data to secondary storage. We review these I/O challenges and introduce two online data layout reorganization approaches for achieving good tradeoffs between read and write performance. We demonstrate the benefits of using these two approaches for the ECP particle-in-cell simulation WarpX, which serves as a motif for a large class of important Exascale applications. We show that by understanding application I/O patterns and carefully designing data layouts we can increase read performance by more than 80 percent.

The scientific community is currently experiencing unprecedented amounts of data generated by cutting-edge science facilities. Soon facilities will be producing up to 1 PB/s which will force scientist to use more autonomous techniques to learn from the data. The adoption of machine learning methods, like deep learning techniques, in large-scale workflows comes with a shift in the workflow’s computational and I/O patterns. These changes often include iterative processes and model architecture searches, in which datasets are analyzed multiple times in different formats with different model configurations in order to find accurate, reliable and efficient learning models. This shift in behavior brings changes in I/O patterns at the application level as well at the system level. These changes also bring new challenges for the HPC I/O teams, since these patterns contain more complex I/O workloads. In this paper we discuss the I/O patterns experienced by emerging analytical codes that rely on machine learning algorithms and highlight the challenges in designing efficient I/O transfers for such workflows. We comment on how to leverage the data access patterns in order to fetch in a more efficient way the required input data in the format and order given by the needs of the application and how to optimize the data path between collaborative processes. We will motivate our work and show performance gains with a study case of medical applications.

The openPMD-api is a library for the description of scientific data according to the Open Standard for Particle-Mesh Data (openPMD). Its approach towards recent challenges posed by hardware and workflow heterogeneity lies in the decoupling of data description in domain sciences from concrete implementations in hardware and IO. This is reflected in the openPMD standard which defines the logical structure, but not the physical implementation of scientific data. This seminar talk gives an introduction on the openPMD standard as well as the openPMD-api. Two live demonstrations show how to write and read openPMD data in Python, and how to visualize openPMD data in the openPMD-viewer.

Ultra-high-intensity laser pulse interactions with solid density targets are of central importance for modern accelerator physics, Inertial Confinement Fusion(ICF) and astrophysics.

In order to meet the requirements of real-world applications, a deeper understanding of the underlying plasma dynamics, including plasma instabilities and acceleration mechanisms, is needed.

Due to high electron density, the over-dense target bulk is impenetrable to probes in the optical range.
Hence, several X-ray diagnostics, such as small-angle X-ray scattering (SAXS) and X-ray polarimetry, were proposed by the community.

Therefore, we bring a Monte Carlo based X-ray radiation transport module into our Particle In Cell simulation framework PIConGPU. Among others, this allows for Thompson scattering, e.g. for SAXS, and Faraday effect calculation for polarimetry - as online, in-situ diagnostics.

We investigated microwave propagation in the chiral spin soliton lattice (CSL) phase of micrometer-sized crystals of the monoaxial chiral helimagnet
CrNb₃S₆. An advantage of the CSL is that its periodicity can be reconfigured over a macroscopic length scale by means of an external magnetic field. Using a two-antenna microwave spectroscopy technique, we measured the anisotropic response of the transmitted microwaves via the spin dynamics of the CSL. When propagating along the direction parallel to the helical axis, the microwave amplitude increased up to a factor of twenty with decreasing the number of chiral soliton kinks. When the propagation direction was rotated by 90 degrees with regards to the helical axis, the microwave amplitude increased by one order of magnitude upon formation of the chiral helimagnetic order in the vicinity of zero magnetic field, exceeding that of the ferromagnetic phase above the critical field. Our findings open a novel route for controlling the characteristics of microwave propagation using noncollinear spin textures.

This thesis demonstrates a combination of three consecutive methods to achieve reliable phase classification in a 3D-image of particulate material obtained by X-ray computed tomography (CT). The method consists of 1.) sample preparation to minimise the effect of image artefacts on the CT-scan and enable the subsequent analysis with Scanning Electron Microscopy (SEM) techniques, 2.) Alignment of 2D SEM-based phase classification to a specific location in the 3D-image and 3.) implementation of the 2D-mineral classification as training data for a Convolutional Neural Network (CNN). The trained neural network enables phase segmentation of the particles in the 3D-image based on the 2D-phase classification.

Physical separation processes are best understood in terms of the behaviour of individual ore particles. Yet, while different empirical particle-based separation modelling approaches have been developed, their predictive performance has never been tested under variable process conditions. Here, we investigated the predictive performance of a state-of-the-art particle-based separation model under variable feed composition for a laboratory-scale magnetic separation of a skarn ore. Two scenarios were investigated: one in which the mass flow of the different processing streams could be measured and one in which it had to be estimated from data. In both scenarios, the predictive models were sufficiently general to predict the process outcomes of new samples of variable composition. Nevertheless, the scenario in which mass flow could be measured was ≈ 4% more precise in predicting mass balances. The process behaviour of minerals present at concentrations above 0.1 wt% could be accurately predicted. Our findings indicate the potential use of this method to minimize the costs of metallurgical testwork while providing in-depth understanding of the recovery behaviour of individual ore particles. Moreover, the method may be used to establish powerful tools to forecast mineral recoveries for partly new ore types at a running mining operation.

Smart skins and smart textiles equipped with strain sensors for motion detection are of prime significance for personalized health monitoring, lifestyle and fitness applications. Yet, the dependence of these devices on wired power supplies and rigid batteries limits their use in everyday settings. Here, we report self-powered and highly elastic strain sensors withstanding stretching to 200% for monitoring the human motion. The sensor is based on a torsional-spring-shaped coil of liquid metal wound around an elastomeric tubing and equipped with a tiny piece of a magnetic ring. The energy is harvested from the body motion relying on the Faraday’s law of electromagnetic induction when the coil is exposed to a time-varying magnetic field of the magnetic ring upon the mechanical deformation of the strain sensor. The max short-circuit current is 2mA, which is much higher than previous work, and the peak power of our device is 20 µW, sufficiently high to drive conventional low-power electronics. We demonstrate the application potential of our sensor for wearable electronics for monitoring the motion of arms and legs during fitness workout and riding bicycle. The sensor can measure motion of fingers and wrist for health applications and establish wireless control of robotic hands.

Structural templating with homogeneous template layers is one of the strategies for controlling the orientation of small molecular absorbers in the photoactive layer of an organic solar cell to increase its power conversion efficiency. A main challenge thereby is the energetic alignment of the template molecules to the photoactive and charge-transporting materials. In the present study, the effects of a cluster-like template layer of ellagic acid (EA) on the morphology and optical properties of side-chain-substituted dicyanovinyl quaterthiophene (DCV4T-Et₂) thin films are discussed. In the monolayer regime, J-aggregation of DCV4T-Et₂ is confirmed. Insertion of the EA template layer leads to an improved aggregation behavior and formation of J-aggregates in DCV4T-Et₂ films near the EA interface. The orientation of DCV4T-Et₂ molecules in 30 nm thick films changes from “edge-on” to “face-on” due to a π−π interaction between the flat-lying EA molecules and the DCV4T-Et₂ molecules. The face-on orientation by templating is preserved in blend layers with C₆₀, and a considerable increase in the crystallinity of the DCV4T-Et₂ phase in the blend is induced. Organic solar cells based on templated DCV4T-Et₂:C₆₀ active layers exhibit more than a 50% increase in the efficiency compared to nontemplated active layers. The short-circuit current density and the fill factor are significantly improved. Although the energetic alignment of EA is not ideal, no additional open-circuit voltage losses were observed with templating, due to the cluster-like morphology of the EA layer. Our results demonstrate a cluster-like templating approach with the novel template molecule EA to tailor the molecular orientation, crystallinity, and consequently optical properties of organic semiconducting molecules without significant energetic losses favorable for use in organic electronics.

We studied the acoustic properties of liquid oxygen up to 90 T by means of ultrasound measurements. We observed a monotonic decrease of the sound velocity and an asymptotic increase of the sound attenuation when applying magnetic fields. The unusual attenuation, twenty times as large as the zero-field value, suggests strong fluctuations of the local molecular arrangement.We assume that the observed fluctuations are related to a liquid-liquid transition or crossover, from a small-magnetization to a large-magnetization liquid, which is characterized by a local-structure rearrangement. To investigate higher-field properties of liquid oxygen, we performed single-turn-coil experiments up to 180 T by means of the acoustic, dilatometric, magnetic, and optical techniques. We observed only monotonic changes of these properties, reflecting the absence of the proposed liquid-liquid transition in our experimental conditions.

This release switches to C++14 as minimum required version. Transition to C++17 is planned for upcoming releases.

We extended PIConGPU with a few new solvers. Binary collisions are now available. We added arbitrary-order FDTD Maxwell's solver. All field solvers are now compatible with perfectly matched layer absorber, which became default. Yee solver now supports incident field generation using total field/scattered field technique. We added Higuera-Cary particle pusher and improved compatibility of pushers with probe species. Implementation of particle boundaries was extended to support custom positions, reflecting and thermal boundary kinds were added.

With this release, PIConGPU fully switches to openPMD API library for performing I/O. The native HDF5 and ADIOS output plugins were replaced with a new openPMD plugin. All other plugins were updated to use openPMD API. Plugins generally support HDF5 and ADIOS2 backends of openPMD API, a user can choose file format based on their installation of openPMD API. We also added new plugins for SAXS and particle merging.

We added support for HIP as a computational backend. In particular, it allows running on AMD GPUs. Several performance optimizations were added. Some functors and plugins now have performance-influencing parameters exposed to a user.

The code was largely modernized and refactored, documentation was extended.

Thanks to Sergei Bastrakov, Kseniia Bastrakova, Brian Edward Marre, Alexander Debus, Marco Garten, Bernhard Manfred Gruber, Axel Huebl, Jakob Trojok, Jeffrey Kelling, Anton Lebedev, Felix Meyer, Paweł Ordyna, Franz Poeschel, Lennert Sprenger, Klaus Steiniger, Manhui Wang, Sebastian Starke, Maxence Thévenet, Richard Pausch, René Widera for contributions to this release!

In this Letter, optical-to-terahertz (THz) conversion of 800 nm femtosecond laser pulses in large-area bias-free InGaAs emitters based on photo-Dember (PD) and lateral photo-Dember (LPD) effects is experimentally investigated. We use metamorphic buffers to grow sub-micrometer thick In𝑥Ga1−𝑥As layers with indium mole fractions 𝑥=0.37, 0.53, and 0.70 on a GaAs substrate. A strong enhancement of THz output energy with an increase of indium content is observed. On the surface of the sample providing the strongest emission (𝑥=0.7), we have fabricated a 1.5cm² area of asymmetrically shaped metallic grating for LPD emission. This LPD emitter allows achieving high conversion efficiency of 0.24⋅10−3 and a broad generation bandwidth of up to 6 THz. We also demonstrate that there is no significant difference in the conversion efficiency when operating at 1 and 200 kHz repetition rates. Our results show that large-area LPD emitters give a convenient, competitive way to generate intense high-repetition-rate THz pulses.

The Junior Research Group BioKollekt works on the development of novel peptide-based separation processes for the recycling of strategically important metals. In earlier studies, peptides with high selectivity and affinity for particles of the fluorescent powders LaPO4:Ce,Tb and CeMgAl11O19:Tb were identified using phage surface display (PSD). Nonetheless, phages are no option to be a peptide carrier in a classical industry applied separation process. However, the transitioning from phage bound peptides to free peptides proved challenging. Since most analytics are developed mainly for solution phase-chemistry, they are not fully applicable to work with fast sedimenting particles and/or within the used concentration range.
The focus of this study is the introduction of a method for testing and comparing particle-binding peptides by immobilization on glass supports. While the method itself is not dependent on fluorescence, exploiting the fluorescent properties of the target materials, as shown in Fig. 1, enables selective fluorescent scanning methods. This method, in general, allows analyzing and visualizing trends in binding efficiency, affinity and selectivity. It also helps to identify structures relevant for binding. Achieved by varying the peptide sequence this method furthermore enables relatively fast screening routines for key factors like peptide concentrations, elution methods and evaluation of binding capacities. Another part of this study is the evaluation of suitable peptide carriers for further transitioning to a working separation process. Suitable carriers under investigation are (nanocrystalline) celluloses and functionalized glass supports in various geometries. However, our current focus are superparamagnetic iron oxide nanoparticles with a bifunctional and therefore fine-tunable amphiphilic surface coating. Their unique behaviour, cheap and easy synthesis and possible recyclability during the process highlighting them as a promising type of carrier. The rather easy adjustment of the polarity to the needs of the separation process can furthermore enhance peptide-particle interactions in terms of selectivity and accessibility. While the research is still ongoing, preliminary results show promising behaviour and flexibility of the chosen systems.

Presenting results of simulations showing the density oscillation. The density oscillation describes the oscillation of the single layers in width and density in a multi layer target. We will see how the the GISAXS method allows to observe this dynmamic.

Quasi-two-dimensional (2D) sodium chloride (NaCl) crystals of various lateral sizes between graphene sheets were manufactured via supersaturation from a saline solution. Aberration-corrected transmission electron microscopy was used for systematic in situ investigations of the crystals and their decomposition under an 80 kV electron beam. Counterintuitively, bigger clusters were found to disintegrate faster under electron irradiation, but in general no correlation between crystal sizes and electron doses at which the crystals decompose was found. As for the destruction process, an abrupt decomposition of the crystals was observed, which can be described by a logistic decay function. Density-functional theory molecular dynamics simulations provide insights into the destruction mechanism, and indicate that even without account for ionization and electron excitations, free-standing NaCl crystals must quickly disintegrate due to the ballistic displacement of atoms from their surface and edges during imaging. However, graphene sheets mitigate damage development by stopping the displaced atoms and enable the immediate recombination of defects at the surface of the crystal. At the same time, once a hole in graphene appears, the displaced atoms escape, giving rise to the quick destruction of the crystal. Our results provide quantitative data on the stability of encapsulated quasi 2D NaCl crystals under electron irradiation and allow the conclusion that only high-quality graphene is suitable for protecting ionic crystals from beam damage in electron microscopy studies.

The successful characterization of high energy density (HED) phenomena in laboratories using pulsed power facilities and coherent light sources is possible only with numerical modeling for design, diagnostic development, and data interpretation. The persistence of electron correlation in HED matter is one of the greatest challenges for accurate numerical modeling and has hitherto impeded our ability to model HED phenomena across multiple length and time scales at sufficient accuracy. Standard methods from electronic structure theory capture electron correlation at high accuracy, but are limited to small scales due to their high computational cost.

In this talk I will summarize our recent efforts on devising a data-driven and physics-informed workflow to tackle this challenge. Based on first-principles data we generate machine-learning surrogate models that replace traditional electronic-structure algorithms. Our surrogates both predict the electronic structure and yield thermo-magneto-elastic materials properties of matter under extreme conditions highly efficiently while maintaining their accuracy.

Matter exposed to extreme conditions (strong electro-magnetic fields, high temperatures, and high pressures) creates high energy density (HED) phenomena which is an archetypal manifestation of a complex system.
The successful characterization of these phenomena in laboratories using pulsed power facilities and coherent light sources is possible only with numerical modeling for design, diagnostic development, and data interpretation. The persistence of electron correlation in HED matter is one of the greatest challenges for accurate numerical modeling and has hitherto impeded our ability to model HED phenomena across multiple length and time scales at sufficient accuracy. Standard methods from electronic structure theory capture electron correlation at high accuracy, but are limited to small scales due to their high computational cost.

In this talk I will summarize our recent efforts towards devising digital twins of HED phenomena. Based on first-principles data we generate machine-learning surrogate models that replace traditional electronic-structure algorithms. Our surrogates both predict the electronic structure and yield thermo-magneto-elastic materials properties of matter under extreme conditions highly efficiently while maintaining their accuracy.

The successful characterization of high energy density (HED) phenomena in laboratories using pulsed power facilities and coherent light sources is possible only with numerical modeling for design, diagnostic development, and data interpretation. The persistence of electron correlation in HED matter is one of the greatest challenges for accurate numerical modeling and has hitherto impeded our ability to model HED phenomena across multiple length and time scales at sufficient accuracy. Standard methods from electronic structure theory capture electron correlation at high accuracy, but are limited to small scales due to their high computational cost.

In this talk I will summarize our recent efforts on devising a data-driven and physics-informed workflow to tackle this challenge. Based on first-principles data we generate machine-learning surrogate models that replace traditional electronic-structure algorithms. Our surrogates both predict the electronic structure and yield thermo-magneto-elastic materials properties of matter under extreme conditions highly efficiently while maintaining their accuracy.

The successful characterization of high energy density (HED) phenomena in laboratories using pulsed power facilities and coherent light sources is possible only with numerical modeling for design, diagnostic development, and data interpretation. The persistence of electron correlation in HED matter is one of the greatest challenges for accurate numerical modeling and has hitherto impeded our ability to model HED phenomena across multiple length and time scales at sufficient accuracy. Standard methods from electronic structure theory capture electron correlation at high accuracy, but are limited to small scales due to their high computational cost.

In this talk I will summarize our recent efforts on devising a data-driven workflow to tackle this challenge. Based on first-principles data we generate machine-learning surrogate models that replace traditional electronic-structure algorithms. Our surrogates both predict the electronic structure and yield thermo-magneto-elastic materials properties of matter under extreme conditions highly efficiently while maintaining their accuracy.

The successful diagnostics of phenomena in matter under extreme conditions relies on a strong interplay between experiment and simulation. Understanding these phenomena is key to advancing our fundamental knowledge of astrophysical objects and has the potential to unlock future energy technologies that have great societal impact.
A great challenge for an accurate numerical modeling is the persistence of electron correlation and has hitherto impeded our ability to model these phenomena across multiple length and time scales at sufficient accuracy.
In this talk, I will summarize our recent efforts on devising a data-driven workflow to tackle this challenge. Based on first-principles data we generate machine-learning surrogate models that replace traditional electronic-structure algorithms. Our surrogates both predict the electronic structure and yield thermo-magneto-elastic materials properties of matter under extreme conditions highly efficiently while maintaining their accuracy. This opens up the path towards multiscale materials modeling for matter under ambient and extreme conditions at a computational scale and cost that is unattainable with current algorithms.

The successful diagnostics of phenomena in matter under extreme conditions relies on a strong interplay between experiment and simulation. Understanding these phenomena is key to advancing our fundamental knowledge of astrophysical objects and has the potential to unlock future energy technologies that have great societal impact.
A great challenge for an accurate numerical modeling is the persistence of electron correlation and has hitherto impeded our ability to model these phenomena across multiple length and time scales at sufficient accuracy.
In this talk, I will present a solution to this problem in terms of a data-driven modeling framework for matter under extreme conditions – the Materials Learning Algorithms (MALA) package. MALA generates surrogate models based on deep neural networks that reproduce the output of state-of-the-art electronic structure methods at a fraction of the computational cost. This opens up the path towards multiscale materials modeling for matter under ambient and extreme conditions at a computational scale and cost that is unattainable with current algorithms.
MALA is jointly developed by the Center for Advanced Systems Understanding (CASUS), Sandia National Laboratories (SNL), and Oak Ridge National Laboratory (ORNL).

The successful diagnostics of phenomena in matter under extreme conditions relies on a strong interplay between experiment and simulation. Understanding these phenomena is key to advancing our fundamental knowledge of astrophysical objects and has the potential to unlock future energy technologies that have great societal impact.
A great challenge for an accurate numerical modeling is the persistence of electron correlation and has hitherto impeded our ability to model these phenomena across multiple length and time scales at sufficient accuracy.
In this talk, I will present a solution to this problem in terms of a data-driven modeling framework for matter under extreme conditions – the Materials Learning Algorithms (MALA) package. MALA generates surrogate models based on deep neural networks that reproduce the output of state-of-the-art electronic structure methods at a fraction of the computational cost. This opens up the path towards multiscale materials modeling for matter under ambient and extreme conditions at a computational scale and cost that is unattainable with current algorithms.
MALA is jointly developed by the Center for Advanced Systems Understanding (CASUS), Sandia National Laboratories (SNL), and Oak Ridge National Laboratory (ORNL).

The successful characterization of high energy density (HED) phenomena in experimental facilities is possible only with numerical modeling. The persistence of electron correlation in HED matter is one of the greatest challenges for accurate numerical modeling and has hitherto impeded our ability to model HED phenomena across multiple length and time scales at sufficient accuracy. Standard methods from electronic structure theory (density functional theory) capture electron correlation at high accuracy, but are limited to small scales due to their high computational cost.
In this talk, I will present a solution to this problem in terms of a data-driven modeling framework for matter under extreme conditions – the Materials Learning Algorithms (MALA) package. MALA generates surrogate models based on deep neural networks that reproduce the output of density functional theory calculations at a fraction of the computational cost. This opens up the path towards multiscale materials modeling for matter under ambient and extreme conditions at a computational scale and cost that is unattainable with current algorithms.
MALA is modular and open source. It enables users to perform the entire modeling toolchain using only a few lines of code. MALA is jointly developed by the Center for Advanced Systems Understanding (CASUS), Sandia National Laboratories (SNL), and Oak Ridge National Laboratory (ORNL).

Computational tools to study thermodynamic properties of magnetic materials have, until recently, been limited to phenomenological modeling or to small domain sizes limiting our mechanistic understanding of thermal transport in ferromagnets.
Herein we study the interplay of phonon and magnetic spin contributions to the thermal conductivity in $\alpha$-iron utilizing non-equilibrium molecular dynamics simulations.
It was observed that the magnetic spin contribution to the total thermal conductivity exceeds lattice transport for temperatures up to two-thirds of the Curie temperature after which only strongly coupled magnon-phonon modes become active heat carriers.
Characterizations of the phonon and magnon spectra give a detailed insight into the coupling between these heat carriers, and the temperature sensitivity of these coupled systems.
Comparisons to both experiments and \textit{ab initio} data support our inferred electronic thermal conductivity, supporting the coupled molecular dynamics/spin dynamics framework as a viable method to extend the predictive capability for magnetic material properties.

Ultra-intense short-pulse lasers in the Petawatt regime and intensity range of 1021W/cm2 offer the possibility to study new compact accelerator schemes by utilizing solid density targets for the generation of energetic ion beams. The optimization of the acceleration process demands comprehensive exploration of the involved plasma dynamics. This applies not only on the femtosecond but also on the pico- to nanosecond timescale, where the laser rising edge modifies the target prior to the 30 fs laser peak. Cryogenic hydrogen jet targets with µm-scale transverse size and solid density (5.2x1022 cm-3) offer the superb opportunity for renewable and debris-free acceleration sources and at the same time allow for comprehensive experimental investigation and realistic simulation of the rich physics involved in the laser target interaction.
Here, we present the results of an experiment for laser proton acceleration from a cryogenic hydrogen jet target at the DRACO-PW laser. Optimized acceleration performance is achieved by tailoring the targets plasma density via hydrodynamic expansion induced by a short low-intensity pre-pulse. Optical shadowgraphy probing is utilized to give a realistic input of the targets plasma density for 3 dimensional particle-in-cell simulations of the particle acceleration process.

High-intensity short-pulse lasers enable novel compact accelerator schemes for the generation of energetic ion beams. The experimental investigation of the process remains challenging due to the femtosecond timescale and micrometer size of the acceleration. Commonly, diagnostic results are explained by a comparison of the experimental findings with computationally expensive particle-in-cell simulations. Cryogenic hydrogen jet targets (~30 critical densities) with µm-scale transverse size are particularly well suited for this approach. Time-resolved diagnostics like optical probing can infer the state of the target at the initialization time of the simulation and benchmark the simulation results. Here we present the implementation of an off-harmonic optical probing setup at an experiment for laser proton acceleration with a cylindrical hydrogen jet target at the DRACO PW laser with plasma-mirror cleaned laser contrast. We show under which conditions the technique overcomes the problem of parasitic plasma self-emission, present technical aspects of the off-harmonic probing technique together with experimental results of the observed plasma dynamics.

The recovery of critical raw materials from a complex waste of electrical and electronic equipment is becoming an increasingly important issue in the transfer towards a more sustainable economy. The main challenges are to separate fine particles, with sizes below 10 microns, in a highly selective and feasible process. In this context, short peptide chains with a high selectivity for inorganic surfaces have the potential to play a key role in innovative particle separation processes.

Using phage surface display, our team has identified peptide sequences that selectively bind to the surface of valuable Rare Earth Element containing phosphors present in compact fluorescent lamps. Subsequently, the biomolecules were chemically immobilized on the surface of superparamagnetic carriers, including magnetic nanoparticles and micron-sized composite beads, to render these particles selectivity for the targeted phosphors. Separation experiments of virgin and end-of-life phosphors were performed in a high-gradient magnetic-separator that allows for a high-throughput process, which can be scaled up readily.

The next goal is to be able to tune the interaction of reusable peptide-functionalized superparamagnetic carriers with the target phosphors, to allow for an integrated magnetic separation process with rapid cycles of: 1) target-carrier sorption 2) separation of target from nontarget particles 3) target-carrier desorption and 4) carrier recovery. Currently, we are further investigating the nature of the peptides’ selective interactions with the phosphors’ surfaces, by means of isothermal titration calorimetry, binding experiments in different media and zetapotential measurements.

Hence, we are working towards a proof-of-concept for the recovery of currently not recyclable fine particles, by applying bio-inspired surfaces to allow for a higher selectivity and lower process cost than conventional separation methods.

Due to the non-linear nature of relativistic laser induced plasma processes, the development of laser-plasma accelerators requires precise numerical modeling. Especially high intensity laser-solid interactions are sensitive to the temporal laser rising edge and the predictive capability of simulations suffers from incomplete information on the plasma state at the onset of the relativistic interaction. Experimental diagnostics utilizing ultra-fast optical backlighters can help to ease this challenge by providing temporally resolved inside into the plasma density evolution. We present the successful implementation of an off-harmonic optical probe laser setup to investigate the interaction of a high-intensity laser at 5.4E21 W / cm^2 peak intensity with a solid-density cylindrical cryogenic hydrogen jet target of 5 um diameter as a target test bed. The temporal synchronization of pump and probe laser, spectral filtering and spectrally resolved data of the parasitic plasma self-emission are discussed. The probing technique mitigates detector saturation by self-emission and allowed to record a temporal scan of shadowgraphy data revealing details of the target ionization and expansion dynamics that were so far not accessible for the given laser intensity. Plasma expansion speeds of up to (2.3+-0.4)E7 m / s followed by full target transparency at 1.4 ps after the high intensity laser peak are observed. A three dimensional particle-in-cell simulation initiated with the diagnosed target pre-expansion at -0.2 ps and post processed by ray tracing simulations supports the experimental observations and demonstrates the capability of time resolved optical diagnostics to provide quantitative input and feedback to the numerical treatment within the time frame of the relativistic laser-plasma interaction.

This contribution is an overview of the latest advances in image processing made by the authors, as well as recent and potential applications, including magnetohydrodynamic systems. Contemporary research of two-phase liquid metal flow requires state of the art experimental methods to probe downscaled representative systems. Among these methods are dynamic neutron and X-ray radiography which have applications in studying bubble flow in liquid metal affected by applied magnetic field [1], bubble collective dynamics in Hele-Shaw liquid metal cells [2], particle flow in liquid metal channels [3] and mesoscale solidification of metals under applied magnetic field [4]. Pushing the boundaries, these measurements are inevitably performed under adverse conditions: low signal-to-noise ratio owing to high frame rates (relative to the source flux) required to capture the physics of interest, but also low image resolution, image artefacts, scattering, etc. Therefore, to extract meaningful information from experimental data and study the underlying processes, appropriate image processing methods and tools are required. Development of such tools is also motivated by very limited (for most researchers) access to high-end neutron and X-ray imaging setups and thus one must make the most of data acquired during limited beamtimes. Examples of applications of our solutions to experimental data from neutron and X-ray imaging are shown in Figures 1-4.

The talk gives an overview about new bio-based tools for metal recycling.

Introduction: Anatomical changes during proton therapy can cause severe dosimetric deviation. Treatment verification is thus highly desirable. Here, we present the first systematic evaluation of the sensitivity of a Prompt-Gamma-Imaging (PGI) based range verification system to detect anatomical changes in prostate-cancer treatments.

Materials and Methods: Spot-wise range deviations were monitored with a PGI slit camera during in total 16 fractions of hypo-fractionated Pencil-Beam-Scanning (PBS) prostate-cancer treatments (2 patients, 2 fields, each 1.5GyE). For all monitored fractions, in-room control-CT scans were acquired, serving as ground-truth reference for the identification and scoring of anatomical changes (strong/moderate/light). The sensitivity to detect these changes was determined for both, clinically measured and simulated PGI-data, respectively: For distal PBS spots, expected shifts, determined from line-dose profiles (planning-CT vs. control-CT), were manually compared with PGI-derived spot-wise shifts (Fig.1). Furthermore, a simple two-parametric model was established to classify each monitored field into scenarios of global, local and no-clinically-relevant anatomical changes.

Results: Overall 66% (84%) of the 64 detected anatomical changes were identified from measured (simulated) PGI-data (Fig.2a). All strong changes (14/64) were identified correctly. The first attempt for automated field-wise classification was able to correctly classify most global changes (9/11). However, differentiation between non-relevant from local changes seemed more difficult (4/6 and 7/14 fields classified correctly, respectively); but even ground-truth classification was often borderline in those cases (Fig.2b).

Conclusion: In the first systematic investigation of the sensitivity of clinical PGI-based treatment verification, its capability to detect strong anatomical changes has been clearly demonstrated. Moving towards automated interpretation of PGI-data, a simple two-parametric model already showed encouraging results.

The alpaka library is a header-only C++14 abstraction library for accelerator development. Its aim is to provide performance portability across accelerators through the abstraction (not hiding!) of the underlying levels of parallelism.

The formation of freckle defects in the presence of an external magnetic field is studied by combining *in situ* synchrotron imaging with numerical simulations. The formation, growth and motion of freckle channels during directional solidification are investigated in a Hele-Shaw cell for a low melting point Ga-In alloy. The solidification cell is placed in a permanent magnet system providing a flux density of about 120 mT within the cell. A parallel numerical study, using a microscopic parallelized Cellular Automata lattice Boltzmann method, is validated by these *in situ* experiments. An excellent match between the numerical model and experiments conducted on thin rectangle sample alloy is achieved. Evaluation of the *in situ* X-ray data and numerical analysis shows the role of thermoelectric magnetohydrodynamics (TEMHD) and electromagnetic damping (EMD) in the formation of channels and ultimately freckle defects. The channel motion can be attributed to the thermoelectric Lorentz force acting on the inter-dendritic liquid flow by causing the solute to accumulate at one side of the cell. *In situ* synchrotron experiments allow us to resolve the complex channel dynamics and simultaneously show how large-scale flow fields may alter it. Both temperature gradient and grain orientation can affect the dynamics of the segregation channels formation in the presence of the magnetic field. The effect of electromagnetic damping force to convective transport needs future investigations. The *in situ* synchrotron data and numerical modelling will provide further understanding of the underlying mechanisms and identifying further interesting phenomena.

The structural and magnetic properties of the (R,R’)₂Fe₁₇-type (R and R’ are heavy (Ho, Er) and light (Sm) rare earth metals, respectively) compounds and the (R,R’)₂Fe₁₇Hy hydrides are reported. The hydrides with a high hydrogen concentration (y ≥ 4) are obtained by direct hydrogen absorption by the intermetallics. The rhombohedral Th₂Zn₁₇-type of structure is inherent in the Sm containing parent compounds and hydrides. The unit cell increase and increased Curie temperature are characteristic of the hydrides. Magnetic properties of the samples are investigated in pulsed magnetic fields up to 60 T. Strong magnetic fields induce phase transitions in the compounds with a high content of heavy rare earth elements. The parameter of the intersublattice exchange interaction is estimated.

This work investigates the feasibility of using contactless electromagnetic sonication for dispersing and refining a strengthening phase in iron and steel for ferrous metal matrix composite (MMCs) production. An oscillating pressure field is generated using superposition of alternating (0.13 T) and steady magnetic fields (up to 8 T). The processing employs a static floating-zone technique and is crucible-free. This approach allows to reach around 0.8 MPa pressure oscillation amplitude that is sufficient to initiate acoustic cavitation in high melting temperature liquid metals. The viability of various reinforcement dispersion, both ex and in situ, using this electromagnetic sonication is explored in the context of oxide dispersion strengthened (ODS) steel and high modulus steel (HMS) production.

During directional solidification, segregation of alloying components may lead to channels of segregate forming, called ‘freckle’. This work aims to understand how the application of a magnetic field effects freckle formation.
Using synchrotron X-ray imaging we quantify the influence of magnetic field strength and orientation on the formation, growth and motion of freckle channels and the resulting suppression of the solidification temperature.By increasing the Bz magnetic component (perpendicular to the sample surface, freckle channels can be driven to one side and suppressed elsewhere, giving a freckle free region in otherwise difficult to cast alloys. The freckle motion can be attributed to the thermoelectric Lorentz force acting on the liquid, changing inter-dendritic liquid flow by causing the solute-enriched liquid to accumulate at one side, biasing channel motion. In the bulk liquid electromagnetic damping force prevails, decreasing the solidification temperature.

Access to primary and secondary raw materials, particularly critical raw materials (CRM), is a prerequisite for a transition to a climate-neutral and circular economy and a successful delivery of the United Nations sustainable development goals (Ali et al., 2017). Resourceand energy-efficient exploration technologies such as hyperspectral imaging will aid discovery and evaluation of ore deposits, and thus contribute to sustaining a supply of CRM. Hyperspectral imaging provides fast, highresolution, spatially continuous, and non-destructive mapping of minerals along drill cores. This spectroscopic technique uses the spectral reflectance and emission measured in hundreds of contiguous spectral bands to provide information about geological materials based on electronic and vibrational processes of bonded elements within minerals. Below we demonstrate the application of hyperspectral drill core scanning for the identification and mapping of CRMs using a case study of Li-bearing pegmatites in Uis, Namibia.

Overview over DAPHNE-related activities at HZDR

Asymmetries in resonant SAXS

Targets for pump probe experiments at upcoming XFEL facilities

We performed simulations for CPA stretcher compressor systems with measured aberrated optic surfaces. These simulations were developed to understand and further optimize the temporal pulse structure of laser pulses which were stretched and compressed with existing and planned stretcher compressor systems.

Within the framework of the ATHENA project, we are developing a high-contrast frontend to generate laser pulses with a center wavelength of 1030 nm, a
FWHM-bandwidth of 25 nm and an output energy of 100 μJ. The system is based on chirped pulse amplification with subsequent crossed polarized wave generation [1]. The generated laser pulses will in future be used as seed pulses at the PEnELOPE [2] laser facility at HZDR.

The Morelos district (Guerrero, Mexico) hosts several Au-Cu skarn deposits, i.e., the El Limon, Guajes and Media Luna deposits, that are centered around the El-Limon granodiorite. The three skarn deposits comprise a total Au endowment of 8.8 Moz at an average grade of 3.2 g/t (all classes). In addition, the Media Luna deposit (south) comprises 20.9 Mt at 1.07 wt.% Cu. This different metal tenor (Au-Cu vs. Au), has been interpreted to be related to higher Mg-concentrations in the sedimentary protolith at Media Luna.
Forty-two samples from eleven drill-cores from the El Limon (north) and Media Luna (south) deposits were selected along systematic depth profiles, including skarn samples in proximal, intermediate and distal position to the intrusion. The prograde skarn mineral association is characterized by mainly garnet, pyroxene and magnetite, whereas retrograde skarn minerals comprise amphibole, calcite and sulfide minerals (mainly pyrrhotite, arsenopyrite and chalcopyrite). Preliminary petrographic analyses of selected drill-core material reveal a systematic variation in garnet color from dark brown/red in proximal endo/exoskarn to green in intermediate exoskarn to pale green in distal marble-hosted garnet nodules.
After detailed mineralogical characterization (optical and scanning electron microscopy), we are planning to analyze major and trace element compositions of skarn-related garnet by electron microprobe analysis and laser ablation – inductively coupled- mass spectrometry. The goal of this study is to constrain trace element systematics in the prograde skarn stage along the fluid flow path as well as to assess the influence of different host rock compositions on garnet trace element chemistry.

Laser plasma based particle accelerators have attracted great interest in fields where conventional accelerators reach limits based on size, cost or beam parameters. Designing future laser ion accelerators requires a high degree of control of the advanced acceleration schemes involved and predictive modelling capabilities. Here, we investigate the interaction of petawatt class laser pulses with a micrometer sized cryogenic hydrogen jet target. Controlled pre-expansion of the target by low intensity pre-pulses allowed for tailored density scans over more than two orders of magnitude. On-shot target characterization using two-color optical probing provided precise starting conditions for numerical simulations. For the optimal target density profile in the near critical regime proton energies of up to 80 MeV were observed which represents a two-fold increase with respect to the initial solid target case. Three-dimensional particle in cell simulations confirmed the transition between different acceleration mechanisms involved and suggested the magnetic-vortex regime to be responsible for the highest proton energies achieved.

Cryogenic jet targets are attracting growing attention in scientific fields investigated at x-ray free electron laser and high power laser facilities, e.g. in high energy density science exploring extreme states of matter or in relativistic laser plasma interactions aiming to develop novel ion accelerators.
A key advantage of cryogenic target is the unique capability to deliver ultra-pure samples at solid density using chemical elements that under ambient conditions exist in the gas phase (e.g. hydrogen) and to generate replenishing, free-standing, debris free targets at high repetition rates.
Novel sheet-like jets geometries enable wider target with adjustable thickness, which is e.g. of interest for the study of different acceleration mechanisms in laser-plasma experiment.
In this talk, we present experimental studies on the characterization of sheet jets for different nozzle types and under various operation conditions. Unlike cylindrical geometries, the shape of the planar target is significantly altered by surface tension until the liquid motion is stopped by solidification. The underlining fluid dynamics and thermodynamics are discussed and compared with computational fluid dynamic simulations.

The research of interactions between the pathogens and their hosts is key for understanding the biology of infection. Commencing on the level of individual molecules, these interactions define the behavior of infectious agents and the outcomes they elicit. Discovery of host-pathogen interactions (HPIs) conventionally involves a stepwise laborious research process. However novel computational approaches including machine learning and deep learning allow to significantly accelerate the discovery process, particularly for rich information sources like microscopy. One example of such approaches includes an algorithm we have devised to detect interactions between intracellular Toxoplasma gondii parasites and the host cell innate immune response molecules with high accuracy from micrographs obtained in high-content fashion. In another example, it was possible to detect intracellular and extracellular poxvirus virions in 3D superresolution micrographs without specific immunohistochemical labelling. This was possible through transfer-learning-enabled deep learning model inference from seemingly irrelevant fluorescence channels, allowing to distinguish minute changes in virus particle signal upon internalization. Finally, bringing temporal dimension as a source of information for deep learning algorithms allows predicting infection outcomes in a population of infected and uninfected host cells employing time-lapse microscopy data. Altogether, these examples suggest a great potential for HPI analysis using novel machine learning.

Conventional mineral exploration methods are usually based on extensive field work supported by geophysical surveying. These techniques can be restricted by field accessibility, financial status, area size and climate. Additionally, these methods can have a considerable footprint on the environment, upsetting the surrounding community and resulting in mistrust in the exploration sector. To tackle these challenges, we propose to use UAVs during the exploration phase in order to decrease conventional restrictions. The rise in expertise and new sensor technology have allowed geoscientists to apply a wide range of optical remote sensing methods for exploration. Light-weight UAVs have shown to be a fast and flexible tool in aerial surveying for geological purposes (e.g. Madjid et al., 2018; Xiang et al., 2018).
Furthermore, the addition of UAV-based hyperspectral data can improve the accuracy of field mapping and provide the needed information of the geochemical make-up of outcropping deposits for future exploration campaigns (e.g. Booysen et al., 2020). UAV-based surveying can supplement and direct geological observation immediately during fieldwork and thus allow for a better integration with in-situ ground measurements. In particular, such systems are beneficial for inaccessible and remote areas with little infrastructure because they allow for a systematic, dense and non-invasive surveying that might not be possible with ground-based techniques. In this contribution, we will present the various UAVs and payloads used in our field campaigns that acquired data for geological purposes.

Technological advancements have led to an increased demand for rare earth elements (REEs), resulting in them becoming crucial to ensure the transformation towards green technologies (Dutta et al., 2016). At the same time, the world’s REE supply is monopolized by a limited number of countries (Simandl, 2014). A renewed and sustainable exploration approach is required to ensure a more ethical and global supply of REEs. For this, we suggest the use of a multi-scale and multi-source remote sensing workflow. Conventional exploration methods typically involve extensive field work and geochemical lab work supported by geophysical surveying. This approach, however, can be constrained by several factors such as the area size and accessibility, climate, finances as well as the lack of a social licence to operate. The suggested multi-scale, multi-source approach uses optical remote sensing data from multiple platforms with increasing spatial resolution. We utilize multispectral satellite data, plane-based hyperspectral data, unmanned aerial vehicle (UAV)-based hyperspectral data and validate the findings with in-situ measurements. Our suggested method can mitigate the disadvantages of traditional exploration techniques by allowing for fast, systematic and dense surveying, particularly in inaccessible and remote areas with little infrastructure. Additionally, a generally less-invasive technique garners a higher social acceptance for the mining and exploration community. We demonstrate our approach on a few, selected carbonatite complexes in southern Africa: the Marinkas Quellen carbonatite complex and the Lofdal carbonatite complex in Namibia.

How can we shape the future of mineral exploration in the EU? And how did the Horizon 2020 project INFACT, which stands for innovative, non-invasive and fully acceptable exploration technologies contribute to move towards a sustainable and socially acceptable exploration in the EU?

Warm dense matter (WDM) is an interdisciplinary field between plasma physics, condensed matter physics, high pressure science, inertial confinement fusion, planetary science, and materials science under extreme conditions [1-3]. Therefore, WDM is a complex regime to which neither ordinary condensed matter theory nor plasma theory are applicable. Due to relatively well developed theoretical and computational methods for homogeneous states, most of the initial studies were focused on uniform WDM. However, recent introduction of THz lasers [4], the novel seeding technique to reach high intensities [5], and laser pumping of a sample with a predesigned periodic grating structure [6] allows us to generate inhomogeneous states. Therefore, in this talk the results will be presented for collective oscillations in inhomogeneous WDM. Additionally, the non-linear density response of electrons and applicability of various exchange-correlation functionals such as LDA, GGA, and meta-GGA will be discussed. The analysis of the quality of the KS-DFT approach based on different exchange-correlations functionals is performed by comparing to QMC data. Finally, the quantum fluid theory of inhomogeneous quantum electrons will be presented. The results on the first ab inito study of the many-fermion Bohm field will be shown. The latter has impact going well beyond WDM, since it is key quantity of Bohmenian quantum mechanics.

[1] A. Ng, IEEE International Conference on Plasma Science (Cat. No.02CH37340),
2002, pp. 163-, doi: 10.1109/PLASMA.2002.1030367.
[2] F. Graziani, M. P. Desjarlais, R. Redmer, and S. B. Trickey, Frontiers and Chal-
lenges in Warm Dense Matter (Springer, 2014).
[3] V. E. Fortov,Extreme States of Matter (Springer, Heidelberg, 2016).
[4] B.K. Ofori-Okai, et al., J. Inst13, P06014 (2018).
[5] T Kluge, et al., Phys. Rev. X 8, 031068 (2018)

Warm dense matter (WDM) is a state of matter covering parameter space between solids and dense plasmas, and is characterized by the simultaneous relevance of electronic quantum degeneracy, thermal excitations, and strong inter-particle correlations. WDM is an interdisciplinary field between plasma physics, condensed matter physics, high pressure science, inertial confinement fusion, planetary science, and materials science under extreme conditions [1-3]. Therefore, WDM is a complex regime to which neither ordinary condensed matter theory nor plasma theory are applicable. In particular, a highly ionized WDM state is closely related to a non-ideal dense plasma state. Parameters of relevant WDM experiments are shown in Fig.1 along with other types of plasma states. Due to relatively well developed theoretical and computational methods for homogeneous states, most of the initial studies were focused on uniform WDM. However, recent introduction of THz lasers [4], the novel seeding technique to reach high intensities [5], and laser pumping of a sample with a pre-designed periodic grating structure [6] allows us to generate inhomogeneous states. Therefore, this talk will be focused on inhomogeneous states.
In particular, periodically inhomogeneous WDM is considered. The results will be presented for collective oscillations in such systems. It will be shown that such interesting features like double plasmon and optical mode appear in electronic density excitation spectra. Additionally, the non-linear density response of electrons and applicability of various exchange-correlation functionals such as LDA, GGA, and meta-GGA will be discussed. The analysis of the quality of the KS-DFT approach based on different exchange-correlations functionals is performed by comparing to QMC data. Finally, the quantum fluid theory of inhomogeneous quantum electrons will be presented. The results on the first ab inito study of the many-fermion Bohm field will be shown. The latter has impact going well beyond WDM, since it is key quantity of Bohmenian quantum mechanics.

[1] A. Ng, IEEE International Conference on Plasma Science (Cat. No.02CH37340),
2002, pp. 163-, doi: 10.1109/PLASMA.2002.1030367.
[2] F. Graziani, M. P. Desjarlais, R. Redmer, and S. B. Trickey, Frontiers and Chal-
lenges in Warm Dense Matter (Springer, 2014).
[3] V. E. Fortov,Extreme States of Matter (Springer, Heidelberg, 2016).
[4] B.K. Ofori-Okai, et al., J. Inst13, P06014 (2018).
[5] T Kluge, et al., Phys. Rev. X 8, 031068 (2018).

Mineral exploration in the West Greenland flood basalt province is attractive because of its resemblance to the magmatic sulphide-rich deposit in the Russian Norilsk region, but it is challenged by rugged topography and partly poor exposure for relevant geologic formations. On northern Disko Island, previous exploration efforts have identified rare native iron occurrences and a high potential for Ni-Cu-Co-PGE-Au mineralization. However, Quaternary landslide activity has obliterated rock exposure at many places at lower elevations. To augment prospecting field work under these challenging conditions, we acquire high-resolution magnetic and optical remote sensing data using drones in the Qullissat area. From the data, we generate a detailed 3D model of a mineralized basalt unit, belonging to the Asuk Member (Mb) of the Palaeocene Vaigat formation.

A wide range of legacy data and newly acquired geo- and petrophysical, as well as geochemical-mineralogical measurements form the basis of an integrated geological interpretation of the unoccupied aerial system (UAS) surveys. In this context, magnetic data aims to define the location and the shape of the buried magmatic body, and to estimate if its magnetic properties are indicative for mineralization. High-resolution UAS-based multispectral orthomosaics are used to identify surficial iron staining, which serve as a proxy for outcropping sulphide mineralization. In addition, high-resolution UAS-based digital surface models are created for geomorphological characterisation of the landscape to accurately reveal landslide features.

UAS-based magnetic data suggests that the targeted magmatic unit is characterized by a pattern of distinct positive and negative magnetic anomalies. We apply a 3D magnetization vector inversion model (MVI) on the UAS-based magnetic data to estimate the magnetic properties and shape of the magmatic body. By means of using constraints in the inversion, (1) optical UAS-based data and legacy drill cores are used to assign significant magnetic properties to areas that are associated with the mineralized Asuk Mb, and (2) the Earth’s magnetic and the paleomagnetic field directions are used to evaluate the general magnetization direction in the magmatic units.

Our results indicate that the geometry of the mineralized target can be estimated as a horizontal sheet of constant thickness, and that the magnetization of the unit has a strong remanent component formed during a period of Earth’s magnetic field reversal. The magnetization values obtained in the MVI are in a similar range as the measured ones from a drillcore intersecting the targeted unit. Both the magnetics and topography confirm that parts of the target unit were displaced by landslides. We identified several fully detached and presumably rotated blocks in the obtained model. The model highlights magnetic anomalies that correspond to zones of mineralization and is used to identify outcrops for sampling.

Our study demonstrates the potential and efficiency of using multi-sensor high-resolution UAS data to constrain the geometry of partially exposed geological units and assist exploration targeting in difficult, poorly exposed terrain.

Ambient seismic noise tomography is a novel, low-impact method to investigate the Earth’s structure. While most passive seismic studies focus on structures at crustal scales, there are only few examples of this technique being applied in a mineral exploration context. In this study, we performed an ambient seismic experiment to ascertain the relationship between the shallow shear wave velocity and mineralized zones in the Erzgebirge in Germany, one of the most important metal provinces in Europe. Late Variscan mineralized greisen and veins occurring in the Geyer-Ehrenfriedersdorf mining district of the Central Erzgebirge were mined from medieval times until the end of the 19th century. These occurrences represent a significant resource for commodities of high economic importance, such as tin, tungsten, zinc, indium, bismuth and lithium. Based on ambient noise data from a dense "LARGE-N" network comprising 400 low-power, short-period seismic stations, we applied an innovative tomographic inversion technique based on Bayesian statistics (transdimensional, hierarchical Monte Carlo search with Markov Chains using a Metropolis/Hastings sampler) to derive a three-dimensional shear wave velocity model. An auxiliary 3D airborne time-domain electromagnetic dataset is used to provide additional insight into the subsurface architecture of the area. The velocity model shows distinct anomalies down to approximately 500 m depth that correspond to known geological features of the study area, such as (a) gneiss intercalations in the mica schist-dominated host rock, imaged by a SW-NE striking low velocity zone with a moderately steep northerly dip, and (b) a NW-trending strike-slip fault, imaged as a subvertical linear zone cross-cutting and offsetting this low velocity domain. Similar to the velocity data, the electromagnetic data exhibit north-dipping (high-conductivity) structures in the mica schists, corresponding to the strike and dip of the predominant metamorphic fabric. An unsupervised classification performed on the bivariate 3D dataset yielded nine spatially coherent classes, one of which shows a high correspondence to drilled greisen occurrences in the roof zone of a granite pluton. The relatively high mean shear velocity and resistivity values of this class could be explained by changes in density and composition during greisen formation as observed in other areas of the Erzgebirge. Our study demonstrates the great potential of the cost-efficient and low-impact ambient noise technology for mineral exploration, especially when combined with other independent geophysical datasets.

Natural test sites are resource-intensive and often limited to single industries or technologies. Drawing upon two strands of research into technology development and innovation strategies, the research question in this paper investigates how converging test sites may provide opportunities for multiple industries and regions. The paper analyzes multi-industrial test sites regarding, (i) the requirements of the social and physical environment, logistic requirements, as well as technical requirements, (i) the added value for technology developers, as well as, (iti) the absorptive capacity of the region. Qualitative and quantitative research designs were adopted to analyze multi-industrial test sites. The results indicate that the suitability of multiindustrial test sites depends on the market and research fit of the test target, the quality of the benchmark data, as well as logistical, organizational, legal, social, and ecological factors. The study shows that multi-industrial test sites increase and strengthen the absorptive capacity of regions. Additionally, the study discusses managerial and political implications of multiindustrial test sites. Until now corporate and public test site practices have received only scant recognition in technology management literature, a gap closed by this paper.

Geological mapping in difficult-to-access terrain such as open pit mines often relies on remotely sensed data. Hyperspectral data yield valuable geological information, especially when spectral ranges of multiple sensors are used in conjunction. In this contribution we project a number of hyperspectral datasets of an open pit mine covering the visible to near-infrared (VNIR), short-wave infrared (SWIR), and long-wave infrared (LWIR) range from airborne, drone-borne and ground-based acquisitions into a photogrammetric point cloud. The resulting hyperspectral digital outcrop is then used as a basis for data integration in a 3D environment. To discriminate geologic materials in the pit we apply a Gaussian deconvolution to identify the position of diagnostic absorption features in the SWIR and LWIR, and then apply a support vector machine-based classification. Our results agree with known lithologic units and alteration patterns and can be used to guide exploration targeting and mine planning.

Das Stranggießen von Stahl ist mit 96 % Marktanteil das weltweit wichtigste Verfahren zur Stahlherstellung. Im Gießprozess beeinflusst das Strömungsprofil in der Kokille entscheidend die Qualität des erstarrten Stahls. Um eine möglichst optimale Kokillenströmung einzustellen, werden Aktuatoren eingesetzt, die die sich bewegende Schmelze kontaktlos mithilfe der Lorentzkraft beeinflussen. Diese Aktuatoren würden auch eine Strömungsregelung ermöglichen, wenn eine geeignete Messtechnik für heiße Schmelzen vorhanden wäre. Allerdings lösen bislang verfügbare Messverfahren vor allem die Strömung im Randgebiet der Kokille auf und sind häufig in ihrer zeitlichen Auflösung limitiert. Eine neue infrage kommende Messtechnik ist die berührungslose induktive Strömungstomographie (contactless inductive flow tomography, CIFT), die aus der gemessenen strömungsinduzierten Verzerrung eines angelegten Magnetfeldes die dreidimensionale Strömung rekonstruieren kann. In dieser Arbeit wird anhand eines 1:8-Labormodells einer Stranggusskokille und numerischen Simulationen untersucht, ob CIFT bei Anlagen mit elektromagnetischen Bremsen eingesetzt werden kann. Besondere Herausforderungen entstehen aufgrund der Verzerrung des CIFT-Anregungsmagnetfeldes durch die ferromagnetische Bremse, der großen Dynamik von 6 Größenordnungen zwischen dem Magnetfeld der Bremse und dem strömungsinduzierten Magnetfeld sowie intrinsischen Strömungsoszillationen mit einer charakteristischen Frequenz im Bereich der üblicherweise verwendeten CIFT-Anregungsfrequenzen. Es wird dargelegt, dass sich CIFT in derartigen Aufbauten einsetzen lässt, wenn (a) eine geeignete Anregungsmagnetfeldstruktur erzeugt werden kann, (b) gradiometrische Induktionsspulen als Magnetfeldsensoren eingesetzt werden und (c) die Anregungsfrequenz in einem optimalen, schmalen Bereich gewählt wird. Diese Messungen werden erst durch in dieser Arbeit dargelegte theoretische und experimentell validierte Analysen der Induktionsspulen möglich, wofür Schwerpunkte auf deren Modellierung, Design und Messunsicherheit gelegt wurden. Außerdem werden für dieses Stranggussmodell erstmals experimentelle Ergebnisse mit horizontal anstatt vertikal orientierten Anregungsmagnetfeldern präsentiert. Um die Skalierbarkeit von CIFT in Richtung industrieller Anlagen zu demonstrieren, werden zum einen CIFT-Strömungsrekonstruktionen in einem heißen 1:2-Labormodell einer Kokille vorgestellt. Eine weitere Herausforderung für CIFT ist die in industriellen Kokillen typischerweise aufgebrachte ferromagnetische Nickelschicht, die eine verzerrende und abschirmende Wirkung auf umgebende Magnetfelder hat. Diese Beschichtung stellt aufgrund ihrer zeitlich und räumlich schwankenden Permeabilität eines der größten Hindernisse für die Anwendung von CIFT im industriellen Stahlguss dar. Die Auswirkung dieser Beschichtung auf CIFT wird mit numerischen Simulationen quantifiziert. Dabei werden neue, im Rahmen dieser Arbeit patentierte Anregungsgeometrien untersucht und erste Strömungsrekonstruktionen gezeigt.

Vacuum birefringence as a clear variant of light-by-light scattering is a feasible experiment at an XFEL combined with a PW-class laser. Such experiment would measure the birefringence of vacuum – induced by the giant electromagnetic field in the focus of the PW laser – directly by ultra-sensitive X-ray polarimetry. In this way, the effect depends on real photons of externally controllable beams, complementary to other effects where photon-photon couplings are in play. The HIBEF user consortium at the European XFEL is aiming for such experiment. We will give a status update on the progress made in recent beamtimes, concerning a) the polarimeter extinction for a well-collimated (laser-like) X-ray beam, b) the X-ray optics to be used inside the polarimeter, c) self-seeded operation of the XFEL and d) assuring best focusing/intensity verification.

Ziel/Aim Although CAR T-cell therapy has demonstrated tremendous clinical efficacy particularly in hematological malignancies, the success in solid tumors is still limited. The combination of the immunotherapeutic (Uni)CAR T-cell therapy with target modules (TM) in solid tumors and radiotherapy could be an additional treatment option. Therefore, we developed a human prostate stem cell antigen (PSCA)-specific, IgG4-based TM radiolabeled with copper-64 (Cu-64) for imaging and with actinium-225 (Ac-225) for treatment.

Methodik/Methods A novel human PSCA-specific IgG4-based TM was conjugated with DOTAGA and radiolabeled with (Cu-64) and (Ac-255). The imaging and therapy were studied in NMRI Foxn1 nu/nu mice with xenotransplanted PSCA-expressing PC-3 tumors. Metabolic changes of the tumors were visualized with 18F-PSMA (PET), the physical size of the tumors and the distance between the vessels in the tumors (US) were measured.

Ergebnisse/Results The radiochemical yield for the Cu-64/Ac-255 TM was 96 % and 52 % respectively, the RCP of the products were g.t. 97 %. The xenotransplanted mice were intravenously injected with the Cu-64 labeled TM given as single dose. After fast distribution, the blood activity concentration decreased very slowly. At 31 h p.i. the activity was maximal in the tumors thereby reaching the optimal tumor to background ratios and only the liver was also visible with much lower activity. The Ac-225 treatment resulted in significant lower tumor size as compared with the control group after 40 days. Doubling time of tumor volume was increased from 15.0 days (control) to 38.6 days of the treatment group. The 18F-PSMA ligand uptake was decreased in the tumor periphery. In comparison to the tumor size the vessel density decreased to a lower extent.

Schlussfolgerungen/Conclusions We here present a Cu-64/Ac-225 labeled TM that allows the combination of (Uni)CAR T-cell immunotherapy with imaging with the Cu-64 labeled TM version and radiotherapy with the Ac-225 labeled TM version combined as radioimmunotheranostics.

Stoffströme in Zuleitungen und im Eintrittsbereich von Trennapparaten müssen gezielt konditioniert und ausgelegt werden um problematische Prozesszustände zu vermeiden. Werden durch ungeeignete Einspeisegeometriene Konzentrations-, Temperatur- oder Impulsgradienten in den Zulauf (Feed) einer Trennkolonne oder eines Phasenabscheiders eingetragen, kann die Leistung der Eintrittsstufe beeinträchtigt werden. Im Falle zweiphasiger Feeds kann falsch ausgelegte Einspeisung beispielsweise zu erhöhter Tropfenbildung und -dispersion beitragen, Schäden am Außenmantel des Apparats hervorrufen oder zum Verblocken von Einbauten, wie Böden oder Ablaufschächten, führen.
Für eine Vielzahl praxisrelevanter Stoffsysteme erfolgt die Auswahl und die Dimensionierung notwendiger Zusatzausrüstung für die Einspeisung heuristisch und anhand von empirischen Korrelationen, welche hauptsächlich mit Daten für das Referenzsystem Wasser/Luft entwickelt wurden. Deren Übertragbarkeit auf kritische Stoffwerte (niedrige Grenzflächenspannung, niedrige Viskosität, hohe Dampfdichte) ist daher oft nur bedingt möglich. Gleiches gilt für die Auswahl geeigneter Einspeiseorgane für zweiphasige Feeds, die die Kenntnis von Phasenanteil und Strömungsform unter Prozessbedingungen (Feedleitungen mit großen Nennweiten und geringen Einlauflängen) erfordern.
In einer neuen Technikumsanlage am Helmholtz-Zentrum Dresden-Rossendorf wird ein Kreislaufprozess mit einem Kältemittel betrieben, mit dem zweiphasige Feeds bei kritischen Stoffwerten charakterisiert werden können. Eine modular aufgebaute Feedleitung (D = 200 mm, L = 20 D) ermöglich die Bestimmung von Strömungsformen flashender Feeds mittels bildgebender Gittersensorik.
Als Kältemittel wird 3MTM Novec 649TM eingesetzt, welches Untersuchungen bei niedrigen Oberflächenspannungen (<10 mN/m) und hohen Dampfdichten (18 bis 170 kg/m³) bei gleichzeitig hohen Dichtedifferenzen den Phasen (887 bis 1450 kg/m³) ermöglicht. Die auftretenden Strömungsmorphologien in der Feedleitung nahe der Entspannungsarmatur und am Kolonneneintritt wurden für verschiedene Massenströme und Dampfanteile charakterisiert und mit gängigen Strömungskarten und Wasser/Luft-Experimentaldaten verglichen.
Diese Arbeiten im Rahmen des Projektes TERESA wurden durch das Bundesministerium für Wirtschaft und Energie (BMWI) gefördert (FKZ 03ET1395).

The lecture will focus on tomographic imaging for particle therapy treatment planning, with special emphasis on the prediction of the stopping power ratio S(PR) as prerequisite for accurate range calculation. This knowledge is valid for both: Treatment planning before therapy start and for plan adaptations. Special emphasis will be on the application of Dual-Energy CT for direct SPR prediction.

The theoretical understanding of plasmon behavior is crucial for an accurate interpretation of inelastic scattering diagnostics in many experiments. We highlight the utility of linear-response time-dependent density functional theory (LR-TDDFT) as a first-principles framework for consistently modeling plasmon properties. We provide a comprehensive analysis of plasmons in aluminum from ambient to warm dense matter conditions and assess typical properties such as the dynamical structure factor, the plasmon dispersion, and the plasmon lifetime. We compare our results with scattering measurements and with other TDDFT results as well as models such as the random phase approximation, the Mermin approach, and the dielectric function obtained using static local field corrections of the uniform electron gas parametrized from path-integral Monte Carlo simulations. We conclude that results for the plasmon dispersion and lifetime are inconsistent between experiment and theories and that the common practice of extracting and studying plasmon dispersion relations is an insufficient procedure to capture the complicated physics contained in the dynamic structure factor in its full breadth.

Iron is one of the most plentiful components on the planet earth and plays a crucial role in our lives. The analysis of iron at high pressures and temperatures is of great geophysical importance because iron makes up the majority of the Earth’s liquid outer core and solid inner core. The technical utility of iron is due to the large phase space of iron-based alloys, which is the source of a wide range of steel microstructures that can be produced with minor compositional changes and proper thermal treatment. The iron phase structure at the extreme conditions under the inner core conditions of the earth is still not conclusive especially in the vicinity of temperature around 6000 K and pressures nearing 300 GPa. Time-dependent
density functional theory (TDDFT) enables calculating electronic transport properties in warm dense matter (WDM) and is an alternative to present state-of-the-art approaches. In TDDFT, the electrical conductivity is computed from the time evolution of the electronic current density and provides direct means to assess the validity of Ohm’s law in WDM. We present TDDFT calculations of the electrical conductivity for iron within the pressure and temperature range found in Earth’s core. We discuss the ramifications of using TDDFT for calculating the electrical conductivity in contrast to the Kubo-Greenwood formalism and dielectric models.

Time-dependent density functional theory (TDDFT) enables calculating electronic transport properties in warm dense matter (WDM) and is an alternative to present state-of-the-art approaches. In TDDFT, the electrical conductivity is computed from the time evolution of the electronic current density and provides direct means to assess the validity of Ohm's law in WDM. We present TDDFT calculations of the electrical conductivity, for example in iron within the pressure and temperature range found in Earth's core. We discuss the ramifications of using TDDFT for calculating the electrical conductivity in contrast to the Kubo-Greenwood formalism and dielectric models.

The performance gap between CPU and memory widens continuously. Choosing the best memory layout for each hardware architecture is increasingly important as more and more programs become memory bound. For portable codes that run across heterogeneous hardware architectures, the choice of the memory layout for data structures is ideally decoupled from the rest of a program. This can be accomplished via a zero-runtime-overhead abstraction layer, underneath which memory layouts can be freely exchanged.
We present the Low-Level Abstraction of Memory Access (LLAMA), a C++ library that provides such a data structure abstraction layer with example implementations for multidimensional arrays of nested, structured data. LLAMA provides fully C++ compliant methods for defining and switching custom memory layouts for user-defined data types. The library is extensible with third-party allocators.
Providing two close-to-life examples, we show that the LLAMA-generated AoS (Array of Structs) and SoA (Struct of Arrays) layouts produce identical code with the same performance characteristics as manually written data structures. Integrations into the SPEC CPU® lbm benchmark and the particle-in-cell simulation PIConGPU demonstrate LLAMA's abilities in real-world applications. LLAMA's layout-aware copy routines can significantly speed up transfer and reshuffling of data between layouts compared with naive element-wise copying.
LLAMA provides a novel tool for the development of high-performance C++ applications in a heterogeneous environment.

Talk on new features in C++14.

LLAMA is a C++17 template header-only library for the abstraction of memory access patterns. It distinguishes between the view of the algorithm on the memory and the real layout in the background. This enables performance portability for multicore, manycore and gpu applications with the very same code.

In-depth introduction to LLAMA and hands-on coding session.

Presentation of LLAMA and PhD progress at ZIH (center for information services and high performance computing - TU Dresden).

Short talk about alpaka, SIMD in C++, Vc, VecCore, LLAMA and std::simd.

Wissenschaftliche Experimente nutzen eine große Bandbreite an verschiedenen Software-Werkzeugen in den verschiedenen Phasen des Projektes von der Proposal-Einreichung über die Datennahme bis zur finalen Publikation. Eine große Herausforderung für Wissenschaftseinrichtungen ist es, WissenschaftlerInnen für die Dokumentation der genutzten Werkzeuge in allen Phasen des Forschungsprojektes zusätzliche Metadaten gemäß der FAIR-Prinzipien zur Verfügung zu stellen. Das Ziel der HELmholtz ScIentific Project WORkflow PlaTform (HELIPORT) ist es daher den kompletten Lebenszyklus eines wissenschaftlichen Projekts zu registrieren und die zugehörigen Programme und Systeme miteinander zu verknüpfen. Die maschinenlesbare Dokumentation aller im jeweiligen Forschungsprojekt durchgeführten Arbeitsschritte gemeinsam mit den dazugehörigen Metadaten macht jeden Arbeitsschritt transparent, verständlich und zitierbar und trägt somit zur Einhaltung guter wissenschaftlicher Praxis bei.

The dynamic properties of a warm dense matter (WDM) system is strongly influenced by electron dynamics. Therefore, there is a need for computational methods which explicitly treat the electron dynamics, while still scalable and independent on any equilibrium assumptions, for the ability to model ongoing experimental efforts. We present an extension of the wave packet molecular dynamic scheme, where the electron state has an arbitrary Gaussian form, increasing the degree of freedom in the model and allowing for anisotropy in collisional dynamics and molecular bonds. The model employs a generalised scheme for the computation of short-range interactions, Ewald summation and Pauli interactions. The highly parallelised molecular dynamics framework LAMMPS has been utilized for the implementation of the model, with the intention to allow investigations of larger-scale systems. We present both ground state as well as the thermodynamic properties of the model, specialising in hydrogen systems.

We investigate the emergence of electronic excitations from the inhomogeneous electronic structure at warm dense matter parameters based on first-principles calculations. The emerging modes are controlled by the imposed perturbation amplitude. They include satellite signals around the standard plasmon feature, transformation of plasmons to optical modes, and double-plasmon modes. These modes exhibit a pronounced dependence on the temperature. This makes them potentially invaluable for the diagnostics of plasma parameters in the warm dense matter regime. We demonstrate that these modes can be probed with present experimental techniques.

Warm dense matter is a highly active research area both at the frontier and interface of material science and plasma physics. We assess the performance of commonly used exchange-correlation (XC) approximation (LDA, PBE, PBEsol, and AM05) in the spin-polarized inhomogeneous electron gas under warm dense conditions based on exact path-integral quantum Monte-Carlo calculations. This extends our recent analysis on the relevance of inhomogeneities in the spin-unpolarized warm dense electron gas [Z.~Moldabekov et al., J. Chem. Phys. 155, 124116 (2021)]. We demonstrate that the predictive accuracy of these XC functionals deteriorates with (1) a decrease in density (corresponding to an increase in the inter-electronic correlation strength) and (2) an increase of the characteristic wave number of the density perturbation. We provide recommendations for the applicability of the considered XC functionals at conditions typical for warm dense matter. Furthermore, we hint at future possibilities for constructing more accurate XC functionals under these conditions.

Correlated many-fermion systems emerge in a broad range of phenomena in warm dense matter, plasmonics, and ultracold atoms. Quantum hydrodynamics (QHD) complements first-principles methods for many-fermion systems at larger scales. We illustrate the failure of the standard Bohm potential central to QHD for strong perturbations when the density perturbation is larger than about 10−3 of the mean density. We then extend QHD to this regime via the many-fermion Bohm potential from first-principles. This may lead to more accurate QHD simulations beyond their common application domain in the presence of strong perturbations at scales unattainable with first-principles methods.

Warm dense matter (WDM)–an exotic, highly compressed state of matter between solid and plasma phases is of high current interest, in particular for astrophysics and inertial confinement fusion. For the latter, in particular the propagation of compression shocks is crucial. The main unknown in the shock propagation in WDM is the behavior of the electrons since they are governed by correlations, quantum and spin effects that need to be accounted for simultaneously. Here we describe the shock dynamics of the warm dense electron gas using a quantum hydrodynamic model. From the numerical hydrodynamic simulations we observe that the quantum Bohm pressure induces shear force which weakens the formation and strength of the shock.
In addition, the Bohm pressure induces an electron density response which takes the form of oscillations. This is confirmed by the theoretical analysis of the early stage of the shock formation. Our theoretical and numerical analysis allows us to identify characteristic dimensionless shock propagation parameters at which the effect of the Bohm force is important.