Publications Repository - Helmholtz-Zentrum Dresden-Rossendorf

Plasmonics offers a promising framework for next-generation photonic applications, including optical tweezers, ultrafast lasing, and quantum communication. Integrating plasmonics into photonics enables efficient interface coupling between heterogeneous systems, resulting in enhanced performance and diverse functionality. This review presents various unique encapsulation methods for developing plasmonics-embedded hybrid nanocomposite systems. Recent progress in the manipulation mechanisms of encapsulated plasmons is systematically summarized, offering an active modulation platform for optimizing optical performance. Considering the opportunities and challenges, the advancement of tunable encapsulated plasmons exhibits promising prospects, as demonstrated by a section discussing recent significant progress in photonic applications.

The manipulation of plasmonics on noble metal nanoparticles (NPs) is of great interest in developing nonlinear photonic devices, such as all-optical switches and frequency combs. An Au@AuY-core/Au-shell nanoparticle (Au@AuY/Au NP) monolayer is proposed for the fine-tuning of plasmonics and enhanced third-order nonlinearity. Based on the different thermodynamic mechanisms of Au and Y ions, the compact Au@AuY/Au core–shell architectures are designed and surface-modified in fused silica (SiO2) with enhanced free electron density, mobility, and quantum size effect. The flexible modulation of plasmonics is realized, resulting in significant absorption enhancement (165% for interband absorption and 38% for free electron absorption, respectively) and fine-tuning of the localized surface plasma resonance (LSPR) band. In addition, the physical mechanism is investigated by density functional theory (DFT) and Mie theory, which reveals a transition from size-independence to size-dependence of LSPR owing to the synergistic effect of multiple physical factors such as free electron density and mobility. With the above advantages, the third-order nonlinearity is enhanced by 4.4 times compared with traditional Au NPs. It indicates the significant potential of Au@AuY/Au core–shell NP monolayer in the performance improvement of nonlinear photonic devices.

We investigated a miscible displacement of a less viscous liquid by a more viscous shear-thinning liquid in a Hele-Shaw cell. Due to a coacervation interaction between both liquids, a hydrodynamic instability appears in the form of inward viscous fingering. The liquids consisted of a solution of the anionic biopolymer xanthan gum, as the injection liquid, which displaced a cationic C14T AB aqueous solution. In the contact zone between the two solutions, the oppositely charged species form polymer-surfactant complexes due to electrostatic interactions. These complexes are insoluble in water and separate from the aqueous solution in the form of a gel-like growing membrane. During the continuing radial displacement, a large variety of patterns can emerge which is attributed to the rheological properties of the system involving viscosity gradients, the non-Newtonian nature of the displacing solution and the complex rheology of the coacervate phase. Variation of the flow rate and gap width of the Hele-Shaw cell revealed distinct instability regimes and allowed to identify main contributing mechanisms. These insights open the door for further investigation of fluid mechanics (i.e. Saffman-Taylor instability), pattern formation and self-organization problems in multiphase systems of complex rheology and its applications in engineering and technology.

Germanium (Ge) is a promising candidate for designing near-infrared photodetectors because of its bandgap (0.66 eV), which induces a large absorption coefficient at near-infrared wavelengths. Also, Ge has excellent compatibility of parallel processing with silicon technology [1,2]. Photodetectors based on Ge material have been fabricated with different structures such as metal-semiconductor-metal (MSM) and p−n junctions. On the other hand, the observation of high responsivity in semiconductor nanowires with a high surface-to-volume ratio has attracted growing interest in using nanowires in photodetectors. So far, significant efforts have been made to fabricate single nanowire-based photodetectors with different materials such as Si, Ge, and GaN to achieve miniaturized devices with high responsivity and short response time [3-5]. Hence, Ge nanowires are an excellent candidate to fabricate single nanowire-based near-infrared photodetectors.

In this work, we report on the fabrication and characterization of an axial p−n junction along Ge nanowires. First, through a resist mask created by electron beam lithography (EBL), the top Ge layers of germanium-on-insulator (GeOI) substrates were locally doped with phosphorus ions using ion beam implantation followed by rear-side flash lamp annealing. Then, the single Ge nanowire-based photodetectors containing an axial p−n junction were fabricated using EBL and inductively coupled plasma reactive ion etching. The fabricated single Ge nanowire devices demonstrate the rectifying current−voltage characteristic of a p−n diode in dark conditions. Moreover, the photoresponse of the axial p−n junction-based photodetectors was investigated under light illumination with three different wavelengths: 637 nm, 785 nm, and 1550 nm. The measurements indicated that the fabricated photodetectors can be operated at zero bias and room temperature under ambient conditions. A high responsivity of 3.7×102 AW-1 and a detectivity of 1.9×1013 cmHz1/2W-1 were observed at zero bias under illumination of a 785 nm laser diode. The responsivity of the single Ge NW photodetectors was increased by applying a reverse bias of 1V.

We present a study of the piezostrain-tunable gyrotropic dynamics in Co40Fe40B20 vortex microstructures fabricated on a 0.7Pb[Mg1/3Nb2/3]O3-0.3PbTiO3 single-crystal substrate. Using field-modulated-spin-rectification measurements, we demonstrate large frequency tunability (up to 45%) in individual microdisks accessed locally with low surface voltages, and magnetoresistive readout. With increased voltage applied to the substrate, we observe a gradual decrease of the vortex-core gyrotropic frequency associated with the contribution of the strain-induced magnetoelastic energy. The frequency tunability strongly depends on the disk size, with increased frequency downshift for disks with larger diameter. Micromagnetic simulations suggest that the observed size effects originate from the joint action of the strain-induced magnetoelastic and demagnetizing energies in large magnetic disks. These results enable a selective energy-efficient tuning of the vortex gyrotropic frequency in individual vortex-based oscillators with all-electrical operation.

In this paper, we present an experiment that explores the plasma dynamics of a 7 μm diameter carbon wire after being irradiated with a near-relativistic-intensity short pulse laser. Using an x-ray free electron laser pulse to measure the small angle x-ray scattering signal, we observe that the scattering surface is bent and prone to instability over tens of picoseconds. The dynamics of this process are consistent with the presence of a sharp, propagating shock front inside the wire, moving at a speed close to the hole boring velocity or that expected from a thermal shock at a few tens of Mbar.

We present a novel approach for an event generator inherently using exact QED descriptions to predict the results of high-energy electron-photon scattering experiments that can be performed at modern X-ray laser facilities.
With the advent of advanced laser systems producing high-frequency x-ray beams, e.g. the EuropeanXFEL as a prominent example, a regime of laser-plasma interaction is reached, where all-optical methods are increasingly questionable (see Fig. 1). Instead, the interaction of hot electrons and the X-ray laser pulse must be modeled with a QED-driven approach. Future experiments taking place at HED-HIBEF, LCLS, and other facilities targeting this regime, will encounter processes in x-ray scattering from (laser-driven) relativistic electrons, where the effects of the energy spectrum of the laser field as well as multi-photon interactions can not be neglected anymore.
In contrast to the application window of existing QED-PIC codes, our event generator makes use of the fact that the classical nonlinearity parameter barely approaches unity in high-frequency regimes. Therefore, based on a momentum-space Furry-picture formulation of strong-field QED, this allows taking the finite bandwidth of the x-ray laser into account in the description of the QED-like multi-photon interaction.

The important roles of bacterial outer membrane vesicles (OMVs) in various diseases and their emergence as a promising platform for vaccine development and targeted drug delivery necessitates the development of imaging techniques suitable for quantifying their biodistribution with high precision. To address this requirement, we aimed to develop an OMV specific radiolabeling technique for positron emission tomography (PET). A novel bacterial strain (E. coli BL21(DE3) ΔnlpI, ΔlpxM) was created for efficient OMV production, and OMVs were characterized using various methods. SpyCatcher was anchored to the OMV outer membrane using autotransporter-based surface display systems. Synthetic SpyTag-NODAGA conjugates were tested for OMV surface binding and 64Cu labeling efficiency. The final labeling protocol shows a radiochemical purity of 100% with a ~ 29% radiolabeling efficiency and excellent serum stability. The in vivo biodistribution of OMVs labeled with 64Cu was determined in mice using PET/MRI imaging which revealed that the biodistribution of radiolabeled OMVs in mice is characteristic of previously reported data with the highest organ uptakes corresponding to the liver and spleen 3, 6, and 12 h following intravenous administration. This novel method can serve as a basis for a general OMV radiolabeling scheme and could be used in vaccine- and drug-carrier development based on bioengineered OMVs.

Genetically engineered T cells expressing chimeric antigen receptors (CARs) have shown promising results particularly when targeting tumor associated antigens (TAAs) related to hematological malignancies. However, TAAs are usually expressed to some extend also on healthy tissues leading to on-target/off-tumor effects. To overcome this important safety issue along with improving targeting specificity and efficient killing of tumor escape variants, we adapted our Reverse CAR (RevCAR) system to follow an AND-gate Boolean logic. For that, Dual-RevCAR T cells were designed and armed with (I) a signaling (SIG) RevCAR, that includes the intracellular domain (ICD) of CD3 zeta; and (II) a costimulatory (COS) RevCAR, which contains a domain derived from CD28. Because the extracellular domains of both RevCARs are derived from the La/SS-B nuclear protein, Dual-RevCAR T cells will remain inactive until they encounter matching target modules (RevTMs). The bispecific antibody (bsAb)-like structure of the RevTMs allows their binding to RevCAR molecules and to specific antigens. However, only the simultaneous binding of RevTMs to SIG and COS RevCARs will promote the full activation of the Dual-RevCAR T cells. The epithelial cell adhesion molecule (EpCAM) and the carcinoembryonic antigen (CEA) have become appealing markers due to their overexpression in various solid tumor entities such as colorectal cancer, therefore representing promising target antigens for cancer immunotherapies following such a Dual Targeting CAR approach.

Having this in mind, the aim of this work was to assess the potential therapeutic application of the Dual-RevCAR system to target EpCAM and CEA following an AND-gating approach.

This study aims to investigate how pulsed electromagnetic fields (PEMF) can affect grain refinement and microstructure during the solidification of a model Ga-In alloy. The magnetic system used generates field intensities of 8 - 11 mT and frequencies in the domain between 10 and 300 Hz, a duty cycle of 50%. We record the dendritic structures at the end of solidification experiments after switching OFF the electromagnetic field via X-ray radiographic imaging. Preliminary lab-scale results show that the solidification under frequencies above 100 Hz leads to dendrite fragmentation and solute redistribution in the mushy zone. No evidence of a CET is observed despite numerous fragmentation events. The fragments that detached from the dendritic network were unable to grow as equiaxed dendrites in the liquid as they became trapped within the dendritic network.

X-ray radiographic imaging is an efficient tool for investigating flow phenomena and solidification processes in optically opaque metallic alloys. This contribution is an overview of the latest advances in in-situ radiographic experiments made by the authors, as well as recent applications, including magnetohydrodynamic systems. We investigated a range of phenomena, such as bubble flow in liquid metal under an applied magnetic field, collective bubble dynamics, particle flow in liquid metal channels, and mesoscale solidification of alloys. Radiography measurements in liquid/solidified metal experiments are inevitably performed under adverse conditions of low signal-to-noise ratio, low image contrast, scattering, etc. To extract meaningful information from experimental data we combine both well-known methodology of data processing and our original codes. Examples of image analysis and results of in-situ experiments performed with low melting point alloys are presented and discussed in this contribution. A focus of these experiments is exploring scaled-down representative systems of industrial processes in metallic alloys.

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. Yet,
the interpretation of these experiments requires a reliable diagnostics based on accurate theoretical
modeling, which is a notoriously difficult task [1].

In this work, I will give an overview of how we can use exact ab-initio path integral Monte Carlo
(PIMC) simulations [2] together with thermal density functional theory (DFT) calculations to get new
insights into the behavior of WDM. Moreover, I will show how switching to the imaginary- time
representation allows us to significantly improve the interpretation of X-ray Thomson scattering
(XRTS) experiments, which are a key diagnostic for WDM [3]. Specifically, I will present a model-
free temperature diagnostic [4] based on the well-known principle of detailed balance, but available
for all wave numbers, and a new idea to directly extract the electron—electron static structure factor
from an XRTS measurement [5]. As an outlook, I will show how new PIMC capabilities will allow
to give us novel insights into electronic correlations in warm dense quantum plasmas, leading to
unprecedented agreement between experiments [6] and theory.

[1] M. Bonitz et al., Physics of Plasmas 27, 042710 (2020)
[2] M. Böhme et al., Physical Review Letters 129, 066402 (2022)
[3] S. Glenzer and R. Redmer, Reviews of Modern Physics 81, 1625 (2009)
[4] T. Dornheim et al., Nature Communications 13, 7911 (2022)
[5] T. Dornheim et al., arXiv:2305.15305 (submitted)
[6] T. Döppner et al., Nature 618, 270-275 (2023)

The nanospace of the van der Waals (vdW) gap between structural units of two-dimensional (2D) materials serves as a platform for growing unusual 2D systems through
intercalation and studying their properties. Various kinds ofmetal chlorides have previously been intercalated for tuning the properties of host layered materials, but the atomic structure of
the intercalants remains still unidentified. In this study, we investigate the atomic structural transformation of molybdenum(V) chloride (MoCl 5 ) after intercalation into
bilayer graphene (BLG). Using scanning transmission electron microscopy, we found that the intercalated material represents MoCl 3 networks, MoCl 2 chains, and Mo 5 Cl 10 rings. Giant lattice distortions and frequent structural transitions occur in the 2D MoCl x that have never been observed in metal chloride systems. The trend of symmetric to nonsymmetric structural
transformations can cause additional charge transfer from BLG to the intercalated MoCl x , as suggested by our density functional theory calculations. Our study deepens the understanding of the behavior of matter in the confined space of the vdW gap in BLG and provides hints at a more efficient tuning of material properties by intercalation for potential applications,
including transparent conductive films, optoelectronics, and energy storage.

We study the role of multiplicative stochastic processes in the description of the dynamics of an order parameter near a critical point. We study equilibrium as well as out-of-equilibrium properties. By means of a functional formalism, we build the Dynamical Renormalization Group equations for a real scalar order parameter with symmetry, driven by a class of multiplicative stochastic processes with the same symmetry. We compute the flux diagram using a controlled -expansion, up to order. We find that, for dimensions the additive dynamic fixed point is unstable. The flux runs to a multiplicative fixed point driven by a diffusion function, where s the order parameter and is the fixed point value of the multiplicative noise coupling constant. We show that, even though the position of the fixed point depends on the stochastic prescription, the critical exponents do not. Therefore, different dynamics driven by different stochastic prescriptions (such as Itô, Stratonovich, anti-Itô and so on) are in the same universality class.

Composites consisting of magnetic fillers in polymers and elastomers enable new application scenarios in soft robotics [1,2] and reconfigurable actuation [3]. Furthermore, they gave birth to the novel technology of solution processable magnetic field sensors. We demonstrate that printed magnetoelectronics can be stretchable, skin-conformal, capable of detection of low magnetic fields and withstand extreme mechanical deformations [4,5]. We feature the potential of our skin-conformal sensors in augmented reality settings for remote and touchless control of virtual objects, scrolling electronic documents and zooming maps. We put forth technology to realise magnetic field sensors, which can be printed and self-heal upon mechanical damage [6]. This opens exciting perspectives for magnetoelectronics in smart wearables, interactive printed electronics. Moreover, this research motivates further explorations towards the realization of eco-sustainable magnetoelectronics. For the latter, we will discuss biocompatible and biodegradable magneto sensitive devices, which can help to minimise electronic waste and bring magnetoelectronics to new application fields in medical implants and health monitoring.

[1] Y. Liu et al., Responsive magnetic nanocomposites for intelligent shape-morphing microrobots. ACS Nano 17, 8899 (2023).
[2] M. Richter et al., Locally addressable energy efficient actuation of magnetic soft actuator array systems. Advanced Science 2302077 (2023).
[3] M. Ha et al., Reconfigurable magnetic origami actuators with on-board sensing for guided assembly. Advanced Materials 33, 2008751 (2021).
[4] M. Ha et al., Printable and stretchable giant magnetoresistive sensors for highly compliant and skin-conformal electronics. Advanced Materials 33, 2005521 (2021).
[5] E. S. Oliveros Mata et al., Dispenser printed bismuth-based magnetic field sensors with non-saturating large magnetoresistance for touchless interactive surfaces. Adv. Mater. Technol. 7, 2200227 (2022).
[6] R. Xu et al., Self-healable printed magnetic field sensors using alternating magnetic fields. Nature Communications 13, 6587 (2022).

Here, by making use of medium and high resolution autocorrected off-axis electron holography, we directly probe the electrostatic potential as well as in-plane structural reconstruction at edges and steps in multilayer hexagonal boron nitride. In combination with ab initio calculations, the data allows revealing the formation of folded zigzag edges at steps comprising two monolayers and their absence at monolayer steps.

Ion implantation is a key capability for the semiconductor industry. As devices shrink, novel materials enter the manufacturing line, and quantum technologies transition to being more mainstream. Traditional implantation methods fall short in terms of energy, ion species, and positional precision. Here, we demonstrate 1 keV focused ion beam Au implantation into Si and validate the results via atom probe tomography. We show the Au implant depth at 1 keV is 0.8 nm and that identical results for low-energy ion implants can be achieved by either lowering the column voltage or decelerating ions using bias while maintaining a sub-micron beam focus. We compare
our experimental results to static calculations using SRIM and dynamic calculations using binary collision approximation codes TRIDYN and IMSIL. A large discrepancy between the static and dynamic simulation is found, which is due to lattice enrichment with high-stopping-power Au and surface sputtering. Additionally, we demonstrate how model details are particularly important to the simulation of these low-energy heavy-ion implantations. Finally, we discuss how our results pave a way towards much lower implantation energies while maintaining high spatial resolution.

We present the extremophilic bacterium Methylacidiphilum fumariolicum SolV as a platform for the recovery of rare earth elements (REE). Strain SolV is able to selectively extract the light REE from artificial industrial waste sources, natural REE-containing and postmining waters. Upscaling, different media composition and accumulation over several cycles were successfully implemented, underlining the potential for bio-recovery of REE.

Der Industriestandort Deutschland ist auf eine sichere und nachhaltige Versorgung mit mineralischen Rohstoffen angewiesen. Hierbei wird das Recycling von Rohstoffen als weiteres Standbein der Versorgung neben der heimischen Rohstoffgewinnung und dem Import von Rohstoffen künftig eine immer wichtigere Rolle spielen. In diesem Zusammenhang veröffentlichte das Bundesministerium für Wirtschaft und Klimaschutz (BMWK) im Januar 2023 das Eckpunktepapier „Wege zu einer nachhaltigen und resilienten Rohstoffversorgung“ und unterstrich darin die strategische Bedeutung einer engen Verzahnung von Kreislaufwirtschafts- und Rohstoffstrategie.
Die Dialogplattform Recyclingrohstoffe wurde im Rahmen der Deutschen Rohstoffstrategie 2020 mit dem Ziel beauftragt, Maßnahmen zu erarbeiten, die den Beitrag von Recyclingrohstoffen (Sekundärrohstoffen) für die Versorgungssicherheit von Metallen und Industriemineralen stärken.
Hierzu wurden in einem Dialogprozess mit über 380 Vertreterinnen und Vertretern aus Wirtschaft, Wissenschaft und Verwaltung sowie Zivilgesellschaft über einen Zeitraum von zwei Jahren in zwei Arbeitskreisen (Metalle und Industrieminerale) mit insgesamt acht Unterarbeitskreisen konkrete Handlungsoptionen entwickelt. Der inhaltliche Zuschnitt der Unterarbeitskreise orientierte sich an spezifischen Stoffströmen, die zum Beispiel aufgrund ihrer Mengenrelevanz, Kritikalität oder ihres Beitrags zu Treibhausgasemissionen von besonderer Relevanz sind und stoffstromspezifische Anforderungen an das Recycling stellen. Darüber hinaus spielte die in den Unterarbeitskreisen vorhandene Expertise der Teilnehmenden eine Rolle beim finalen Zuschnitt der Themen.
Die Ergebnisse aus den Unterarbeitskreisen bilden den inhaltlichen Kern des vollzogenen Dialogprozesses und werden in Steckbriefen beschrieben. So liegen für den Arbeitskreis Metalle detaillierte Steckbriefe für die Stoffströme Aluminium, Eisen und Stahl, Kupfer sowie Technologiemetalle vor.
Der Arbeitskreis Industrieminerale umfasst detaillierte Steckbriefe für die Stoffströme Baurohstoffe, Gips, Keramische Rohstoffe (Feuerfestkeramik) sowie Industrielle Reststoffe und Nebenprodukte.
Insgesamt wurden über die gesamte Projektlaufzeit 94 stoffstromspezifische Handlungsoptionen in den verschiedenen Unterarbeitskreisen erarbeitet, die auf einer systematischen Analyse bestehender Barrieren basieren. Alle Handlungsoptionen für die spezifischen Stoffströme finden sich im jeweiligen Steckbrief. Auf Ebene der beiden Arbeitskreise Metalle und Industrieminerale wurden zudem neun stoffstromübergreifende Handlungsfelder aggregiert, zu denen unter den Teilnehmenden Einigkeit erzielt werden konnte. Weitere Themenbereiche, die einem verbesserten Recycling potenziell zuträglich sind, zu denen jedoch kontroverse Diskussionen unter den Teilnehmenden stattfanden, werden in diesem Bericht transparent dargelegt. Ferner ist zu beachten, dass alle übergreifenden Handlungsfelder einen direkten Recyclingbezug aufweisen. Weiter gefasste wirtschaftspolitische Instrumente, zum Beispiel aus dem Bereich der Klimapolitik, die ebenfalls einen förderlichen Effekt auf ein verstärktes Recycling haben können, wie eine CO2-Bepreisung oder ein Emissionshandel, sind daher nicht vertiefend in der Dialogarbeit aufgegriffen worden. Nachfolgend sind die übergreifenden Handlungsfelder der beiden Arbeitskreise kurz zusammengefasst, wobei die Relevanz der genannten Themen zwischen den Unterarbeitskreisen zum Teil erheblich variiert.
Die ausführlichen Steckbriefe der acht stoffstromspezifischen Unterarbeitskreise, die in dieser Kurzfassung nur umrissen werden, umfassen weitere Handlungsoptionen und betten diese jeweils in die Ausgangslage des Status quo und die daraus resultierenden Barrieren für das Recycling ein. Des Weiteren umfassen die separat zur Verfügung gestellten Steckbriefe eine differenzierte Beurteilung der „Machbarkeit“ sowie möglicher Zielkonflikte in der Umsetzung der jeweiligen Handlungsoptionen. Gerade diese einbettende Betrachtung der vorgeschlagenen Handlungsoptionen stellt einen zentralen Mehrwert der Dialogarbeit dar, da den Lesenden hierdurch ein umfassenderes Verständnis der Vor- und gegebenenfalls auch Nachteile ermöglicht werden soll.

Additive nanotechnology enable curvilinear and three-dimensional (3D) magnetic architectures with tunable topology and functionalities surpassing their planar counterparts. Here, we experimentally reveal that 3D soft magnetic wireframe structures resemble compact manifolds and accommodate magnetic textures of high order vorticity determined by the Euler characteristic, $\chi$. We demonstrate that self-standing magnetic tetrapods (homeomorphic to a sphere; $\chi=+2$) support six surface topological solitons, namely four vortices and two antivortices, with a total vorticity of +2 equal to its Euler characteristic. Alternatively, wireframe structures with one loop (homeomorphic to a torus; $\chi = 0$) possess equal number of vortices and antivortices, which is relevant for spin-wave splitters and 3D magnonics. Subsequent introduction of $N$ holes into the wireframe geometry (homeomorphic to an $N$-torus; $\chi < 0$) enables the accommodation of a virtually unlimited number of antivortices, which suggests their usefulness for non-conventional (e.g. reservoir) computation. Furthermore, complex stray-field topologies around these objects are of interest for superconducting electronics, particle trapping and biomedical applications.

Antiferromagnets have large potential for ultrafast coherent switching of magnetic order with minimum heat dissipation. In materials such as Mn₂Au and CuMnAs, electric rather than magnetic fields may control antiferromagnetic order by Néel spin-orbit torques (NSOTs). However, these torques have not yet been observed on ultrafast time scales. Here, we excite Mn₂Au thin films with phase-locked single-cycle terahertz electromagnetic pulses and monitor the spin response with femtosecond magneto-optic probes.We observe signals whose symmetry, dynamics, terahertz-field scaling and dependence on sample structure are fully consistent with a uniform inplane antiferromagnetic magnon driven by field-like terahertz NSOTs with a torkance of (150 ± 50) cm² A⁻¹ s⁻¹. At incident terahertz electric fields above 500 kV cm⁻¹, we find pronounced nonlinear dynamics with massive Néelvector deflections by asmuch as 30°. Our data are in excellent agreement with a micromagnetic model. It indicates that fully coherent Néel-vector switching by 90° within 1 ps is within close reach.

Fast and accurate equation-of-state (EOS) data is of critical importance in the warm dense matter (WDM) regime, for example as input to hydrodynamic codes used in inertial confinement fusion modelling. Since EOS data must be generated on-the-fly for many applications, a tabular approach based on interpolation of known data points is generally used. Alternatively, average-atom models are (in some cases) fast enough to generate on-the-fly EOS data in an ab initio way, i.e. without empirical inputs. In this presentation, we present a newly-developed method which can be considered a hybrid of these two approaches. In our method, we use data generated by average-atom models as input features to a neural network model [1], which is trained on the first principles EOS dataset of Militzer and co-workers [2]. This approach has several advantages relative to using an unmodified average-atom model, and relative to standard interpolation techniques.

[1] Callow, T. J., Kraisler, E., & Cangi, A. (2023). Physics-enhanced neural networks for equation-of-state calculations. arXiv preprint arXiv:2305.06856.
[2] Militzer, B., González-Cataldo, F., Zhang, S., Driver, K. P., & Soubiran, F. (2021). First-principles equation of state database for warm dense matter computation. Phys. Rev. E, 103(1), 013203

Animal encounters are key components of population dynamics, community dynamics, and
evolutionary processes. Consequently, measuring encounter rates (i.e. encounters per time) can be
insightful. Encounter rates can be measured from animal tracking data, using metrics that can be split
into two groups. The first group consists of trajectory-based metrics, i.e. measures based on serial
records of animal locations. This first group includes PROX, the number of observed per number of
samples. The second group, in contrast, consists of metrics based on home range overlap, including
the Bhattacharyya coefficient (BC). In this study, we argue both types of metrics are limited.
Trajectory-based metrics are direct measures of encounter rates but have statistical estimation
issues due to their dependency on the frequency of location sampling. Meanwhile, home-rangebased metrics are statistically sound but are not proportional to encounter rates. To overcome both
challenges, we proposed a new metric, Relative Encounter Rate (RER). RER increases linearly with the
number of encounters and does not depend on the frequency of sampling (i.e. it is scale-invariant). In
an individual-based simulation, we measured how RER, BC, and PROX relative error under different
sample sizes and sampling frequencies. Further, we compared these metrics in three empirical case
studies. We tested Jaguars for polygyny, deforestation effects on tapir connectivity, and an extension
of the dearest enemy hypothesis with brown bears. We also compared partner hierarchy according
to BC and RER in Jaguar mating clusters. In the simulation study, we found PROX overestimates the
encounter rate when data has a low sampling frequency. The simulation also indicates BC
overestimated encounters. Furthermore, PROX led to false positives in the Tapir and Bear case
studies. In addition, PROX was incapable of detecting many individual relationships in the jaguar
polygyny study. RER does not depend on sampling frequency (contrary to PROX) or sample size
(contrary to BC). We discuss further hypotheses to test with RER and argue RER can enable ecologists
to analyze encounters with a level of detail adequate to their importance, leading to a better
understanding of how individual behaviors influence population and community dynamics.

In natural sciences, mineral exploration has a high network centrality. For industries with high technological- and knowledge proximity, transfer effects are an important function for innovation. Despite the high level of proximity between mineral exploration and other natural sciences, scholars hardly examine transfers from and to mineral exploration. This paper analyzes obstacles and mechanisms of transfer effects in and from mineral exploration and finds answers on how to institutionalize knowledge and technology transfer (KTT). The study employs a qualitative research design. The underlying database consists of 16 expert interviews, from the fields of natural science. The results show that KTT between areas as diverse as mineral exploration, healthcare, and arts are possible. A lack of interdisciplinary exchange and rigid scientific structures is the main inhibitor of KTT. Before this study, evidence for KTT from and to smaller industries is mostly anecdotal. The study is among the few, which investigates KTT concerning functional transfer opportunities.

The electronic structure of UC (x = 0.9, 1.0, 1.1, 2.0) was studied by means of x-ray absorption spectroscopy (XAS) at the C K edge and measurements in the high energy resolution fluorescence detection (HERFD) mode at the U and edges. The full-relativistic density functional theory calculations taking into account the Coulomb interaction U and spin-orbit coupling (DFT+U+SOC) were also performed for UC and UC. While the U HERFD-XAS spectra of the studied samples reveal little difference, the U HERFD-XAS spectra show certain sensitivity to the varying carbon content in uranium carbides. The observed gradual changes in the U HERFD spectra suggest an increase in the C 2p-U 5f charge transfer, which is supported by the orbital population analysis in the DFT+U+SOC calculations, indicating an increase in the U 5f occupancy in UC as compared to that in UC. On the other hand, the density of states at the Fermi level were found to be significantly lower in UC, thus affecting the thermodynamic properties. Both the x-ray spectroscopic data (in particular, the C K XAS measurements) and results of the DFT+U+SOC calculations indicate the importance of taking into account U and SOC for the description of the electronic structure of actinide carbides.

The ingress of water into an underground nuclear repository, described as a worst-case scenario, can lead to the degradation of cement-based engineered barriers and thus to the release of organic cement additives that can affect radionuclide immobilisation. The additive 2-phosphonobutane-1,2,4-tricarboxylic acid (PBTC) is one of the most commonly used long-term retarders in cement, and also used as a corrosion inhibitor in reinforced concrete and steel. PBTC
is an organophosphonate ligand with one phosphonate and three carboxyl groups [1]. These functional groups make PBTC an effective dispersant and strong complexing agent for various metal ions (e.g. Ca2+, Al3+, Fe3+). However, neither the complexation of radionuclides by PBTC nor the influence of PBTC on radionuclide retention in cement phases has been investigated.
Therefore, both the complexation of U(VI) with PBTC in solution (binary system) and the influence of PBTC on the U(VI) retention by cementitious materials (ternary system) were investigated for the first time. The U(VI) complexation studies were performed by different series varying the pH from 2 to 11 and/or the U(VI) to PBTC ratio. The structure-sensitive methods NMR, IR and Raman spectroscopy were used to characterize the complex structure. Complementary DFT calculations were carried out. The U(VI) speciation in presence of PBTC was determined by UV-Vis and TRLFS spectroscopy. In the case of PBTC excess, soluble complex species are formed up to pH >10, which is relevant for cementitious systems due to degradation processes. For the U(VI) retention studies both calcium (aluminate) silicate hydrate (C-(A-)S-H) phases of different compositions, representing different cement degradation stages, as well as hardened cement paste were applied. TRLFS was applied to characterize the U(VI) binding. The PBTC retention was quantified by 1H and 31P solution NMR.

In a nuclear waste repository, cement-based materials are to be used for waste conditioning and as an engineered barrier. The ingress of water into the nuclear waste repository, described as a worst-case scenario, leads to increased aging and degradation of the concrete. These processes are associated with a leaching of diverse organic substances usually added to the cement to realize the desired physicochemical and mechanical properties of the cement-based materials. The impact of the additives is based on their excellent ability to complex metal ions. Consequently, the complexation behavior of such additives towards radionuclides (RN) and thus their impact on RN mobilization and migration into the environment is essential for a comprehensive risk assessment. One of the additives commonly used for long-term retardation of cement hardening is 2-phosphonobutane-1,2,4-tricarboxylic acid (PBTC).
PBTC is a polyfunctional ligand possessing three carboxyl groups and one phosphonate group, which have been shown to make PBTC a strong complexing agent for various metal ions (e.g. Ca2+, Zn2+, Al3+, Fe3+) [1,2]. However, to date, there are no studies on PBTC interaction with radionuclides. Therefore,
the complexation of PBTC with U(VI) was investigated for the first time, using different spectroscopic methods over a wide pH range (2 through 11) to identify and characterize possible complex species.
U(VI)-PBTC species with solubility as high as 100 mM were observed throughout the entire pH range studied, especially when PBTC is in excess. This allowed the convenient application of structuresensitive methods such as NMR, IR, and Raman spectroscopies. Furthermore, time-resolved laserinduced
fluorescence spectroscopy (TRLFS) and UV-Vis titration studies provided insight into U(VI)–PBTC system’s speciation.

Organophosphonates are used multipurpose in the chemical industry. One of the most commonly used organophosphonates is 2-phosphonobutane-1,2,4-tricarboxylic acid (PBTC).[1] The functional groups of PBTC consist of one phosphonate and three carboxylate groups, which make PBTC not only an effective dispersant, but also a very good complexing agent for various metal ions (e.g. Ca2+, Al3+, Fe3+).[2,3] Due to these properties, PBTC is used, for example, as an efficient long-term retarder in cement, as a corrosion inhibitor in reinforced concrete and steel, or as a scale inhibitor in water treatment plants or cooling water circulation systems.[4,5] However, this ubiquitous use can also lead to anthropogenic discharge into the environment, where PBTC can complex heavy metals or even radionuclides. Complexation can increase the solubility of metal ions and thus their bioavailability. As a result, there is an increased risk of toxic metal ions being distributed in the environment and thus also being absorbed into the human food chain.
However, to date there have been no studies on the complexation of PBTC with radionuclides. For this reason, the complexation of PBTC with U(VI) in the pH range from 1 to 11 was investigated for the first time using various spectroscopic methods. The studies were performed by different series varying the pH or the U(VI) to PBTC ratio. For the methods used, U(VI) concentrations in the mM range were employed, which was possible due to the very good water solubility of the U(VI)-PBTC complexes. The structure-sensitive methods NMR, IR and Raman spectroscopy were used to characterise the complex structure. Supporting DFT calculations were carried out. The stability constants of the complex species were determined by UV-Vis spectroscopy. By applying the different spectroscopic methods, it was possible to determine chelation of U(VI) by the phosphonate group and one of the carboxyl groups. Furthermore, by means of factor analysis, the distribution of complex species as well as the complexation constants could be determined for the first time. Therefore, the results of this study make it possible to evaluate the risk of PBTC entering the environment in relation to the radionuclide uranium.

The effective spin-1/2 antiferromagnetic Heisenberg-Ising chain materials, ACo₂V₂O₈, A = Sr, Ba, are a rich source of exotic fundamental phenomena and have been investigated for their model magnetic properties both in zero and nonzero magnetic fields. Here we investigate a new member of the family, namely, PbCo₂V₂O₈. We synthesize powder and single-crystal samples of PbCo₂V₂O₈ and determine its magnetic structure using neutron diffraction. Furthermore, the magnetic field/temperature phase diagrams for a magnetic field applied along the c, a, and [110] crystallographic directions in the tetragonal unit cell are determined via magnetization and heat capacity measurements. A complex series of phases and quantum phase transitions are discovered that strongly depend on both the magnitude and direction of the field. Our results show that PbCo₂V₂O is an effective spin-1/2 antiferromagnetic Heisenberg-Ising chain with properties that are, in general, comparable to those of SrCo₂V₂O₈ and BaCo₂V₂O₈. One interesting departure from the results of these related compounds is, however, the discovery of a new field-induced phase for the field direction H ӏӏ [110].

The rising interest in increased manufacturing maturity of quantum processing units is pushing the development of alternative superconducting materials for semiconductor fab process technology. However, these are often facing CMOS process incompatibility. In contrast to common CMOS materials, such as Al, TiN, and TaN, reports on the superconductivity of other suitable transition-metal nitrides are scarce, despite potential superiority. Here, we demonstrate fully CMOS-compatible fabrication of HfN and ZrN thin films on state-of-the-art 300mm semiconductor process equipment, utilizing reactive DC magnetron sputtering on silicon wafers. Measurement of mechanical stress and surface roughness of the thin films demonstrates process compatibility. We investigated the materials phase and stoichiometry by structural analysis. The HfN and ZrN samples exhibit superconducting phase transitions with critical temperatures up to 5.84 and 7.32 K, critical fields of 1.73 and 6.40 T, and coherence lengths of 14 and 7 nm, respectively. A decrease in the critical temperature with decreasing film thickness indicates mesoscopic behavior due to geometric and grain-size limitations. The results promise a scalable application of HfN and ZrN in quantum computing and related fields.

We investigated the elastic properties of Y_1−xNd_xCo₂Zn₂₀ with localized Nd f electrons and ground-state Kramers doublet. All longitudinal and transverse moduli of NdCo₂Zn₂₀ (x = 1) show an elastic softening below 50 K accompanied by a minimum around 2.5 K. The softening, which is robust to magnetic fields up to 8 T, is not observed for samples with Nd concentrations of x = 0.19, 0.05, and 0. In localized f electron systems, elastic softening from high temperatures is often understood by crystal electric field effects; however, this cannot explain the behavior in NdCo₂Zn₂₀. Our experimental and calculated results reveal that the softening neither is caused by a phonon contribution, a Nd³⁺ single-site effect, nor a magnetic interaction. We conclude that the softening is due to a local-symmetry-sensitive electronic state in NdCo₂Zn₂₀.

Direct studies of the adiabatic temperature change (ΔT_ad) in the Ni₄₇Mn₄₀Sn_12.5Cu_0.5 Heusler alloy in steady magnetic fields up to 8 T by the extraction method and in pulsed magnetic fields up to 50 T were carried out in this paper. The alloy Ni₄₇Mn₄₀Sn_12.5Cu_0.5 demonstrates a magnetostructural phase transition (MSPT) of the first order in the 254–283 K temperature range as well as a second order phase transition near the Curie temperature T_C = 313 K. An inverse magnetocaloric effect (MCE) was found in the region of the MSPT, and it reaches the maximum value ΔT_ad = -12 K in 20 T at the initial temperature T₀ = 275 K. The irreversible part of the MCE reached ΔT_ir = -10 K when the field is completely removed. We consider the dynamics of the MCE in the vicinity of the MSPT and discuss the mechanisms that cause the giant irreversibility of the MCE as well as the possibilities of its application in hybrid cooling systems.

Cr2O3 is an exceptional antiferromagnet with an easy axis of anisotropy that exhibits a magnetoelectric effect at room temperature [1]. Although there are technological challenges to use it for applications because of the relatively low bulk Neel temperature of TN = 308 K, there are demonstrations that TN can be substantially enhanced by strain in thin films. The morphology and growth procedure of such samples allow the appearance of flexomagnetic effects and pinning of domain walls at grain boundaries [2,3].

Here, we propose a material model of granular antiferromagnetic films and apply it to maze-like domain patterns in thin Cr2O3 samples [4]. The domain pattern is obtained by means of the nitrogen vacancy magnetometry and compared with spin-lattice simulations. We analyze the statistics of the size and self-similarity of the domain wall patterns to correlate the experimental measurements with the parameters of the theoretical model and compare the domain wall patterns with predictions made by a machine learning approach. The estimated inter-grain coupling is characterized by a substantial reduction of the effective exchange coupling to about 10% of the bulk value, with a wide standard deviation. Based on the material model, we provide design rules for the granular AFM recording media.

The term Research Software Engineer, or RSE, emerged a little over 10 years ago as a way to represent
individuals working in the research community but focusing on software development. The term has been widely
adopted and there are a number of high-level definitions of what an RSE is. However, the roles of RSEs vary
depending on the institutional context they work in. At one end of the spectrum, RSE roles may look similar to
a traditional research role. At the other extreme, they resemble that of a software engineer in industry. Most
RSE roles inhabit the space between these two extremes. Therefore, providing a straightforward, comprehensive
definition of what an RSE does and what experience, skills and competencies are required to become one is
challenging. In this community paper we define the broad notion of what an RSE is, explore the different types
of work they undertake, and define a list of fundamental competencies as well as values that define the general
profile of an RSE. On this basis, we elaborate on the progression of these skills along different dimensions, looking
at specific types of RSE roles, proposing recommendations for organisations, and giving examples of future
specialisations. An appendix details how existing curricula fit into this framework.

Data are an essential part of every scientific endeavour. An efficient and future oriented research data management is therefore essential in order to ensure long-term availability of the generated data. This in turn ensures the reproducibility of scientific results. In order to facilitate FAIR data management within the Helmholtz community the incubator platform “Helmholtz Metadata Collaboration (HMC)” was established.

HMC develops and provides services, tools and trainings to support and improve FAIR (meta)data management in the Helmholtz Association and aligns these approaches with national and international approaches and initiatives (e.g. RDA, EOSC, NFDI) to ensure compatibility with international research communities.

To achieve this goal, HMC builds its work along three strategic areas: (1) Assessing and monitoring the state of FAIR data across Helmholtz, (2) Facilitating the connectivity of Helmholtz research data, and (3) Transforming (meta)data recommendations into implementations. At the centres, HMC supports research communities and data professionals with six research-field specific hubs: At HZDR HMC is represented locally by a unit dedicated to research field Energy and remotely by a unit for research field Matter. In our poster we will illustrate how research and data professional communities at HZDR can benefit from HMC's services, tools and trainings.

Most underground nuclear waste disposal concepts envisage the extensive use of cementitious materials in the geo-engineered barrier as a buffer and borehole sealing material and to ensure the mechanical stability of disposal systems. In order to assess the radionuclide (RN) retention potential of these barrier materials, it is necessary to study the impact of various repository relevant conditions that will evolve over time, such as changed pH values, increased ionic strength, elevated temperatures, or the release of organic components. The U(VI) retention by calcium (aluminate) silicate hydrate (C-(A-)S-H) phases, forming owing to Al-rich additives in cement formulations, was studied for samples with C/S molar ratios of 0.8, 1.2, and 1.6, representing different alteration stages of concrete, and with increasing A/S molar ratios of 0, 0.06, and 0.18 in each series, with special focus on the presence of organics. The latter thereby comprise gluconate (GLU), 2-phosphonobutane-1,2,4,-tricarboxylate (PBTC), and a mixture of cellulose degradation products (CDP) obtained from dry radiolysis (dose rate 0.6 kGy/h, absorbed dose ~ 1.37 MGy) followed by hydrolysis in artificial cement water (pH > 13, anoxic conditions) provided by project partners within the CORI framework. Complementary analytical techniques were applied to address the different specific aspects of the cement / organics / RN ternary systems. 27Al and 29Si magic angle spinning (MAS) nuclear magnetic resonance (NMR) spectroscopy and powder X-ray diffraction (XRD) were applied to determine the bulk structure and composition of the synthesized C-(A-)S-H phases. 13C-, and in case of PBTC also 31P-, MAS NMR measurements aimed at localization and speciation of the organic components involved [1]. 1H and 31P solution NMR of the aqueous phase allowed for quantification of the organics’ fraction removed from solution and hence associated with the solid phase. Retained U(VI) species were identified by time-resolved laser-induced luminescence spectroscopy (TRLFS). Zeta-potential measurements were conducted to study the organics’ influence on the surface charge and, upon changing the order of mixing the individual components of the ternary systems (e.g., C-(A-)S-H phases synthesized in absence or presence of U(VI) and/or organics), along with results from spectroscopies, to derive mechanistic understanding of retention processes as well as surface complex models.

Die Methode zur Verstärkung sogenannter gechirpter Laserpulse wurde im Jahr 2018 mit dem Physik-Nobelpreis ausgezeichnet. Basierend auf dieser Methode lassen sich nun Laseranlagen mit Leistungen im Bereich von mehreren Petawatt realisieren. Werden die ultrakurzen Laserpulse eines solchen Hochleistungslasers auf dünne Metall- oder Plastikfolien fokussiert, können auf einer Strecke von wenigen Mikrometern intensive Ionenpulse beschleunigt werden. Bei der Wechselwirkung von Laserpuls und Folie entsteht dabei ein Plasma, in dem die Laserenergie zuerst auf schnelle Elektronen übertragen wird. Deren kollektive Felder bewirken dann die Beschleunigung von Ionen – das Prinzip eines Laser-Plasma-Beschleunigers. Besonders effektiv ist dieser Prozess für Protonen, die sich aktuell auf Energien jenseits der 100 MeV beschleunigen lassen.
Neben der Möglichkeit, kompakte Beschleuniger bauen zu können, punkten Laser-Plasma-Beschleuniger durch eine weitere besondere Eigenschaft: ihre Strahlen sind sehr intensiv und in Pikosekundenlänge gepulst, sodass ultrahohe Dosisleistungen erreicht werden. Davon kann die translationalen Krebsforschung profitieren – also jenes Forschungsgebiet, in dem Resultate der Grundlagenforschung auf neue Ansätze zur Prävention, Diagnostik und Behandlung von Krebserkrankungen übertragen werden. Die radiobiologische Wirkung stark gepulster Strahlung stellt hier nämlich eine aktuell intensiv diskutierte Fragestellung dar.
An der Realisierung einer Plattform für Radiobiologie mit ultrahohen Dosisraten basieren auf einem Laser-Plasma-Beschleuniger wird am Helmholtz-Zentrum Dresden—Rossendorf seit mehr als 15 Jahren gearbeitet. 2020 ist es hier nun erstmals gelungen, Tumoren in einem Kleintiermodell kontrolliert mit laserbeschleunigten Protonen zu bestrahlen. Bisherige Untersuchungen beschränkten sich auf Zellkulturen. Über viele Jahre wurden dazu der Laser, der Beschleunigungsmechanismus und das radiobiologische Modell studiert und optimiert, um bisherige Limitierungen bezüglich Protonenenergie und Beschleunigerstabilität zu überwinden und schließlich den Schritt von der Petrischale hin zum lebenden Modell wagen zu können. Die Ergebnisse markieren einen Meilenstein für die Entwicklung zuverlässiger Laser-Plasma-Beschleuniger und ermöglichen neuartige radiobiologische Studien.

Extended abstract of the EOSC EVERSE project (https://everse.software/), submitted for presentation in the International Research Software Engineering Research (IRSER) Community Meetup in January 2024 (https://www.software.ac.uk/news/international-research-software-engineering-research-irser-community-meetup)

Rationale: Current therapies for metastatic osseous disease frequently fail to provide a durable treatment response. To date, there are only limited therapeutic options for metastatic prostate cancer, the mechanisms that drive the survival of metastasis-initiating cells are poorly characterized, and reliable prognostic markers are missing. A high aldehyde dehydrogenase (ALDH) activity has been long considered a marker of cancer stem cells (CSC). Our study characterized a differential role of ALDH1A1 and ALDH1A3 genes as regulators of prostate cancer progression and metastatic growth.
Methods: By genetic silencing of ALDH1A1 and ALDH1A3 in vitro, in xenografted zebrafish and murine models, and by comparative immunohistochemical analyses of benign, primary tumor, and metastatic specimens from patients with prostate cancer, we demonstrated that ALDH1A1 and ALDH1A3 maintain the CSC phenotype and radioresistance and regulate bone metastasis-initiating cells. We have validated ALDH1A1 and ALDH1A3 as potential biomarkers of clinical outcomes in the independent cohorts of patients with PCa. Furthermore, by RNAseq, chromatin immunoprecipitation (ChIP), and biostatistics analyses, we suggested the molecular mechanisms explaining the role of ALDH1A1 in PCa progression.
Results: We found that aldehyde dehydrogenase protein ALDH1A1 positively regulates tumor cell survival in circulation, extravasation, and metastatic dissemination, whereas ALDH1A3 plays the opposite role. ALDH1A1 and ALDH1A3 are differentially expressed in metastatic tumors of patients with prostate cancer, and their expression levels oppositely correlate with clinical outcomes. Prostate cancer progression is associated with the increasing interplay of ALDH1A1 with androgen receptor (AR) and retinoid receptor (RAR) transcriptional programs. Polo-like kinase 3 (PLK3) was identified as a transcriptional target oppositely regulated by ALDH1A1 and ALDH1A3 genes in RAR and AR-dependent manner. PLK3 contributes to the control of prostate cancer cell proliferation, migration, DNA repair, and radioresistance. ALDH1A1 gain in prostate cancer bone metastases is associated with high PLK3 expression.
Conclusion: This report provides the first evidence that ALDH1A1 and PLK3 could serve as biomarkers to predict prostate cancer metastatic dissemination and radiotherapy resistance in patients with prostate cancer and be potential therapeutic targets to eliminate metastasis-initiating and radioresistant tumor cell populations.

Flash lamp annealing (FLA) is an ultra-short annealing method which excellently meets the requirements of thin film processing and has already been used in microelectronics. Due to the relatively high hole mobility, thin Ge layers are highly interesting as a transistor channel material or generally as a functional layer both in CMOS technology and in the field of low-cost electronics. One possibility to realize ohmic contacts with low contact resistance is the use of metal germanides, especially the stoichiometric NiGe phase.
In this work, NiGe contacts on thin Ge films were fabricated by magnetron sputtering followed by FLA. The evolution of microstructure with increasing thermal budget was traced by transmission electron microscopy, energy-dispersive X-ray spectroscopy, and X-ray diffraction. The electrical measurements focus on the determination of contact resistance by the circular transfer length method (cTLM). The contacts were fabricated by two different approaches, and the influence of different process steps on layer morphology and the uncertainty of the measurement was studied.

In this work, NiGe contacts on thin Ge films were fabricated by magnetron sputtering followed by flash lamp annealing (FLA). The evolution of microstructure with increasing thermal budget was traced by transmission electron microscopy, energy-dispersive X-ray spectroscopy and X-ray diffraction. The film sheet resistance, the free charge carrier mobility and concentration, and the contact resistance were measured by the four-point-probe method, by Hall effect measurements, and by the circular transfer length method, respectively. Based on this data, the formation process of NiGe contacts during FLA is described, which passes through a stage of Ni-rich phases with high electrical resistivity, before the final stoichiometric NiGe phase is formed.

The application of deep learning (DL) algorithms to Earth observation (EO) in recent years has enabled substantial progress in fields that rely on remotely sensed data. However, given the data scale in EO, creating large datasets with pixel-level annotations by experts is expensive and highly time-consuming. In this context, priors are seen as an attractive way to alleviate the burden of manual labeling when training DL methods for EO. For some applications, those priors are readily available. Motivated by the great success of contrastive-learning methods for self-supervised feature representation learning in many computer-vision tasks, this study proposes an online deep clustering method using crop label proportions as priors to learn a sample-level classifier based on government crop-proportion data for a whole agricultural region. We evaluate the method using two large datasets from two different agricultural regions in Brazil. Extensive experiments demonstrate that the method is robust to different data types [synthetic-aperture radar (SAR) and optical images], reporting higher accuracy values considering the major crop types in the target regions. Thus, it can alleviate the burden of large-scale image annotation in EO applications.

Timely and accurate building damage mapping is essential for supporting disaster response activities. While RS satellite imagery can provide the basis for building damage map generation, detection of building damages by traditional methods is generally challenging. The traditional building damage mapping approaches focus on damage mapping based on bi-temporal pre/post-earthquake dataset extraction information from bi-temporal images, which is difficult. Furthermore, these methods require manual feature engineering for supervised learning models. To tackle the abovementioned limitation of the traditional damage detection frameworks, this research proposes a novel building damage map generation approach based only on post-event RS satellite imagery and advanced deep feature extractor layers. The proposed DL based framework is applied in an end-to-end manner without additional processing. This method can be conducted in five main steps: (1) pre-processing, (2) model training and optimization of model parameters, (3) damage mapping generation, (4) accuracy assessment, and (5) visual explanations of the proposed method’s predictions. The performance of the proposed method is evaluated by two real-world RS datasets that include Haiti-earthquake and Bata-explosion. Results of damage mapping show that the proposed method is highly efficient, yielding an OA of more than 84%, which is superior to other advanced DL-based damage detection methods.

Among all the natural hazards, earthquake prediction is an arduous task. Although many studies have been published on earthquake hazard assessment (EHA), very few have been published on the use of artificial intelligence (AI) in spatial probability assessment (SPA). There is a great deal of complexity observed in the SPA modeling process due to the involvement of seismological to geophysical factors. Recent studies have shown that the insertion of certain integrated factors such as ground shaking, seismic gap, and tectonic contacts in the AI model improves accuracy to a great extent. Because of the black-box nature of AI models, this paper explores the use of an explainable artificial intelligence (XAI) model in SPA. This study aims to develop a hybrid Inception v3-ensemble extreme gradient boosting (XGBoost) model and shapely additive explanations (SHAP). The model would efficiently interpret and recognize factors’ behavior and their weighted contribution. The work explains the specific factors responsible for and their importance in SPA. The earthquake inventory data were collected from the US Geological Survey (USGS) for the past 22 years ranging the magnitudes from 5 Mw and above. Landsat-8 satellite imagery and digital elevation model (DEM) data were also incorporated in the analysis. Results revealed that the SHAP outputs align with the hybrid Inception v3-XGBoost model (87.9% accuracy) explanations, thus indicating the necessity to add new factors such as seismic gaps and tectonic contacts, where the absence of these factors makes the prediction model performs poorly. According to SHAP interpretations, peak ground accelerations (PGA), magnitude variation, seismic gap, and epicenter density are the most critical factors for SPA. The recent Turkey earthquakes (Mw 7.8, 7.5, and 6.7) due to the active east Anatolian fault validate the obtained AI-based earthquake SPA results. The conclusions drawn from the explainable algorithm depicted the importance of relevant, irrelevant, and new futuristic factors in AI-based SPA modeling.

Theoretical models in ecology often assume well-mixed populations and thus that individuals interact with one another with the same probability regardless of their spatial location. This strong assumption results in mathematically very tractable models, often based on ordinary differential equations for the population size, that have taught us a lot about how populations change over time. However, these models do not account for the fact that populations are spatially structured, which favors interactions between nearby individuals.
In this presentation, I will present some of our recent work investigating the effect of space in shaping population dynamics. First, I will discuss how stochastic spatial models, from lattice models to systems of interacting particles, can provide more accurate descriptions of ecological populations and how to analyze them computationally and analytically. Then, I will use a specific example based on an asymmetric voter model to show how modeling spatial processes can reverse the outcome of species competition predicted by a well-mixed model.

A lot of ecological theory relies on ordinary-differential-equation models that assume well-mixed systems and do not incorporate any information about the spatial distribution of organisms. However, ecosystems present spatial heterogeneities at different scales that can impact individual fitness and, ultimately, population dynamics. I will present an alternative approach to describe the spatiotemporal dynamics of a population of interacting agents. To this end, I will consider a system with nonlinear birth-death rates and positive and negative inter-individual interactions acting at different spatial ranges. I will first describe the stochastic, individual-level rules that govern the reproduction and death of each individual. Then, using field-theory techniques, I will derive a non-local partial differential equation for the population density and compare its predictions with those obtained assuming well-mixed populations. Finally, I will discuss the ecological relevance of our results and how this approach can be extended to more complex scenarios.

Despite the importance of ungulate migrations, we lack a complete understanding of why some ungulates species migrate and some do not. Moreover, at the population level, some migrate and others remain behind as residents, a phenomenon known as partial migration. Even though progress has been made towards understanding long-term fitness benefits of partial migration, the underlying decision-making process that makes some individuals migrate and others remain within one single range remain unknown. In this presentation I will combine empirical data from three different migrant ungulate species and mathematical modeling to address this question. I will first show that, across these three ungulate species, the number of residents is unrelated to total population size, a pattern predicted by no previous modeling framework. Next, I will introduce a new model of ungulate partial migration wherein individuals probabilistically decide to start migrating based on the intensity of environmental and social cues. Within this modeling framework, residents arise for most parameter combinations and the number of residents is largely invariant with total population size. Therefore, this new model explains the ubiquity of residents in migratory ungulate populations and presents novel patterns to be tested with further data collection.

A lot of ecological theory relies on ordinary-differential-equation models that assume well-mixed systems and do not incorporate any information about the spatial distribution of organisms. However, ecosystems present spatial heterogeneities at different scales that can impact individual fitness and, ultimately, population dynamics. I will present an alternative approach to describe the spatiotemporal dynamics of a population of interacting agents. To this end, I will consider a system with nonlinear birth-death rates and positive and negative inter-individual interactions acting at different spatial ranges. I will first describe the stochastic, individual-level rules that govern the reproduction and death of each individual. Then, using field-theory techniques, I will derive a non-local partial differential equation for the population density and compare its predictions with those obtained assuming well-mixed populations. Finally, I will discuss the ecological relevance of our results and how this approach can be extended to more complex scenarios.

Complex dynamic processes occurring in nature may be captured by time-lapse imaging.
However, understanding and reproducing these processes remains a challenge. These
processes range from mass transfer in fluids to the complex behaviour of live active matter
dynamics in cell motility driven by poxvirus infection spread in a monolayer of cells.
Understanding these processes can be attempted through time-lapse sequence synthesis by
means of generative modelling. Here, we present a novel method to predict behaviour from
video sequences, where the underlying mechanics are governed by differential equations with
known and unknown characteristics. Our method is an extension of residual video diffusion in
which we learn an approximation of the underlying differential equation separating the drift
term and the stochastic term. We evaluate it with the reaction-diffusion equation in which we
hide the inhibitor variable from the model and the incompressible Navier-Stokes equation with
a stochastic forcing parameter. Our model accurately predicts the inhibitor variable in the
reaction-diffusion equations and the stochastic forcing parameter in the Navier-Stokes
equation. Additionally, we evaluate the model's capability to learn distinct biological behaviours
of the active matter when trained on time-lapse microscopy of poxvirus spread phenotypes.
The results confirm the model's potential in capturing meaningful equation embeddings, thus
contributing to a deeper understanding of biological dynamics. To assess the accuracy of
poxvirus prediction, we measured the mean absolute error when close to the initial condition.
To evaluate the generation of longer sequences, we employed a qualitative analysis in which
our model achieved excellent results.

A large body of existing ecological theory relies on very strong and unrealistic assumptions about the way individuals move and get to interact with each other and with the environment. Specifically, several models assume that individuals behave like the molecules of an ideal gas: following completely random trajectories through the entire area occupied by the population and only interacting with each other when their trajectories intersect.

In this presentation, I will first discuss how traditional population dynamics models emerge from ideal gas assumptions. Then, I will present our ongoing research to refine those models using more elaborated tools from random walk theory, spatially-extended nonlinear dynamical systems, and stochastic calculus. I will discuss examples covering both the development of new theory and its application ecological data.

Recent years have witnessed the great success of deep learning algorithms in the geoscience and remote sensing (RS) realm. Nevertheless, the security and robustness of deep learning models deserve special attention when addressing safety-critical RS tasks. In this article, we provide a systematic analysis of backdoor attacks for RS data, where both scene classification and semantic segmentation tasks are considered. While most of the existing backdoor attack algorithms rely on visible triggers such as squared patches with well-designed patterns, we propose a novel wavelet transform-based attack (WABA) method, which can achieve invisible attacks by injecting the trigger image into the poisoned image in the low-frequency domain. In this way, the high-frequency information in the trigger image can be filtered out in the attack, resulting in stealthy data poisoning. Despite its simplicity, the proposed method can significantly cheat the current state-of-the-art deep learning models with a high attack success rate. We further analyze how different trigger images and the hyperparameters in the wavelet transform would influence the performance of the proposed method. Extensive experiments on four benchmark RS datasets demonstrate the effectiveness of the proposed method for both scene classification and semantic segmentation tasks and thus highlight the importance of designing advanced backdoor defense algorithms to address this threat in RS scenarios. The code will be available online at https://github.com/ndraeger/waba .

Despite the importance of ungulate migrations, we lack a complete understanding of why some ungulates species migrate and some do not. Moreover, at the population level, some migrate and others remain behind as residents, a phenomenon known as partial migration. Even though progress has been made towards understanding long-term fitness benefits of partial migration, the underlying decision-making process that makes some individuals migrate and others remain within one single range remain unknown. In this presentation I will combine empirical data from three different migrant ungulate species and mathematical modeling to address this question. I will first show that, across these three ungulate species, the number of residents is unrelated to total population size, a pattern predicted by no previous modeling framework. Next, I will introduce a new model of ungulate partial migration wherein individuals probabilistically decide to start migrating based on the intensity of environmental and social cues. Within this modeling framework, residents arise for most parameter combinations and the number of residents is largely invariant with total population size. Therefore, this new model explains the ubiquity of residents in migratory ungulate populations and presents novel patterns to be tested with further data collection.

From microbial colonies to entire landscapes, biological systems often self-organize into regular spatial patterns, which might have significant ecological consequences. Several models have been proposed to explain the emergence of these patterns. Most of them rely on a Turing-like activation-inhibition scale-dependent feedback whereby interactions favoring growth dominate at short distances and inhibitory, competitive interactions dominate in the long-range. However, the importance of short-range positive interactions for pattern formation remains disputable. Alternative theories predict their emergence from long-range inhibition alone. In this presentation, I will explain how self-organized patterns might emerge in purely competitive models. I will first present in which conditions long-range competition alone can generate regular patterns of population density in systems with one and two species. Then, I will discuss the ecological implications of those patterns both for population persistence and species coexistence.

Recent advances in artificial intelligence (AI) have significantly intensified research in the geoscience and remote sensing (RS) field. AI algorithms, especially deep learning-based ones, have been developed and applied widely to RS data analysis. The successful application of AI covers almost all aspects of Earth-observation (EO) missions, from low-level vision tasks like superresolution, denoising, and inpainting, to high-level vision tasks like scene classification, object detection, and semantic segmentation. Although AI techniques enable researchers to observe and understand the earth more accurately, the vulnerability and uncertainty of AI models deserve further attention, considering that many geoscience and RS tasks are highly safety critical. This article reviews the current development of AI security in the geoscience and RS field, covering the following five important aspects: adversarial attack, backdoor attack, federated learning (FL), uncertainty, and explainability. Moreover, the potential opportunities and trends are discussed to provide insights for future research. To the best of the authors’ knowledge, this article is the first attempt to provide a systematic review of AI security-related research in the geoscience and RS community. Available code and datasets are also listed in the article to move this vibrant field of research forward.

Earthquakes are the most destructive natural hazards because of their adversely severe impacts on urban areas. Earthquakes affect people's lives and properties, thus captivating the extensive attention of seismologists. Carrying out probability and hazard assessment for the prevention, and reduction of mega-events and recovery will be of great significance in affected areas. Given that limited studies have attempted to estimate earthquake Spatial Probability Assessment (SPA) in the Arabian Peninsula, this study aims to evaluate the SPA and Earthquake Hazard Assessment (EHA). This study implements and evaluates various machine learning and explainable-AI (XAI) techniques for the estimation of SPA and EHA in the Arabian Peninsula, explores the contribution and highlights the importance of different factors in the development of AI-based models. A total of twelve factors ranging from seismological to geophysical factors were evaluated. Two machine learning models namely Light Gradient Boosting Machine (LightGBM) and deep Recurrent Neural Networks (RNN) along with three XAI approaches (i.e, Smart predictor, Smart Explainer and Local Interpretable Model-Agnostic Explanation (LIME) model) were investigated. Results of the comparative earthquake SPA estimation demonstrated that the accuracy of 89% and 87% were achieved by LightGBM and RNN models. Moreover, the results of the XAI models show that the Smart Predictor provides better spatial outputs than the other evaluated XAI models. The stable factors identified by Smart Predictor were magnitude variation and earthquake frequency whereas the important factors were magnitude variation, earthquake frequency, depth variation, and seismic gap. Collectively, results of SPA show that, the Gulf of Aden, Red Sea, Iran, and Turkey are falling under a very-high SPA index (0.991–1). Correspondingly, Gulf areas, coastal areas of Saudi Arabia, and areas in the Zagros fault and Anatolian fault zone fall under a very-high hazard zone. This research could support planners, and decision-makers for emergency planning, infrastructure development, and reconstruction projects.

Introduction to the group research.

Water-limited ecosystems are incredibly complex systems covering 40% of Earth's land surface, mostly in developing countries, and are home to 35% of the world population. A paradigmatic property of these ecosystems is the spatial self-organization of vegetation, which leads to strikingly regular spatial distributions of plants. These self-organized spatial patterns have been suggested as important ecosystem health indicators. Specifically, pattern shapes may indicate the proximity of the ecosystem to undergo a desertification transition. Despite this potential ecological importance, the plant interactions that underlie pattern formation remain unclear. Without strong empirical evidence of how these patterns emerge, mathematical modeling has been crucial in formulating different hypotheses. However, models assuming different mechanistic origins reproduce the same series of patterns but predict contradicting ecological consequences for them. In this context, a new approach to understanding vegetation dynamics in water-limited ecosystems that focus on unveiling how plants interact with each other and how those interactions scale to large population sizes to create emergent patterns is needed.

In this presentation, I will first give an overview of existing models for vegetation pattern formation and their connection to well-known physical systems. Then, I will present our efforts to study vegetation dynamics in water-limited ecosystems, using a combination of models and greenhouse experiments.

Plant roots determine carbon uptake, survivorship, and agricultural yield and represent a large proportion of the world’s vegetation carbon pool. The study of below-ground plant growth, unlike aboveground shoot competition, is hampered by our inability to observe roots. We have few observations of intact root systems in soil and lack a comprehensive theory for root system responses to their environment.

In this presentation, I will first review previous theoretical efforts to explain plant below-ground competition and discuss how they lead to seemingly contradictory predictions. Then, I will introduce our recent theoretical and experimental work and show how it resolves existing controversy and provides a unifying framework to study below-ground plant interactions, both competitive and facilitative. I will conclude by discussing future research lines that depart from our results and how they can be addressed with extensions of our framework

Animals moving through landscapes need to strike a balance between finding sufficient resources to grow and reproduce while minimizing encounters with predators 1,2. Because encounter rates are determined by the average distance over which directed motion persists 1,3–5, this trade-off should be apparent in individuals’ movement. Using GPS data from 1,396 individuals across 62 species of terrestrial mammals, we show how predators maintained directed motion ~7 times longer than for similarly-sized prey, revealing how prey species must trade off search efficiency against predator encounter rates. Individual search strategies were also modulated by resource abundance, with prey species forced to risk higher predator encounter rates when resources were scarce. These findings highlight the interplay between encounter rates and resource availability in shaping broad patterns mammalian movement strategies.

Direct encounters, in which two or more individuals are physically close to one another, are a topic of increasing interest as more and better movement data become available. Recent progress, including the development of statistical tools for estimating robust measures of changes in animals’ space use over time, facilitates opportunities to link direct encounters between individuals with the long-term consequences of those encounters. Working with movement data for coyotes (Canis latrans) and grizzly bears (Ursus arctos horribilis), we investigate whether close intraspecific encounters were associated with spatial shifts in the animals’ range distributions, as might be expected if one or both of the individuals involved in an encounter were seeking to reduce or avoid conflict over space. We analyze the movement data of a pair of coyotes in detail, identifying how a shift in home range location resulting from altered movement behavior was apparently a consequence of a close intraspecific encounter. With grizzly bear 50 movement data, we approach the problem from the perspective of a set of encounter pairs within a population. We find support for the hypotheses that 1) close intraspecific encounters between bears are, on average, associated with subsequent shifts in range distributions and 2) encounters defined at finer spatial scales are followed by greater changes in space use. Our results suggest that animals can undertake long-term, large-scale spatial shifts in response to close intraspecific encounters that have the potential for conflict. These results lend support for existing theory on the evolution of territories and space use (e.g., Maynard-Smith’s bourgeois strategy regarding low-conflict coexistence). Overall, we find that analyses of movement data in a pairwise context can 1) identify distances at which individuals’ proximity to one another may alter behavior and 2) facilitate testing of population-level hypotheses concerning the potential for direct encounters to alter individuals’ space use.

Ecologists have long investigated how the demographic and movement parameters of a population determine its spatial spread and the critical habitat size that can sustain it. Yet, most existing models make oversimplifying assumptions about individual movement behavior, neglecting how landscape heterogeneity influences dispersal. We relax this assumption and introduce a reaction-advection-diffusion model that describes the spatial density distribution of a population with space-dependent movement bias toward preferred regions, including avoidance of degraded habitats. In this scenario, the critical habitat size depends on the spatial location of the habitat edges with respect to the preferred regions and on the intensity of the movement bias components. In particular, we identify parameter regions where the critical habitat size decreases when diffusion increases, a phenomenon known as the “drift paradox”. We also find that biased movement toward low-quality or highly populated regions can reduce the population size, therefore creating ecological traps. Our results emphasize the importance of species-specific movement behavior and habitat selection as drivers of population dynamics in fragmented landscapes and, therefore, the need to account for them in the design of protected areas.

Intraspecific interactions are key drivers of population dynamics because they establish relations between individual fitness and population density. The component Allee effect is defined as a positive correlation between any fitness component of a focal organism and population density, and it can lead to positive density dependence in the population per capita growth rate. The spatial population structure is key to determining whether and to which extent a component Allee effect will manifest at the demographic level because it determines how individuals interact with one another. However, existing spatial models to study the Allee effect impose a fixed spatial structure, which limits our understanding of how a component Allee effect and the spatial dynamics jointly determine the existence of demographic Allee effects. To fill this gap, we introduce a spatially-explicit theoretical framework where spatial structure and population dynamics are emergent properties of the individual-level demographic and movement rates. Depending on the intensity of the individual-level processes, the population exhibits a variety of spatial patterns, including evenly spaced aggregates of organisms, that determine the demographic-level by-products of an existing individual-level component Allee effect. We find that aggregation increases population abundance and allows populations to survive in harsher environments and at lower global population densities when compared with uniformly distributed organisms. Moreover, aggregation can prevent the component Allee effect from manifesting at the population level or restrict it to the level of each independent group. These results provide a mechanistic understanding of how component Allee effects might operate for different spatial population structures and show at the population level. Because populations subjected to demographic Allee effects exhibit highly nonlinear dynamics, especially at low abundances, our results contribute to a better understanding of population dynamics in the presence of Allee effects and can potentially inform population management strategies.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused a pandemic with millions of infections and deaths worldwide and devastating impact on global economy. Up to now, vaccines and monoclonal antibody (mAb) therapies lack to provide a long-lasting protection against rapidly evolving new emerging SARS-CoV-2 variants. Thus, novel therapeutic options are pressingly needed especially for immunocompromised patients and/or patients with high risk for developing a severe coronavirus disease 2019 (COVID-19).
In that regard, we developed a novel immunotherapeutic drug based on the SARS-CoV-2 entry receptor angiotensin-converting enzyme 2 (ACE2). This ACE2 decoy potently binds to the SARS-CoV-2 receptor binding domain (RBD), neutralizes SARS-CoV-2 as well as the Delta and Omicron variant and protects hamsters from a SARS-CoV-2 infection. To additionally use this ACE2 decoy for elimination of virus infected cells, we equipped it with an epitope tag. Thus, it can be applied as adapter molecule in the modular platform technologies UniMAB and UniCAR, which already demonstrated great success in the setting of malignant diseases. As adapter molecule the ACE2 decoy is able to efficiently recruit either universal chimeric antigen receptor (UniCAR) modified T cells or, in combination with an anti-peptide epitope-anti-CD3 bispecific Ab of the UniMAB system, unmodified T cells to efficiently kill SARS-CoV-2 RBD expressing human cells.
Taken together, the ACE2 decoy represents a very promising immunotherapeutic drug for both SARS-CoV-2 variant neutralization and infected cell killing via the UniMAB and UniCAR system and might, therefore, clearly improve the treatment of COVID-19 patients.

Marinobactins are a suite of amphiphilic siderophores (microbial iron-chelators) produced by Marinobacter sp. They belong to the hydroxamate class of siderophores and are composed of peptidic group linked with fatty acid chain that varies in length and saturation. Amphiphilic nature of these siderophores and metal complexation ability make them an interesting molecule for an application in the flotation process. In this work, marinobactin was investigated as a potential Gallium (Ga) collector in an ion flotation process. Single metal flotation test suggested the Ga recovery and marinobactin-Ga complexation in the collected concentrates was confirmed by HPLC. Further, preliminary flotation test with metal mixture (1mM) containing both Ga and Arsenic (As) revealed nearly 60% and 24% recovery of Ga and As respectively, at pH 7 and 0.5mM marinobactin concentration. This study shows the feasibility of employing novel siderophores in the ion flotation process for the recovery of metals from low concentrated wastewater.

Background
There are only a handful of true theranostic matched pairs, and in particular the theranostic radiocopper trio 61Cu, 64Cu and 67Cu, for diagnosis and therapy respectively, is a very attractive candidate. In fact, the alternative of two imaging radionuclides with different half-lives is a clear advantage over other theranostic pairs, since it offers a better matching for the tracer biological and radionuclide physical half-lives. Due to the high availability of 64Cu, its translation into the clinic is being successfully carried out, giving the example of the FDA approved radiopharmaceutical Detectnet (copper Cu 64 dotatate injection). However, a shorter-lived PET radionuclide such as 61Cu may as well be beneficial.
Results
Proton irradiation of enriched 62Ni electrodeposited targets with a compact cyclotron produced the desired radionuclide via the 62Ni(p,2n)61Cu nuclear reaction, leading to 61Cu activities of up to 20 GBq at end of bombardment and 8 GBq at end of purification. Furthermore, two purification methods are compared leading to comparable results regarding separation yield and product purity. Following the radiochemical separation, quality assessment of this product [61Cu]CuCl2 solution proved radionuclidic purities (RNP) over 99.6 % and apparent molar activities (AMA) of 260 GBq/µmol with the 1,4,8,11-tetraazacyclotetradecane-1,4,8,11-tetraacetic acid (TETA) chelator, end of purification corrected.
Conclusions
In the current article a comprehensive novel production method for the PET radionuclide 61Cu is presented, providing an alternative to the most popular production routes. Characterization of the [61Cu]CuCl2 product showed both high RNP as well as high AMA, proving that the produced activity presented high quality regarding radiolabeling up to 9 hours after EOP. Furthermore, production scalability could be easily achieved by increasing the irradiation time.

The Open Standard for Particle-Mesh Data (openPMD) is a F.A.I.R. metadata standard for tabular (particle/dataframe) and structured mesh data in science and engineering.
We show the basic components of openPMD, its extensions to specific domains, applications from laser-plasma physics, particle accelerators, material physics to imaging and the ability to bridge multiple heterogeneous scientific models with a commonly-understood markup.

The openPMD-api builds upon established portable I/O formats such as HDF5 and ADIOS2, enabling workflows that scale from single-user computers up to Exascale simulations, in-transit data processing, 3D visualization, GPU-accelerated data analytics and AI/ML. openPMD links into the existing ecosystems of its scalable I/O backends and extends them with tooling that understands the openPMD data markup.
An overview over the openPMD ecosystem and community is shown.

Attention is given to recent developments in openPMD that interplay with HDF5, including mesh refinement and the Helmholtz Metadata Collaboration's HELPMI project which aims for an easier integration of openPMD with other HDF5-based standards, this way bringing openPMD closer to experiment workflows.

References:

[1] Axel Huebl, Remi Lehe, Jean-Luc Vay, David P. Grote, Ivo F. Sbalzarini, Stephan Kuschel, David Sagan, Christopher Mayes, Frederic Perez, Fabian Koller, and Michael Bussmann. “openPMD: A meta data standard for particle and mesh based data,” DOI:10.5281/zenodo.591699 (2015)
[2] Homepage: https://www.openPMD.org
[3] GitHub Organization: https://github.com/openPMD
[4] Projects using openPMD: https://github.com/openPMD/openPMD-projects
[4] Reference API implementation: Axel Huebl, Franz Poeschel, Fabian Koller, and Junmin Gu. “openPMD-api 0.14.3: C++ & Python API for Scientific I/O with openPMD,” DOI:10.14278/rodare.1234 (2021)
https://openpmd-api.readthedocs.io
[5] Selected earlier presentations on openPMD:
https://zenodo.org/search?page=1&size=20&q=openPMD&type=presentation
[6] Axel Huebl, Rene Widera, Felix Schmitt, Alexander Matthes, Norbert Podhorszki, Jong Youl Choi, Scott Klasky, and Michael Bussmann. “On the Scalability of Data Reduction Techniques in Current and Upcoming HPC Systems from an Application Perspective,” ISC High Performance 2017: High Performance Computing, pp. 15-29, 2017. arXiv:1706.00522, DOI:10.1007/978-3-319-67630-2_2
[7] Franz Poeschel, Juncheng E, William F. Godoy, Norbert Podhorszki, Scott Klasky, Greg Eisenhauer, Philip E. Davis, Lipeng Wan, Ana Gainaru, Junmin Gu, Fabian Koller, Rene Widera, Michael Bussmann, and Axel Huebl. Transitioning from file-based HPC workflows to streaming data pipelines with openPMD and ADIOS2, Part of Driving Scientific and Engineering Discoveries Through the Integration of Experiment, Big Data, and Modeling and Simulation, SMC 2021, Communications in Computer and Information Science (CCIS), vol 1512, 2022. arXiv:2107.06108, DOI:10.1007/978-3-030-96498-6_6
[8] The Helmholtz Metadata Collaboration's ongoing HELPMI project: https://helmholtz-metadaten.de/de/inf-projects/helpmi-helmholtz-laser-plasma-metadata-initiative

The general SW–NE course of the Variscan orogen in Europe is abruptly bent to the N–S course at its eastern margin, where an oblique convergence occurred. The main suture in this part of the Variscan orogenic belt is called the Moldanubian Thrust, characterized by a dominant dextral strike‑slip kinematics and a minor thrust component. The deep level of erosion and the good exposure of this structure allowed us to study the mechanisms of oblique convergence and the incorporation of the foreland basement into the orogenic belt. The combination of small‑scale structures with the anisotropy of magnetic susceptibility studies allowed the recognition of two deformations in the studied rocks: dextral simple shearing and drag folding. Due to oblique convergence, the deformations induced by this mechanism were non‑coaxial; therefore, their contributions can be easily distinguished. Finally, an overturned, almost recumbent large‑scale synformal fold structure in the footwall and an antiformal structure in the hanging wall of the Moldanubian Thrust were formed. These two folds can be interpreted as structures formed by dragging along the Moldanubian Thrust. The previously described sinistral simple shearing in the upper limb of the synform resulted from the original dextral strike‑slip shearing, which was overturned during progressive deformation.

The whole-rock major, trace element and isotope geochemical tables, magnetic fabrics source data and details of methods.

The evolution of eruptive vents related to calderas is not fully understood. We focus on a structural, rock-magnetic, and geochemical investigation of a ∼314 Ma rhyolite dyke swarm associated with the late-orogenic Altenberg–Teplice Caldera, Bohemian Massif, eastern Variscan belt. The whole-rock major element, trace element, and Nd–Pb isotope geochemistry along with the published U-Pb zircon geochronology link the extra-caldera dyke swarm with intra-caldera ignimbrites. The magnetic fabrics determined using the anisotropy of magnetic susceptibility are interpreted to record a continuum from magma ascent, emplacement, and eruption during sinistral shearing. The latter evidences an interplay with regional tectonics associated with the activity of crustal-scale shear zones. The sinistral kinematics and strike of the dyke swarm, the elongation of caldera intrusive units, and the kinematics of major caldera faults are consistent with the dextral Riedel shear system, where the dykes correspond to antithetic Ŕ/X-shears. Such a kinematic configuration implies that the maximum and minimum principal stresses were oriented roughly north-south and east-west, respectively. The relation between the stress field with respect to the caldera elongation and orientation is not typical. We suggest that a pre-existing mutually perpendicular set of cross-cutting structural lineaments largely controlled the magma chamber and caldera formation.

Deforestation has become a major cause of climate change, and as a result, both characterizing the drivers and estimating segmentation maps of deforestation have piqued the interest of researchers. In the computer vision domain, Vision Transformers (ViTs) have shown their superiority compared to extensively utilized convolutional neural networks (CNNs) over the last couple of years. Although, ViTs has several challenges, specifically in remote sensing image processing, including their significant complexity that increases the computation costs and their need for much higher reference data than that of CNNs. As such, in this paper, we introduce an attention gates aided TransU-Net, called TransU-Net++ for semantic segmentation with an application of deforestation mapping in two South American forest biomes, i.e., the Atlantic Forest and the Amazon Rainforest. The heterogeneous kernel convolution (HetConv), U-Net, attention gates, and ViTs are all utilized in the proposed TransU-Net++ to their advantage. The TransU-Net++ significantly increased the performance of TransU-Net’s over the Atlantic Forest dataset by about 4%, 6%, and 16%, respectively, in terms of overall accuracy, F1-score, and recall, respectively.Moreover, the results show that the developed TrasnU-Net++ model (0.921) achieves the highest Area under the ROC Curve value in the 3-band Amazon forest dataset as compared to other segmentation models, including ICNet (0.667), ENet (0.69), SegNet (0.788), U-Net (0.871), Attention U-Net-2 (0.886), R2U-Net (0.888), TransU-Net (0.889), Swin U-Net (0.893), ResU-Net (0.896), U-Net+++ (0.9), and Attention U-Net (0.908), respectively. The code will be made publicly available at https://github.com/aj1365/TransUNetplus2.

Intralogistik gewinnt bei der Produktionssteuerung für die Organisation und Optimierung von Zulieferung und Warenumschlag stetig an Bedeutung. Darüber hinaus werden durch Intralogistik innerbetriebliche Materialflüsse und Informationsströme gesteuert und – wenn möglich – die Produktionslogistik intelligent gesteuert.

Die Intralogistik von Aufbereitungs- und Recyclingprozesse unterscheidet sich erheblich von der Intralogistik bei Produktionsprozessen. Bei Herstellung von Produkten und Halbzeugen sind Eigenschaften von Ausgangsmaterialien meist chargengenau bekannt. Während bei der Aufbereitung von Bergbauhalden oder dem Recycling die relevanten Stoffe in den Ausgangsmaterialien in ihrer Zusammensetzung, Qualität und Quantität höchst inhomogen verteilt und weitgehend unbekannt sind. Die Intralogistik bei solchen Prozessen ist hochkomplex und muss daher bei der dynamischen Analyse des Ausgangsmaterials beginnen und mit Ergebnissen des Aufbereitungsprozesses enden. Die Steuerung des Aufbereitungsprozesses muss dynamisch und datengesteuert angepasst werden.

Am Beispiel der Aufbereitung von Haldenmaterial mit Hilfe der Flotation soll die Verknüpfung der Datenerfassung des Aufgabegutes mit der Prozesssteuerung, der Intralogisitk und weiteren Verarbeitungs- und Optimierungsschritten gezeigt werden.

The Ramsar Convention of 1971 encourages wetland preservation, but it is unclear how climate change will affect wetland extent and related biodiversity. Due to the use of the self-attention mechanism, vision transformers (ViTs) gain better modeling of global contextual information and become a powerful alternative to Convolutional Neural Networks (CNNs). However, ViTs require enormous training datasets to activate their image classification power, and gathering training samples for remote sensing applications is typically costly. As such, in this study, we develop a deep learning algorithm called (WetMapFormer), which effectively utilizes both CNNs and vision transformer architectures for precise mapping of wetlands in three pilot sites around the Albert county, York county, and Grand Bay-Westfield located in New Brunswick, Canada. The WetMapFormer utilizes local window attention (LWA) rather than the conventional self-attention mechanism for improving the capability of feature generalization in a local area by substantially reducing the computational cost of vanilla ViTs. We extensively evaluated the robustness of the proposed WetMapFormer with Sentinel-1 and Sentinel-2 satellite data and compared it with the various CNNs and vision transformer models which include ViT, Swin Transformer, HybridSN, CoAtNet, a multimodel network, and ResNet, respectively. The proposed WetMapFormer achieves F-1 scores of 0.94, 0.94, 0.96, 0.97, 0.97, 0.97, and 1 for the recognition of aquatic bed, freshwater marsh, shrub wetland, bog, fen, forested wetland, and water, respectively. As compared to other vision transformers, the WetMapFormer limits receptive fields while adjusting translational invariance and equivariance characteristics. The codes will be made available publicly at https://github.com/aj1365/WetMapFormer.

Mit der weltweit einmaligen Forschungsinfrastruktur FlexiPlant wollen wir Rohstoffe aller Art energie- und ressourceneffizient zurückgewinnen. Dafür entwickeln wir eine neue Generation adaptiver, flexibler & digitalisierter Aufbereitungstechnologien.

In recent years, advanced research has focused on the direct learning and analysis of remote-sensing images using natural language processing (NLP) techniques. The ability to accurately describe changes occurring in multi-temporal remote sensing images is becoming increasingly important for geospatial understanding and land planning. Unlike natural image change captioning tasks, remote sensing change captioning aims to capture the most significant changes, irrespective of various influential factors such as illumination, seasonal effects, and complex land covers. In this study, we highlight the significance of accurately describing changes in remote sensing images and present a comparison of the change captioning task for natural and synthetic images and remote sensing images. To address the challenge of generating accurate captions, we propose an attentive changes-to-captions network, called Chg2Cap for short, for bi-temporal remote sensing images. The network comprises three main components: 1) a Siamese CNN-based feature extractor to collect high-level representations for each image pair; 2) an attentive encoder that includes a hierarchical self-attention block to locate change-related features and a residual block to generate the image embedding; and 3) a transformer-based caption generator to decode the relationship between the image embedding and the word embedding into a description. The proposed Chg2Cap network is evaluated on two representative remote sensing datasets, and a comprehensive experimental analysis is provided. The code and pre-trained models will be available online at https://github.com/ShizhenChang/Chg2Cap .

The accurate and fast assessment of damaged buildings following a disaster is critical for planning rescue and reconstruction efforts. The damage assessment by the traditional methods is time-consuming and with limited performance. In this article, we propose an end-to-end deep-learning network named building damage detection network-plus (BDD-Net+). The BDD-Net+ is based on a combination of convolution layers and transformer blocks. The proposed framework takes the advantage of the multiscale residual convolution blocks and self-attention layers. The proposed framework consists of four main steps: data preparation, model training, damage map generation and evaluation, and the use of an explainable artificial intelligence (XAI) framework for understanding and interpretation of the operation model. The experimental results include two representative real-world benchmark datasets (i.e., the Haiti earthquake and the Bata explosion). The obtained results illustrate that BDD-Net+ achieves excellent efficacy in comparison with other state-of-the-art methods. Furthermore, the visualization of the results by XAI shows that BDD-Net+ provides more interpretable and explainable results for damage detection than the other studied methods.

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. Yet, the interpretation of these experiments requires a reliable diagnostics
based on accurate theoretical modeling, which is a notoriously difficult task [1].

In this talk, I will explain how we can use X-ray Thomson scattering (XRTS) measurements [2] to infer important system parameters such as the temperature, density, and degree of ionization. Interestingly, standard forward modeling methods based on the widespread Chihara decomposition have neglected transitions between free and bound electrons (the inverse process of the usual bound-free transitions), which are negligible at ambient conditions, but become important in the WDM regime [3]. In addition, I will show how switching to the imaginary-time representation opens up new avenues towards the model-free interpretation of XRTS signals, and gives one direct access to the temperature [4,5] and electronic correlations [6] of the system. Finally, I will outline new PIMC capabilities [7,8] that allow for quasi-exact simulations of experiments conducted at the Gbar platform at the National Ignition Facility (NIF) in Livermore.

[1] M. Bonitz et al, Physics of Plasmas 27, 042710 (2020)
[2] S. Glenzer and R. Redmer, Reviews of Modern Physics 81, 1625 (2009)
[3] M. Böhme et al, arXiv:2306.17653 (submitted)
[4] T. Dornheim et al, Nature Communications 13, 7911 (2022)
[5] T. Dornheim et al, Physics of Plasmas 30, 042707 (2023)
[6] T. Dornheim et al, arXiv:2305.15305 (submitted)
[7] M. Böhme et al, Physical Review Letters 129, 066402 (2022)
[8] T. Dornheim et al, Journal of Chemical Physics 159, 164113 (2023)

Impact of hydrodynamics on flotation kinetics has been heavily studied but research concerning their effect on complex individual particles recovery is lacking. This study aims at understanding the effect of superficial gas velocity (Jg) and impeller tip speed (Vs) on the flotation kinetics of a complex porphyry copper ore by investigating the recovery of individual particles in relation to their particulate properties (size, shape, mineral composition, i.e. surface liberation, mineral association and texture). Experiments were performed on FLSmidth’s 6L nextSTEPTM flotation cell following a full-factorial DoE approach. Jg (0.40–0.50 cm/s) and Vs (4.20–5.50m/s) were used as process parameters at constant pulp density, pH, and reagent dosages. Particle datasets pertaining all products were collected using 2D-automated mineralogy. Logistic regression-based models were trained using experimental data to compute the recovery probabilities of each particle at different operating conditions. This served to quantify the influence of hydrodynamics and particle properties on the process behavior of the main ore mineral (chalcopyrite), and gangue minerals including pyrite, quartz, micas, and other silicates.

Oil spill detection has attracted increasing attention in recent years, since marine oil spill accidents severely affect environments, natural resources, and the lives of coastal inhabitants. Hyperspectral remote sensing images provide rich spectral information which is beneficial for the monitoring of oil spills in complex ocean scenarios. However, most of the existing approaches are based on supervised and semi-supervised frameworks to detect oil spills from hyperspectral images (HSIs), which require a massive amount of effort to annotate a certain number of high-quality training sets. In this study, we make the first attempt to develop an unsupervised oil spill detection method based on isolation forest (iForest) for HSIs. First, a Gaussian statistical model is designed to remove the bands corrupted by severe noise. Then, kernel principal component analysis (KPCA) is employed to reduce the high dimensionality of the HSIs. Next, the probability of each pixel belonging to one of the classes of seawater and oil spills is estimated with the iForest, and a set of pseudolabeled training samples is automatically produced using the clustering algorithm on the detected probability. Finally, an initial detection map can be obtained by performing the support vector machine (SVM) on the dimension-reduced data, and the initial detection result is further optimized with the extended random walker (ERW) model so as to improve the detection accuracy of oil spills. Experiments on hyperspectral oil spill database (HOSD) created by ourselves demonstrate that the proposed method obtains superior detection performance with respect to other state-of-the-art detection approaches. We will make HOSD and our developed library for oil spill detection publicly available at https://github.com/PuhongDuan/HOSD to further promote this research topic.

The accurate description of non-ideal quantum many-body systems is of prime importance for a host of applications within physics, quantum chemistry, material science, and related disciplines. At finite temperatures, the gold standard is given by \textit{ab initio} path integral Monte Carlo (PIMC) simulations, which do not require any empirical input, but exhibit an exponential increase in the required compute time for fermionic systems with increasing the system size N. Very recently, it has been suggested to compute fermionic properties without this bottleneck based on PIMC simulations of fictitious identical particles. In the present work, we use this technique to carry out very large (N≤1000) PIMC simulations of the warm dense electron gas and demonstrate that it is capable of providing a highly accurate description of investigated properties, i.e., the static structure factor, the static density response function, and local field correction, over the entire range of length scales.

This article presents FLGC, a simple yet effective fully linear graph convolutional network for semi-supervised and unsupervised learning. Instead of using gradient descent, we train FLGC based on computing a global optimal closed-form solution with a decoupled procedure, resulting in a generalized linear framework and making it easier to implement, train, and apply. We show that (1) FLGC is powerful to deal with both graph-structured data and regular data, (2) training graph convolutional models with closed-form solutions improve computational efficiency without degrading performance, and (3) FLGC acts as a natural generalization of classic linear models in the non-Euclidean domain (e.g., ridge regression and subspace clustering). Furthermore, we implement a semi-supervised FLGC and an unsupervised FLGC by introducing an initial residual strategy, enabling FLGC to aggregate long-range neighborhoods and alleviate over-smoothing. We compare our semi-supervised and unsupervised FLGCs against many state-of-the-art methods on a variety of classification and clustering benchmarks, demonstrating that the proposed FLGC models consistently outperform previous methods in terms of accuracy, robustness, and learning efficiency. The core code of our FLGC is released at https://github.com/AngryCai/FLGC.

Convolutional neural networks (CNNs) have recently found great attention in image classification since deep CNNs have exhibited excellent performance in computer vision. Owing to their immense success, of late, scientists are exploring the functionality of transformers in Earth observation applications. Nevertheless, the primary issue with transformers is that they demand significantly more training data than CNN classifiers. Thus, the use of these transformers in remote sensing is considered challenging, notably in utilizing polarimetric synthetic aperture radar (PolSAR) data, due to the insufficient number of existing labeled data. In this letter, we develop and propose a vision transformer (ViT)-based framework that utilizes 3-D and 2-D CNNs as feature extractors and, in addition, local window attention (LWA) for the effective classification of PolSAR data. Extensive experimental results demonstrated that the developed model PolSARFormer obtained better classification accuracy than the state-of-the-art vision Swin Transformer and FNet algorithms. The PolSARFormer outperformed the Swin Transformer and FNet by the margin of 5.86% and 17.63%, in terms of average accuracy (AA) in the San Francisco data benchmark. Moreover, the results over the Flevoland dataset illustrated that the PolSARFormer exceeds several other algorithms, including the ResNet (97.49%), Swin Transformer (96.54%), FNet (95.28%), 2-D CNN (94.57%), and AlexNet (91.83%), with a kappa index (KI) of 99.30%. The code will be made available publicly at https://github.com/aj1365/PolSARFormer .

In this presentation, I will present our recent progress in integrating machine learning to significantly boost the computational efficiency of electronic structure calculations [1]. I will specifically address our efforts to speed up density functional theory calculations, for which we have developed the Materials Learning Algorithms framework [2]. Our findings illustrate significant improvements in calculation speed for metals at their melting point. Additionally, our use of automated machine learning has yielded significant reductions in computational resources required to identify optimal neural network architectures, laying the groundwork for extensive investigations [3]. Furthermore, I will show the transferability of our ML model across temperatures [4]. Most importantly, I will present our latest breakthrough, which enables fast neural-network driven electronic structure calculations for systems unattainable by conventional density functional theory calculations [5].

References
[1] L. Fiedler, K. Shah, M. Bussmann, A. Cangi, Phys. Rev. Materials, 6, 040301 (2022).
[2] J. Ellis, L. Fiedler, G. Popoola, N. Modine, J. Stephens, A. Thompson, A. Cangi, S. Rajamanickam, Phys. Rev. B, 104, 035120 (2021).
[3] L. Fiedler, N. Hoffmann, P. Mohammed, G. Popoola, T. Yovell, V. Oles, J. Austin Ellis, S. Rajamanickam, A. Cangi, Mach. Learn.: Sci. Technol., 3, 045008 (2022).
[4] L. Fiedler, N. A. Modine, K. D. Miller, and A. Cangi Phys. Rev. B 108, 125146 (2023).
[5] L. Fiedler, N. Modine, S. Schmerler, D. Vogel, G. Popoola, A. Thompson, S. Rajamanickam, A. Cangi, npj. Comput. Mater., 9, 115 (2023).

There is a close and multifaceted relation between fluid dynamics and
the charge/discharge behavior of liquid metal batteries. The talk will
give an overview of numerical and experimental work on transport
phenomena in liquid metal batteries and discuss how they influence
the cycling behavior of the cells.

The Energy Hub of the Helmholtz Metadata Collaboration (HMC) conducted interviews with various stakeholders from the Helmholtz Research Field Energy on the topic of research data management (RDM) in 2022. The intentions were to build and serve a metadata community in the energy research field and to extend the Helmholtz-wide survey conducted by HMC in 2021 Arndt et al., 2022). Besides the deeper insight into the current state of RDM and metadata handling at the Helmholtz sites relevant to the Energy Hub the interviews focused on the related needs and difficulties of researchers and their satisfaction with the current state. Furthermore, we tried to discover already existing workflows and software solutions, to establish contacts and to make HMC better known.

Swelling is the most fundamental property of graphite oxides (GO). Here, a structural study of Brodie graphite oxide (BGO) swelling in a set of long chain 1-alcohols (named C11 to C22 according to the number of carbons) performed using synchrotron radiation X-ray diffraction at elevated temperatures is reported. Even the longest of tested alcohols (C22) is found to intercalate BGO with enormous expansion of the interlayer distance from ≈6Å up to ≈63Å, the highest expansion of GO lattice ever reported. Swelling transitions from low temperature alpha-phase to high temperature beta-phase are found for BGO in all alcohols in the C11–C22 set. The transitions correspond to decrease of inter-layer distance correlating with the length of alcohol molecules, and change in their orientation from perpendicular to GO planes to layered parallel to GO (Type II transitions). These transitions are very different compared to BGO swelling transitions (Type I) found in smaller alcohols and related to insertion/de-insertion of additional layer of alcohol parallel to GO. Analysis of general trends in the whole set of 1-alcohols (C1 to C22) shows that the 1-alcohol chain length defines the type of swelling transition with Type I found for alcohols with C<10 and Type II for C>10.

Tutorial on geometry optimization in 2D materials.

Hands-on introduction to the AFLOW software for materials design.

The predictive power of physical theories has led to remarkable findings such as the discovery of planets,
new elementary particles, and gravitational waves. The Dresden-concept group “Autonomous Materials
Thermodynamics – AutoMaT” leverages ab initio density functional theory as a predictive tool for
materials design. We specifically focus on the data-driven discovery of novel two-dimensional (2D)
materials for future electronics and energy applications with strong partners from HZDR,
Forschungszentrum Jülich, the DFG collaborative research center “Synthetic Two-dimensional
Materials” hosted at TU Dresden, and Duke University (United States).
Two-dimensional (2D) materials are traditionally derived from bulk layered compounds. The recent
surprising experimental realization of some 2D sheets obtained from non-layered crystals [1,2]
foreshadows a new direction for this diverse class of nanostructures. Generalizing these findings, we
recently predicted by data-driven methods and autonomous ab initio calculations a large set of novel
representatives [3,4] (see Figure 1). They exhibit diverse magnetic properties such as complex surface
spin polarizations enabling spintronics. These systems are thus an attractive platform for fundamental and
applied nanoscience.
[1] A. Puthirath Balan et al., Nat. Nanotechnol. 13, 602 (2018).
[2] A. Puthirath Balan et al., Chem. Mater. 30, 5923 (2018).
[3] R. Friedrich et al., Nano Lett. 22, 989 (2022).
[4] T. Barnowsky et al., Adv. Electron. Mater. 2201112 (2023).

Every technology is intimately connected to a certain materials platform. Information technology is based
on silicon, modern batteries are made from lithium compounds, and magnetic materials are important for
the energy sector. Moreover, interfaces between materials are often the enabler of dedicated
functionalities calling for not only the design of individual materials but also of their interfaces.
The Dresden-concept group “Autonomous Materials Thermodynamics – AutoMaT” leverages state of the
art predictive computational methods for materials and interface design. We specifically focus on the
data-driven discovery of novel two-dimensional (2D) materials for future electronics and energy
applications with strong partners from the DFG collaborative research center 1415 “Synthetic Two-dimensional
Materials” hosted at TU Dresden, HZDR, Forschungszentrum Jülich, and Duke University
(United States).
Two-dimensional materials are traditionally derived from bulk layered compounds. The recent surprising
experimental realization of some 2D sheets obtained from non-layered crystals foreshadows a new
direction for this diverse class of nanostructures. Generalizing these findings, we recently predicted by
data-driven methods and autonomous ab initio calculations a large set of novel representatives. They
exhibit appealing magnetic properties enabling spintronics. These systems and their interfaces are thus an
attractive platform for fundamental and applied nanoscience.

Invited talk at the HZDR data management day.

Invited talk at the AFLOW seminar series.

Ion irradiation can cause burrowing of nanoparticles in substrates, strongly depending on the material properties and irradiation parameters. In this study, it is demonstrated that the sinking process can be accomplished with ion irradiation of cube-shaped Ag nanoparticles on top of silicon; how ion channeling affects the sinking rate; and underline the importance of the amorphous state of the substrate upon ion irradiation. Based on these experimental findings, the sinking process is described as being driven by capillary forces enabled by ion-induced plastic flow of the substrate.

Vorstellung der FORKA-Projekte EMPRADO und WERREBA

To help with the decommissioning of the unit 2 of the Greifswald NPP, this study aims at determining the activities of 3H, 14C, 60Co, 152Eu and 154Eu in the concrete to estimate the maximal activity in the entire bioshield.
The study will focus on the activity as a function of depth in the concrete layer, as well as composition of the mineral phases. As the flux of neutrons generated during fission reaction encounters the mineral phases of concrete, the natural elements present in these phases absorb neutrons, which leads to the formation of their radioactive isotopes. Therefore, the elemental composition of each mineral phase in the concrete is important in the activation process, and the concrete being a heterogeneous material, different phases will present different activities.
A precise knowledge of the activities and of the elemental composition of the concrete and its mineral phases helps refining the models to calculate and predict the activities in a long-term scale. The calculations and the experimental results support the sorting of the materials for disposal.
This study will analyze the concrete with three main objectives:

• Activation;
• Chemical composition;
• Structure of the concrete and mineral phases.