Publications Repository - Helmholtz-Zentrum Dresden-Rossendorf

The Helmholtz Association is a pioneer in the establishment of research software guidelines and policies in the German research landscape. The roots go back to one of the first German RSE focused workshops, which took place in Dresden in 2016. Since then, the field of RSE has been successively expanded at various levels through the provision of training and support services, technical platforms and, last but not least, the development of guidelines and policies. In context of research data management, a similar process has been driven with a strong focus on research data policies and data management plans. Guidelines for software development are just as important in modern research, but have hardly been established to date.

The talk is a progress report on the activities and results that have been achieved in the Helmholtz Centers in recent years. We present concrete examples with facts, statistics and user experience reports. In addition, we also share our experiences on how to actively stimulate this process, for example, through awards and visible indicators.The policy implementation at Helmholtz is ongoing and is actively supported in regular Helmholtz-wide research software forums organized by the Helmholtz Incubator Platform HIFIS and the Task Group Research Software of the Helmholtz Open Science Working Group.

We develop a Transformer-based model enhanced with a Channel Attention Module (CAM) to capture the inter-channel dependencies in 3D hyperspectral point cloud data for geological applications. We hypothesize that specific channels of hyperspectral data correspond to distinct mineral types, and therefore, exploiting the relationships among these channels is beneficial for our analysis. We evaluate our method using the newly released Tinto dataset, which consists of 3D hyperspectral point clouds featuring three different spectral ranges: LongWave Infrared (LWIR), ShortWave Infrared (SWIR), and Visible-Near Infrared (VNIR).We explore four different CAMs from various networks—SENet, ECANet, CBAM, and DANet—and successfully integrate them into a CNN-based model to enhance feature representation. We specifically tailor the channel attention to our use of 3D hyperspectral point cloud data. Our experiments demonstrate significant improvements in performance after incorporating the CAM into our backbone model, which draws inspiration from the Point Cloud Transformer architecture and Vector Self-Attention mechanism. These results highlight the potential for further research into enhancing classification accuracy using hyperspectral data in geological applications. The code will be released on https://github.com/aldinorizaldy/CAM-Transformer.

Transformer-based models have achieved state-of-the-art results in point cloud segmentation. However, their evaluation has been limited to benchmark data with natural objects. In this study, we present the first investigation of Transformers for hyperspectral point clouds, comparing different attention modules. We utilize the Tinto dataset, which provides extensive hyperspectral features for geological applications, offering diverse benchmarking settings. Our experiments demonstrate that the Transformer with vector attention surpasses the commonly-used dot-product scalar attention. Moreover, this model achieves significantly higher accuracy scores than the well-known point cloud models, PointNet and PointNet++, across three hyperspectral sensors.

Recently, MnTe was established as an altermagnetic material that hosts spin-polarized electronic bands as well as anomalous transport effects like the anomalous Hall effect. In addition to these effects arising from altermagnetism, MnTe also hosts other magnetoresistance effects. Here, we study the manipulation of the magnetic order by an applied magnetic field and its impact on the electrical resistivity. In particular, we establish which components of anisotropic magnetoresistance are present when the magnetic order is rotated within the hexagonal basal plane. Our experimental results, which are in agreement with our symmetry analysis of the magnetotransport components, showcase the existence of an anisotropic magnetoresistance linked to both the relative orientation of current and magnetic order, as well as crystal and magnetic order. Altermagnetism is manifested as a three-fold component in the transverse magnetoresistance which arises due to the anomalous Hall effect.

We report on a quantum critical behavior in the quasi-1D spin-1/2 zigzag frustrated chain antiferromagnet CaCoV₂O₇, induced by an applied magnetic field. Below T_N = 3.3 K our zero-field neutron diffraction studies revealed the up-up-down-down spin structure, stabilized by an order-by-disorder phenomenon. At base temperature, the magnetic order is suppressed by an applied magnetic field (B), inducing a transition into a quantum paramagnetic state at B_c = 3 T, as revealed by both neutron diffraction and ESR data. The transition exhibits an unusually sharp phase boundary with the critical exponent φ = 0.164(3) ≈ 1/6, in contrast to the earlier experimental observations for uniform spin-1/2 chain systems. Such a sharp QPT is anticipated due to a rare combination of spin-orbit coupling and competing NN and NNN exchange interactions J₁ and J₂ of the zigzag spin chain.

Purpose: 68Ga-labeled fibroblast activation protein inhibitor (FAPI) is a novel PET tracer with great potential for staging pancreatic cancer. Data on locally advanced or recurrent disease is sparse, especially on tracer uptake before and after high dose chemoradiotherapy (CRT). The aim of this study was to evaluate [68Ga]Ga-FAPI-46 PET/CT staging in this setting.

Methods: Twenty-seven patients with locally recurrent or locally advanced pancreatic adenocarcinoma (LRPAC n = 15, LAPAC n = 12) in stable disease or partial remission after chemotherapy underwent FAPI PET/CT and received consolidation CRT in stage M0 with follow-up FAPI PET/CT every three months until systemic progression. Quantitative PET parameters SUVmax, SUVmean, FAPI-derived tumor volume and total lesion FAPI-uptake were measured in baseline and follow-up PET/CT scans. Contrast-enhanced CT (ceCT) and PET/CT data were evaluated blinded and staged according to TNM classification.

Results: FAPI PET/CT modified staging compared to ceCT alone in 23 of 27 patients in baseline, resulting in major treatment alterations in 52% of all patients (30%: target volume adjustment due to N downstaging, 15%: switch to palliative systemic chemotherapy only due to diffuse metastases, 7%: abortion of radiotherapy due to other reasons). Regarding follow-up scans, major treatment alterations after performing FAPI PET/CT were noted in eleven of 24 follow-up scans (46%) with switch to systemic chemotherapy or best supportive care due to M upstaging and ablative radiotherapy of distant lymph node and oligometastasis. Unexpectedly, in more than 90 % of the follow-up scans, radiotherapy did not induce local fibrosis related FAPI uptake.

During the first follow-up, all quantitative PET metrics decreased, and irradiated lesions showed significantly lower FAPI uptake in locally controlled disease (SUVmax p = 0.047, SUVmean p = 0.0092) compared to local failure.

Conclusion: Compared to ceCT, FAPI PET/CT led to major therapeutic alterations in patients with LRPAC and LAPAC prior to and after radiotherapy, which might help identify patients benefiting from adjustments in every treatment stage. FAPI PET/CT should be considered a useful diagnostic tool in LRPAC or LAPAC before and after CRT.

Background

PSMA-PET is increasingly used for staging prostate cancer (PCA) patients. However, it is not clear if quantitative imaging parameters of positron emission tomography (PET) have an impact on disease progression and are thus important for the prognosis of localized PCA.

Methods

This is a monocenter retrospective analysis of 86 consecutive patients with localized intermediate or high-risk PCA and PSMA-PET before treatment The quantitative PET parameters maximum standardized uptake value (SUVmax), tumor asphericity (ASP), PSMA tumor volume (PSMA-TV), and PSMA total lesion uptake (PSMA-TLU = PSMA-TV × SUVmean) were assessed for their prognostic significance in patients with radiotherapy or surgery. Cox regression analyses were performed for biochemical recurrence-free survival, overall survival (OS), local control, and loco-regional control (LRC).

Results

67% of patients had high-risk disease, 51 patients were treated with radiotherapy, and 35 with surgery. Analysis of metric PET parameters in the whole cohort revealed a significant association of PSMA-TV (p = 0.003), PSMA-TLU (p = 0.004), and ASP (p < 0.001) with OS. Upon binarization of PET parameters, several other parameters showed a significant association with clinical outcome. When analyzing high-risk patients according to the primary treatment approach, a previously published cut-off for SUVmax (8.6) showed a significant association with LRC in surgically treated (p = 0.048), but not in primary irradiated (p = 0.34) patients. In addition, PSMA-TLU (p = 0.016) seemed to be a very promising biomarker to stratify surgical patients.

Conclusion

Our data confirm one previous publication on the prognostic impact of SUVmax in surgically treated patients with high-risk PCA. Our exploratory analysis indicates that PSMA-TLU might be even better suited. The missing association with primary irradiated patients needs prospective validation with a larger sample size to conclude a predictive potential.

The spin degree of freedom in magnetically ordered materials is an important aspect for a variety of research directions. Antiferromagnets represent a broad class of systems with compensated or almost compensated net magnetization. On one side, it is a factor in the complications of their experimental investigation. However, on the other side, they offer unique features on ultrafast dynamics, strong robustness regarding external magnetic fields and delicate symmetry-driven phenomena in spin torques and multiferroicity. Specific research attention is paid to the properties of antiferromagnetic solitons as potential information carriers and the surface properties at which the readout of the magnetic state is performed. Here, we focus on the seminal magnetoelectric antiferromagnet Cr2O3 (chromia) with the easy-axis magnetic anisotropy.

In bulk single crystal chromia, the multidomain state is not favorable due to thermodynamic reasons, thus the stabilization of domain walls is possible on the defects. In particular, the litographically partterned surface topography of the sample can serve as the pinning landscape for the domain wall. The spatial inhomogeneity of this landscape allows to uncover the mechanical properties of the magnetic textures such as elastic deformation of the domain wall plane governed by the exchange boundary conditions [1]. Extension of this model onto chiral antiferromagnets with an inhomogeneous Dzyaloshinskii-Moriya interaction (DMI) shows that the domain walls and skyrmions possess a substantial modification of their shape approaching surface and side faces of the sample. These modifications limit the minimal size of racetracks to keep the bulk-like properties of magnetic solitons [2].

The surface of an antiferromagnet itself can substantially change its magnetic state. Chromia possesses two nominally compensated high-symmetry planes with an experimental evidence of a finite magnetization. The latter can be understood in terms of the surface magnetic symmetry group which supports a homogeneous DMI and can even change the bulk collinear antiferromagnetic ordering to a canted ferrimagnetic one [3].

In contrast to bulk, the chromia thin films are commonly in the multidomain state, which is determined by their granular structure. The domain wall pinning at the defects depends on the defect properties. Therefore, the visual analysis of the domain picture obtained, e.g., via Nitrogen vacancy magnetometry can be used as a source of quantification of the inter-grain coupling in the thin film [4] and, even quantification of such exotic phenomena like thermally driven flexomagnetism [5].

The spin degree of freedom in magnetically ordered materials is an important aspect for a variety of research directions. Antiferromagnets represent a broad class of systems with compensated or almost compensated net magnetization. On one side, it is a factor in the complications of their experimental investigation. However, on the other side, they offer unique features on ultrafast dynamics, strong robustness regarding external magnetic fields and delicate symmetry-driven phenomena in spin torques and multiferroicity. Specific research attention is paid to the properties of antiferromagnetic solitons as potential information carriers and the surface properties at which the readout of the magnetic state is performed. Here, we focus on the seminal magnetoelectric antiferromagnet Cr2O3 (chromia) with the easy-axis magnetic anisotropy.

In bulk single crystal chromia, the multidomain state is not favorable due to thermodynamic reasons, thus the stabilization of domain walls is possible on the defects. In particular, the litographically partterned surface topography of the sample can serve as the pinning landscape for the domain wall. The spatial inhomogeneity of this landscape allows to uncover the mechanical properties of the magnetic textures such as elastic deformation of the domain wall plane governed by the exchange boundary conditions [1]. In contrast, the chromia thin films are commonly in the multidomain state, which is determined by their granular structure. The domain wall pinning at the defects depends on the defect properties. Therefore, the visual analysis of the domain picture obtained, e.g., via Nitrogen vacancy magnetometry can be used as a source of quantification of the inter-grain coupling in the thin film [2]. Furthermore, in the case of the high-quality chromia samples epitaxially grown at sapphire substrate, the presence of domain walls allows to reveal a new temperature-driven source of the flexomagnetism in thin antiferromagnetic films [3].

Even in absence of specific processing like litography or design of exchange bias multilayers, the surface of an antiferromagnet can alter its magnetic state by its specific magnetic symmetry. Chromia possesses two nominally compensated high-symmetry planes with an experimental evidence of finite magnetization. It can be understood by the surface magnetic point symmetry group, which renders the m and a planes of chromia to be canted ferrimagnet and antiferromagnet, respectively [4].

We report terahertz (THz)-pump/mid-infrared probe near-field studies on Si-doped GaAs–InGaAs core–shell nanowires utilizing THz radiation from the free-electron laser FELBE. Upon THz excitation of free carriers, we observe a red shift of the plasma resonance in both amplitude and phase spectra, which we attribute to the heating of electrons in the conduction band. The simulation of heated electron distributions anticipates a significant electron population in both the L- and X-valleys. The two-temperature model is utilized for quantitative analysis of the dynamics of the electron gas temperature under THz pumping at various power levels.

Background: MR-integrated proton therapy is under development. It consists
of the unique challenge of integrating a proton pencil beam scanning (PBS)
beam line nozzle with an magnetic resonance imaging (MRI) scanner.The magnetic
interaction between these two components is deemed high risk as the
MR images can be degraded if there is cross-talk during beam delivery and
image acquisition.
Purpose: To create and benchmark a self -consistent proton PBS nozzle model
for empowering the next stages of MR-integrated proton therapy development,
namely exploring and de-risking complete integrated prototype system designs
including magnetic shielding of the PBS nozzle.
Materials and Methods: Magnetic field (COMSOL Multiphysics) and radiation
transport (Geant4) models of a proton PBS nozzle located at OncoRay (Dresden,
Germany) were developed according to the manufacturers specifications.
Geant4 simulations of the PBS process were performed by using magnetic field
data generated by the COMSOL Multiphysics simulations. In total 315 spots
were simulated which consisted of a 40 × 30cm2 scan pattern with 5 cm spot
spacings and for proton energies of 70, 100, 150, 200, and 220 MeV. Analysis
of the simulated deflection at the beam isocenter plane was performed to
determine the self -consistency of the model. The magnetic fringe field from a
sub selection of 24 of the 315 spot simulations were directly compared with high
precision magnetometer measurements.These focused on the maximum scanning
setting of ± 20 cm beam deflection as generated from the second scanning
magnet in the PBS for a proton beam energy of 220 MeV. Locations along the
beam line central axis (CAX) were measured at beam isocenter and downstream
of 22, 47, 72, 97, and 122 cm. Horizontal off -axis positions were measured
at 22 cm downstream of isocenter (± 50,± 100,and ± 150 cm from CAX).
Results: The proton PBS simulations had good spatial agreement to the theoretical
values in all 315 spots examined at the beam line isocenter plane
(0–2.9 mm differences or within 1.5 % of the local spot deflection amount).
Careful analysis of the experimental measurements were able to isolate
the changes in magnetic fields due solely to the scanning magnet contribution,
and showed 1.9 ± 1.2 uT–9.4 ± 1.2 uT changes over the range
of measurement locations. Direct comparison with the equivalent simulations
matched within the measurement apparatus and setup uncertainty in all but
one measurement point.
Conclusions: For the first time a robust, accurate and self -consistent model
of a proton PBS nozzle assembly has been created and successfully
benchmarked for the purposes of advancing MR-integrated proton therapy
research. The model will enable confidence in further simulation based work
on fully integrated designs including MRI scanners and PBS nozzle magnetic
shielding in order to de-risk and realize the full potential of MR-integrated proton
therapy.

The analysis of particle–bubble collisions in turbulent flow is a fundamental problem of high technological relevance, e.g., for the separation of valuable mineral particles by froth flotation. This relevance contrasts with an apparent lack of experimental data and understanding of this collision process. To this end, a periodic bubble chain was used to study the collision of millimeter-sized bubbles with polystyrene particles. The collision process between these entities was measured using 4D particle tracking velocimetry (PTV). By analyzing the collision data as a function of the polar angle along the bubble surface, we show that the collision took place not only at the leading edge but also at the trailing edge of the bubble. To understand the underlying mechanisms of the trailing edge collision, the flow field around a rising bubble chain was measured with Tomographic Particle Image Velocimetry (TPIV). The vortex formed in the bubble wake led to a velocity in the direction of the bubble surface that enabled trailing edge collisions. This effect is amplified by an increase in the turbulent kinetic energy and dissipation rate in the bubble wake. Overall, the investigation reveals different collision mechanisms and advances our understanding of the role of the wake in the bubble–particle collision.

Improvement in resolving hydrodynamic variables in multiphase flows is key to optimizing flotation performance. However, due to equipment complexity and opacity of three-phase systems, in situ measurements become challenging. Therefore, by using a novel multi-sensor approach, the aim of this study is to spatially resolve key hydrodynamic and gas dispersion parameters in a mechanical flotation cell such as superficial gas velocity (Jg), gas holdup (εg), bubble size distribution (BSD), and bubble surface area flux (Sb). A high-resolution inline endoscope (SOPAT), Jg and εg sensors were fixed at multiple axial positions in a 6L nextSTEP™ flotation cell. This multi-sensor concept has been applied to a simplified benchmark flotation scenario, as part of a binary (pyrite-quartz) flotation test campaign (30 % solid load). Varying operating conditions include tip speed (4.7 – 5.5 m/s), air flow (0.4 – 0.5 cm/s), frother (MIBC: 30 – 60 g/ton), and collector concentrations (PAX: 30 – 60 g/ton). Sb is a good indicator of gas dispersion efficiency in flotation, and local measurements indicated that there are significant differences in the local superficial gas velocities which can be measured with our adapted sensor. Real-time bubble size measurements reflected the high shear rates near the rotor–stator region. Overall, the gas flow rate and frother concentration were shown to have the most significant effect on the gas dispersion in the benchmark flotation tests.

Theory questions the persistence of non-reciprocal interactions in which one plant has a positive net effect on a neighbor that, in return, has a negative net impact on its benefactor—a phenomenon known as antagonistic facilitation. We develop a spatially explicit consumer-resource model for below-ground plant competition between ecosystem engineers, plants able to mine resources and make them available for any other plant in the community, and exploiters. We use the model to determine in what environmental conditions antagonistic facilitation via soil resource engineering emerges as an optimal strategy. Antagonistic facilitation emerges in stressful environments where ecosystem engineers’ self-benefits from mining resources outweigh the competition with opportunistic neighbors. Among all potential causes of stress considered in the model, the key environmental parameter driving changes in the interaction between plants is the proportion of the resource that becomes readily available for plant consumption in the absence of any mining activity. Our results align with theories of primary succession and the stress gradient hypothesis. However, we find that the total root biomass and its spatial allocation through the root system, often used to measure the sign of the interaction between plants, do not predict facilitation reliably.

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

Introduction The standard treatment for patients with focal drug-resistant epilepsy (DRE) who are not eligible for
open brain surgery is the continuation of anti-seizure medication (ASM) and neuromodulation. This treatment does not cure epilepsy but only decreases severity. The PRECISION trial offers a non-invasive, possibly curative intervention for these patients, which consist of a single stereotactic radiotherapy (SRT) treatment. Previous studies have shown promising results of SRT in this patient population. Nevertheless, this intervention is not yet available and reimbursed in the Netherlands. We hypothesize that: SRT is a superior treatment option compared to palliative standard of care, for patients with focal DRE, not eligible for open surgery, resulting in a higher reduction of seizure frequency (with 50% of the patients reaching a 75% seizure frequency reduction at 2 years follow-up).
Methods In this waitlist-controlled phase 3 clinical trial, participants are randomly assigned in a 1:1 ratio to either
receive SRT as the intervention, while the standard treatments consist of ASM continuation and neuromodulation.
After 2-year follow-up, patients randomized for the standard treatment (waitlist-control group) are offered SRT.
Patients aged ≥ 18 years with focal DRE and a pretreatment defined epileptogenic zone (EZ) not eligible for open
surgery will be included. The intervention is a LINAC-based single fraction (24 Gy) SRT treatment. The target volume is defined as the epileptogenic zone (EZ) on all (non) invasive examinations. The seizure frequency will be monitored on a daily basis using an electronic diary and an automatic seizure detection system during the night. Potential side effects are evaluated using advanced MRI, cognitive evaluation, Common Toxicity Criteria, and patient-reported outcome questionnaires. In addition, the cost-effectiveness of the SRT treatment will be evaluated.
Discussion This is the first randomized trial comparing SRT with standard of care in patients with DRE, non-eligible for open surgery. The primary objective is to determine whether SRT significantly reduces the seizure frequency 2 years after treatment. The results of this trial can influence the current clinical practice and medical cost reimbursement in the Netherlands for patients with focal DRE who are not eligible for open surgery, providing a non-invasive curative treatment option.

Purpose: Radio(chemo)therapy (RCT) as part of the standard treatment of glioma patients, inevitably leads to
radiation exposure of the tumor-surrounding normal-appearing (NA) tissues. The effect of radiotherapy on the
brain microstructure can be assessed by magnetic resonance imaging (MRI) using diffusion tensor imaging (DTI).
The aim of this study was to analyze regional DTI changes of white matter (WM) structures and to determine
their dose- and time-dependency.
Methods: As part of a longitudinal prospective clinical study (NCT02824731), MRI data of 23 glioma patients
treated with proton or photon beam therapy were acquired at three-monthly intervals until 36 months following
irradiation. Mean, radial and axial diffusivity (MD, RD, AD) as well as fractional anisotropy (FA) were investi-
gated in the NA tissue of 15 WM structures and their dependence on radiation dose, follow-up time and distance
to the clinical target volume (CTV) was analyzed in a multivariate linear regression model. Due to the small and
non-comparable patient numbers for proton and photon beam irradiation, a separate assessment of the findings
per treatment modality was not performed.
Results: Four WM structures (i.e., internal capsule, corona radiata, posterior thalamic radiation, and superior
longitudinal fasciculus) showed statistically significantly decreased RD and MD after RT, whereas AD decrease
and FA increase occurred less frequently. The posterior thalamic radiation showed the most pronounced changes
after RCT [i.e., ΔRD = −8.51 % (p = 0.012), ΔMD = −6.14 % (p = 0.012)]. The DTI changes depended
significantly on mean dose and time.
Conclusion: Significant changes in DTI for WM substructures were found even at low radiation doses. These
findings may prompt new radiation dose constraints sparing the vulnerable structures from damage and sub-
sequent side-effects.

Objective In the era of image-guided adaptive radiotherapy, definition of the clinical target volume (CTV) is a challenge in various solid tumors, including esophageal cancer (EC). Many tumor microenvironmental factors, e.g., tumor cell proliferation or cancer stem cells, are hypothesized to be involved in microscopic tumor extension (MTE). Therefore, this study assessed the expression of FAK, ILK, CD44, HIF-1α, and Ki67 in EC patients after neoadjuvant radiochemotherapy followed by tumor resection (NRCHT+R) and correlated these markers with the MTE.
Methods Formalin-fixed paraffin-embedded tumor resection specimens of ten EC patients were analyzed using multiplex immunofluorescence staining. Since gold fiducial markers had been endoscopically implanted at the proximal and distal tumor borders prior to NRCHT+R, correlation of the markers with the MTE was feasible.
Results In tumor resection specimens of EC patients, the overall percentages of FAK+, CD44+, HIF-1α+, and Ki67+ cells were higher in tumor nests than in the tumor stroma, with the outcome for Ki67+ cells reaching statistical significance (p< 0.001). Conversely, expression of ILK+ cells was higher in tumor stroma, albeit not statistically significantly. In three patients, MTE beyond the fiducial markers was found, reaching up to 31mm.
Conclusion Our findings indicate that the overall expression of FAK, HIF-1α, Ki67, and CD44 was higher in tumor nests, whereas that of ILK was higher in tumor stroma. Differences in the TME between patients with residual tumor cells in the original CTV compared to those without were not found. Thus, there is insufficient evidence that the TME influences the required CTV margin on an individual patient basis.

The evidence for the value of particle therapy (PT) is still sparse. While randomized trials remain a cornerstone for robust comparisons with photon-based radiotherapy, data registries collecting real-world data can play a crucial role in building evidence for new developments. This Perspective describes how the European Particle Therapy Network (EPTN) is actively working on establishing a prospective data registry encompassing all patients undergoing PT in European centers. Several obstacles and hurdles are discussed, for instance harmonization of nomenclature and structure of technical and dosimetric data and data protection issues. A preferred approach is the adoption of a federated data registry model with transparent and agile governance to meet European requirements for data protection, transfer, and processing. Funding of the registry, especially for operation after the initial setup process, remains a major challenge.

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

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

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

Ternary nitrides are rapidly emerging as promising compounds for optoelectronic and energy conversion applications, yet comparatively little of this vast composition space has been explored. Furthermore, the crystal structures of these compounds can exhibit a significant amount of disorder, the consequences of which are not yet well understood. Here, the deposition of bixbyite-type ZrTaN3 thin films is demonstrated by reactive magnetron co-sputtering and observed semiconducting character, with a strong optical absorption onset at 1.8 eV and significant photoactivity, with prospective application as functional photoanodes. It is found that Wyckoff-site occupancy of cations is a critical factor in determining these beneficial optoelectronic properties. First-principles calculations show that cation disorder leads to minor deviations in the total energy but modulates the bandgap by 0.5 eV, changing orbital hybridization of valence and conduction band states. In addition to demonstrating that ZrTaN3 is a promising visible light-absorbing semiconductor and active photoanode material, the findings provide important insights regarding the role of cation ordering on the electronic structure of ternary semiconductors. In particular, it is shown that not only cation order, but also the cationic Wyckoff site occupancy has a substantial impact on key optoelectronic properties, which can guide future design and synthesis of advanced semiconductors.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Here we report the synthesis of the 6-(6-methyl-1,2,4,5-tetrazine-3-yl)-2,2‘-bipyridine (MTB) ligand, that has been developed for lanthanide/actinide separation. A multi-method study of the
complexation of MTB with trivalent actinide and lanthanide ions is presented. Single crystal X-ray diffraction measurements reveal the formation of [Ce(MTB)2(NO3)3], [Pr(MTB)NO3)3H2O], and [Ln(MTB)(NO3)3MeCN] (Ln = Nd, Sm, Eu, Gd). In addition, the complexation of Cm(III) with MTB in solution was studied by time-resolved laser fluorescence spectroscopy. The results show the formation of [Cm(MTB)1-3] 3+ complexes, which occurs in two different isomers. Quantum chemical calculations reveal an energy difference between these isomers of 12 kJ mol-1, clarifying the initial observations made by TRLFS. Furthermore, QTAIM analysis of the Cm(III) and Ln(III) complexes was performed, indicating a stronger covalent contribution in the Cm-N interaction compared to the respective Ln-N interaction. These findings align well with extraction data showing a preferred extraction of Am and Cm over lanthanides (e.g., max. SFAm/Eu = 8.3) at nitric acid concentrations < 0.1 mol L-1 HNO3.
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I will talk about two recent efforts to apply advanced machine learning methods to the electronic structure problem at the density functional theory (DFT) level. First, I will present a machine learning approach based on physics-informed neural networks and neural operators for inverting the Kohn-Sham equations for the exchange-correlation (XC) potential; neural networks provide a new way to perform DFT inversions at scale by learning the mapping from density to potential [1]. Second, I will present a very recent development in which we use neural operators to predict the electron dynamics of systems driven by a laser field. This approach complements conventional numerical solvers and has the potential to enable real-time, on-the-fly modeling of laser-irradiated molecules and materials with varying experimental parameters [2]. Both methods are illustrated on a conceptual level using one-dimensional models of diatomic molecules, but the approach can be readily applied to realistic systems in three dimensions.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Perpendicular magnetic anisotropy thin film systems are well known for their periodic magnetic stripe domain structures. In this study, we focus on investigating the behavior of [Co(3.0 nm)/Pt(0.6 nm)]X multilayers within the transitional regime from preferred in-plane to out-of-plane magnetization orientation. Particularly, we examine the sample with X = 11 repetitions, which exhibits a remanent state characterized by a significant presence of both out-of-plane (OOP) and in-plane (IP) magnetization components, here referred to as the “tilted” stripe domain state. Vector vibrating sample magnetometry and magnetic force microscopy are used to investigate this specific sample and its unusual out-of-plane reversal behavior. Through experimental data analysis and micromagnetic simulations of the tilted magnetization system, we identify a single point of irreversibility during an out-of-plane external magnetic field sweep. This behavior is qualitatively similar to the reversal of a Stoner-Wohlfarth particle or of an IP magnetized disk with remanent vortex structure, since both show distinct points of irreversibility as well. Such a collective response to an external field is typically not observed in conventional OOP or IP systems, where the reversal process often involves independent nucleation, propagation, and annihilation of individual domains. Finally, we show that our findings are not at all restricted to Co/Pt multilayers, but are a quite general feature of transitional in-plane to out-of-plane magnetization systems.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Plasmon polaritons, or plasmons, are coupled oscillations of electrons and electromagnetic fields that can confine the latter into deeply subwavelength scales, enabling novel polaritonic devices. While plasmons have been extensively studied in normal metals or semimetals, they remain largely unexplored in correlated materials. In this paper, we report infrared (IR) nano-imaging of thin flakes of CsV₃Sb₅, a prototypical layered Kagome metal. We observe propagating plasmon waves in real-space with wavelengths tunable by the flake thickness. From their frequency-momentum dispersion, we infer the out-of-plane dielectric function εc that is generally difficult to obtain in conventional far-field optics, and elucidate signatures of electronic correlations when compared to density functional theory (DFT). We propose correlation effects might have switched the real part of εc from negative to positive values over a wide range of middle-IR frequencies, transforming the surface plasmons into hyperbolic bulk plasmons, and have dramatically suppressed their dissipation.

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

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

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

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

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

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

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

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

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

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

Neutron irradiation causes embrittlement of reactor pressure vessel (RPV) steels. Post-irradiation annealing is capable of partly or fully restoring the unembrittled condition. While annealing at high temperatures (e.g. 475 °C) was successfully applied to extend the lifetime of operating VVER-440 reactors, the benefit of annealing at lower temperatures (e.g. 343 °C – the maximum to which the primary cooling water can be heated) is a matter of debate. In this study, neutron-irradiated VVER-440 RPV base metal and weld were exposed to isothermal annealing at 343 °C up to 2000 hours. Given the limited amount of material, the degree of recovery was estimated in terms of Vickers hardness, the ductile-brittle transition temperature derived from small punch tests, and the master curve reference temperature derived from fracture mechanics tests of subsized samples. For the base metal, small-angle neutron scattering was applied to underpin the findings at the nm-scale. We have found significant partial recovery in both materials after annealing for 300 hours or longer. The variations of the degree of recovery are critically discussed and put into the context of wet annealing.