Publications Repository - Helmholtz-Zentrum Dresden-Rossendorf

We give an overview of the Where2Test project, focusing on the general approach and particular applications for machine learning.

Conical structures towards nanometer length scales are attractive for numerous applications including super-hydrophobic and electrocatalytic materials. Among the various methods of synthesizing arrays of micro- and nano-cones, electrochemical deposition techniques have been widely applied. We aim at enhancing the conical growth during deposition by applying an external magnetic field. Most of the magnetic field effects can be attributed to the Lorentz force and the magnetic gradient force [1]. If the magnetic field imposed on the electrochemical cell is well designed, the magnetic forces can generate an electrolyte flow which brings fresh electrolyte towards the tip of a cone, so that the local mass transfer would be enhanced and the conical growth would be supported.

We first performed analytical and numerical studies of electrodeposition on diamagnetic (Cu) and ferromagnetic (Fe) cones of mm size under the influence of a homogeneous vertical magnetic field. The beneficial structuring effects of the Lorentz force has already been shown for the Cu cone case [2]. The magnetization of the Fe cones causes additionally a strong magnetic gradient force near the cone tips and gives rise to a flow that can bring enriched electrolyte to the conical cathode. As the cathodes are placed at the bottom of the electrochemical cell, solutal buoyancy tends to bring upwards lighter electrolyte from the conical cathode and thus counteract the downward flow caused by the magnetic forces. Our results show that for the Cu cones, the Lorentz force becomes smaller than the buoyancy force after the first few seconds of the deposition, while the magnetic gradient force in case of the Fe cones keeps surpassing the buoyancy during the deposition.

Next, scaling studies on cones of sizes ranging from millimeter to micrometer allow to deliver insights into the magnetic field assisted electrodeposition towards micro- and nano-cones. As the cone size shrinks, the geometrical inhomogeneity decreases, and the current density gets more uniformly distributed on the cone, which is making the conical growth more difficult. Furthermore, the beneficial flow forced by the magnetic field near smaller cones suffers from higher wall friction. But this can be partially compensated by the larger magnetic gradients existing at smaller Fe cones, and the flow caused by the magnetic gradient force was found to decrease more slowly than the flow caused by other forces with the decreasing cone size. Such scaling behavior of the flow velocity corresponds well with a theoretical analysis of the Navier-Stokes equation. For a Fe cone with a radius of 10 micron under study here, the magnetic gradient force generates a beneficial downward flow with a velocity of about 5 micron per second. But in general the structuring effects during the deposition is much weaker than at larger length scales.

This work shows the potential of using the magnetic gradient force for growing ferromagnetic conical structures during electrodeposition. Optimization possibilities for conical growth at smaller scales by e.g. enhancing the cell current, applying stronger magnetic fields and pulsed electrodeposition will also be discussed.

Encounter rates link movement strategies to intra- and inter-specific interactions, and therefore translate individual movement behavior into higher-level ecological processes. Indeed, a large body of interacting population theory rests on the law of mass action, which can be derived from assumptions of Brownian motion in an enclosed container with exclusively local perception. These assumptions imply completely uniform space use, individual home ranges equivalent to the population range, and encounter dependent on movement paths actually crossing. Mounting empirical evidence, however, suggests that animals use space non-uniformly, occupy home ranges substantially smaller than the population range, and are of- ten capable of nonlocal perception. Here, we explore how these empirically supported behaviors change pairwise encounter rates. Specifically, we derive novel analytical expressions for encounter rates under Ornstein-Uhlenbeck motion, which features non-uniform space use and allows individual home ranges to differ from the population range. We compare OU-based encounter predictions to those of Reflected Brownian Motion, from which the law of mass action can be derived. For both models, we further explore how the interplay between the scale of perception and home-range size affects encounter rates. We find that neglecting realistic movement and perceptual behaviors can lead to systematic, non-negligible biases in encounter-rate predictions.

Insufficient testing capacity continues to be a critical bottleneck in the worldwide fight against COVID-19. Optimizing the deployment of limited testing resources has therefore emerged as a keystone problem in pandemic response planning. Here, we use a modified SEIR model to optimize testing strategies under a constraint of limited testing capacity. We define pre-symptomatic, asymptomatic, and symptomatic infected classes, and assume that positively tested individuals are immediately moved into quarantine. We further define two types of testing. Clinical testing focuses only on the symptomatic class. Non-clinical testing detects pre- and asymptomatic individuals from the general population, and an information parameter governs the degree to which such testing can be focused on high infection risk individuals. We then solve for the optimal mix of clinical and non-clinical testing as a function of both testing capacity and the information parameter. We find that purely clinical testing is optimal at very low testing capacities, supporting early guidance to ration tests for the sickest patients.
Additionally, we find that a mix of clinical and non-clinical testing becomes optimal as testing capacity increases. At high but empirically observed testing capacities, a mix of clinical testing and unfocused (information=0) non-clinical testing becomes optimal. We further highlight the dvantages of early implementation of testing programs, and of combining optimized testing with contact reduction interventions such as lockdowns, social distancing, and masking.

The lowest swirling wave mode arising in upright circular cylinders as a response to orbital excitation has been widely studied in the last decade, largely owing to its high practical relevance for orbitally shaken bioreactors. Our recent theoretical study (Horstmann et al. 2020) revealed a damping-induced symmetry breaking mechanism that can cause spiral wave structures manifested in the so far widely disregarded higher rotating wave modes. Building on this work, we develop a spiralisation criterion and classify different spiral regimes as a function of the most relevant dimensionless groups. The analysis suggests that high Bond numbers and shallow liquid layers favour the formation of coherent spiral waves. This result paved the way to find the predicted wave structures in our interfacial sloshing experiment. We present two sets of experiments, with different characteristic damping rates, verifying the formation of both coherent and overdamped spiral waves in conformity with the theoretical predictions.

The microstructure of molybdenum mirrors was refined by high pressure torsion. Already after one rotation microhardness significantly increased from 231 for the as-received mirror to 542 HV0.2. The increase of number of rotations to five caused further slight increase of microhardness to 558 HV0.2. The higher microhardness values correspond well with the grain refinement as the grain size decreased with the increase of the deformation degree down to 480 and 110 nm, respectively for 1 and 5 rotations. Subsequently, refined mirrors and a reference micrograined one were irradiated by He ions to the dose of 8x1016/cm 2 to simulate the effect of plasma exposure on diagnostic mirrors to be applied in D-T fusion devices. Irradiations were followed by reflectivity measurements in the 300-2400 nm range with a dual beam spectrometer. It was noticed that irradiation caused a slight decrease in total reflectivity of the micrograined mirror, whereas that of high-pressure torsion-processed samples decreases by an additional 2.5%. Nanohardness measurements, detailed microscopy observations using focused ion beam and scanning transmission electron microscope as well as positron annihilation spectroscopy investigations were performed to elucidate that cause of those changes. Based on the results, it is postulated that the nanocracks created at grain boundaries during irradiation in the optically active layer are responsible for lower reflectivity of high-pressure torsion-processed mirrors.

There are two paradigms to study nanoscale engines in stochastic and quantum thermodynamics.
Autonomous models, which do not rely on any external time-dependence, and models that make use of time-dependent control fields, often combined with dividing the control protocol into idealized strokes of a thermodynamic cycle. While the latter paradigm offers theoretical simplifications, its utility in practice has been questioned due to the involved approximations. Here, we bridge the two paradigms by constructing an autonomous model, which implements a thermodynamic cycle in a certain parameter regime. This effect is made possible by self-oscillations, realized in our model by the well studied electron shuttling mechanism. Based on experimentally realistic values, we find that a thermodynamic cycle analysis for a single-electron working fluid is unrealistic, but already a few-electron working fluid could suffice to justify it. We also briefly discuss additional open challenges to autonomously implement the more studied Carnot and Otto cycles.

Invar-behavior occurring in many magnetic materials has long been of interest to materials science. Here, we show not only invar behavior of a continuous film of FePt but also even negative thermal expansion of FePt nanograins upon equilibrium heating. Yet, both samples exhibit pronounced transient expansion upon laser heating in femtosecond x-ray diffraction experiments. We show that the granular microstructure is essential to support the contractive out-of-plane stresses originating from in-plane expansion via the Poisson effect that add to the uniaxial contractive stress driven by spin disorder. We prove the spin contribution by saturating the magnetic excitations with a first laser pulse and then detecting the purely expansive response to a second pulse. The contractive spin stress is reestablished on the same 100-ps time scale that we observe for the recovery of the ferromagnetic order. Finite-element modeling of the mechanical response of FePt nanosystems confirms the morphology dependence of the dynamics.

We use vector Hamiltonian formalism (VHF) to study theoretically three-magnon parametric interaction (or three-wave splitting) in a magnetic disk existing in a magnetic vortex ground state. The three-wave splitting in a disk is found to obey two selection rules: (i) conservation of the total azimuthal number of the resultant spin-wave modes, and (ii) inequality for the radial numbers of interacting modes, if the mode directly excited by the driving field is radially symmetric (i.e. if the azimuthal number of the directly excited mode is m=0). The selection rule (ii), however, is relaxed in the "small" magnetic disks, due to the influence of the vortex core. We also found, that the efficiency of the three-wave interaction of the directly excited mode strongly depends on the azimuthal and radial mode numbers of the resultant modes, that becomes determinative in the case when several splitting channels (several pairs of resultant modes) simultaneously approximately satisfy the resonance condition for the splitting. The good agreement of the VHF analytic calculations with the experiment and micromagnetic simulations proves the capability of the VHF formalism to predict the actual splitting channels and the magnitudes of the driving field thresholds for the three-wave splitting.

Das Ziel von „Circular by Design“ besteht in der Entwicklung eines kreislauffähigen Produktdesigns für Kühl-/Gefriergeräte, das neben Energieeffizienz auch auf Ressourceneffizienz hin optimiert ist. Mit dem durch das BMBF geförderten Projekt soll die tatsächlich machbare Umsetzung von zirkulärem Design, zum einen mit dem Fokus auf Repair/Reuse und zum anderen auf möglichst geschlossene Recyclingpfade, demonstriert werden.
Die Zusammenführung der Ressourceneffizienzanalyse mit dem technologieorientierten und simulationsbasierten „Design for Recycling“-Modell soll künftig die Vorhersage eines für eine vollständige Kreislaufführung geeigneten Produktdesigns erlauben. Es werden die derzeitigen Verluste beim Erfassen und Recycling eines Kühl-/Gefriergerätes auf verschiedenen Ebenen quantifiziert, Ressourceneffizienzpotentiale dargestellt und auf dieser Basis ein Produktdesign entwickelt, dessen Materialzusammensetzung ein möglichst vollständiges Recycling sowie Reuse erlaubt.

Machine learning is changing how we design and interpret experiments in materials science. In this work we show how unsupervised learning, combined with ab initio random structure searching, improves our understanding of structural metastability in multicomponent alloys. We focus on the case of Al-O-N alloys where the formation of aluminum vacancies in wurtzite AlN upon the incorporation of substitutional oxygen can be seen as a general mechanism of solids where crystal symmetry is reduced to stabilize defects. The ideal AlN wurtzite crystal structure occupation cannot be matched due to the presence of an aliovalent hetero-element into the structure. The traditional interpretation of the c-lattice shrinkage in sputter-deposited Al-O-N films from X-ray diffraction (XRD) experiments suggests the existence of a solubility limit at 8 at.% oxygen content. Here we show that such naive interpretation is misleading. We support XRD data with accurate ab initio modeling and dimensionality reduction on advanced structural descriptors to map structure-property relationships. No signs of a possible solubility limit are found. Instead, the presence of a wide range of non-equilibrium oxygen-rich defective structures emerging at increasing oxygen contents suggests that the formation of grain boundaries is the most plausible mechanism responsible for the lattice shrinkage measured in Al-O-N sputtered films. We further confirm our hypothesis using positron annihilation lifetime spectroscopy.

Helium ions offer an alternative imaging probe to electrons, with a smaller de Broglie wavelength at the same energy [1] [2][3]. Furthermore, the ability for neutralisation means that images can be formed by collecting only post-sample neutrals or both neutrals and transmitted ions. A comparison between the two can map where ions are more easily neutralised and offers an alternative contrast mechanism not possible with electrons. Transmission helium ion imaging is quite an understudied field and more experiments are required to fully assess the possibilities and benefits with this new microscopy. With this aim in mind, a prototype transmission helium ion microscope (THIM) has been constructed at the Luxembourg -Institute of Science and Technology (LIST) (Figure 1). The ion source is a duoplasmatron operated at 10-20 keV with a minimum beam spot size of 100 µm and a beam current of 0.1-2 nA . A microchannel plate (MCP) located behind the sample converts the transmitted ion signal to an electron shower which then hits a phosphor screen for direct transmission imaging with a stationary beam [4]. The detector is placed over 50 cm away from the sample. Imaging of crystalline powders showed unexpectedly large charging and deformation of the beam, producing collections of spots (Figure 2). Scanning transmission ion microscopy (STIM) can also be conducted if the phosphor screen is replaced with a metal anode plate. As the beam is scanned over the sample surface, the current from the plate is measured and gives the intensity at each pixel in the STHIM image. A secondary electron detector in front of the sample is used to record secondary electron images at the same time as STIM imaging (Figure 1). Post sample deflectors blank all ions in transmission, such that only neutral atoms are imaged and the fraction of neutralised ions can be estimated. Electrostatic blanking and using the anode plate current as a stop signal allows one to determine the energy of transmitted particles by measuring their time of flight (TOF). In addition, a position sensitive delay line detector has recently been installed, to add position sensitivity to the TOF measurements. This allows both the trajectory and energy of and ion to be measured at the same time, providing a more complete record of the transmission through the sample.

On a separate prototype machine, the ‘NPScope’ instrument, which combines a gas field ion source with a transmission delay line detector, STIM can be performed with nanometre spot size. This enables parallel bright and dark field imaging using the same detector (Figure 3).

We present a plasma switch based on gold implanted germanium (Ge:Au) as a potential candidate for efficient cavity dumping of a free-electron laser (FEL). Ge:Au has a sub-nanosecond carrier lifetime – much shorter than the FEL pulsing period of 77 ns – and demonstrates a high photoinduced reflectivity in a broad range of infrared wavelengths from 6 to 90 µm. The Ge:Au plasma switch exhibits negligible absorption of the FEL radiation in the ʻoff ʼ state and requires only moderate thermoelectric cooling for incident FEL power of several Watts. A reflectivity level of more than 50 % in the ‘on’ state is achieved over the entire spectral range of this study. The corresponding optical pump fluence exhibits a linear relationship with the FEL frequency. This scaling is corroborated by our simulations highlighting the role of a finite sub-µm thickness of the photoinduced reflecting plasma layer. The demonstrated device is promising for the realization of the FEL cavity dumping for experiments that simultaneously require higher pulse energy and lower average power.

Exascale is the next big step in the field of high-performance computing. However, the hardware configurations of supercomputers around the world are becoming increasingly heterogeneous. Programmers have to take into account varying processor architectures (x86, ARM, RISC-V, ...) as well as different accelerator types (multicore CPUs, GPUs, FPGAs, ...) and the accompanying tools. Our goal is a portable stack of C++ libraries and tools. Together they shall form an ecosystem which abstracts away the differences between hardware configurations without sacrificing performance.

The occurrence of magnetohydrodynamic quasiperiodic flows with four fundamental frequencies in differentially rotating spherical geometry is understood in terms of a sequence of bifurcations breaking the azimuthal symmetry of the flow as the applied magnetic field strength is varied. These flows originate from unstable periodic and quasiperiodic states with broken equatorial symmetry, but having fourfold azimuthal symmetry. A posterior bifurcation gives rise to twofold symmetric quasiperiodic states, with three fundamental frequencies, and a further bifurcation to a four-frequency quasiperiodic state which has lost all the spatial symmetries. This bifurcation scenario may be favored when differential rotation is increased and periodic flows with m-fold azimuthal symmetry, m being a product of several prime numbers, emerge at sufficiently large magnetic field.

This paper provides an up-to-date review of the problems related to the generation, detection and mitigation of strong electromagnetic pulses created in the interaction of high-power, high-energy laser pulses with different types of solid targets. It includes new experimental data obtained independently at several international laboratories. The mechanisms of electromagnetic field generation are analyzed and considered as a function of the intensity and the spectral range of emissions they produce. The major emphasis is put on the GHz frequency domain, which is the most damaging for electronics and may have important applications. The physics of electromagnetic emissions in other spectral domains, in particular THz and MHz, is also discussed. The theoretical models and numerical simulations are compared with the results of experimental measurements, with special attention to the methodology of measurements and complementary diagnostics. Understanding the underlying physical processes is the basis for developing techniques to mitigate the electromagnetic threat and to harness electromagnetic emissions, which may have promising applications.

The Cassini and Juno probes have revealed large coherent cyclonic vortices in the polar regions of Saturn and Jupiter, a dramatic contrast from the east–west banded jet structure seen at lower latitudes. Debate has centred on whether the jets are shallow, or extend to greater depths in the planetary envelope. Recent experiments and observations have demonstrated the relevance of deep convection models to a successful explanation of jet structure, and cyclonic coherent vortices away from the polar regions have been simulated recently including an additional stratified shallow layer. Here we present new convective models able to produce long-lived polar vortices. Using simulation parameters relevant for giant planet atmospheres we find flow regimes of geostrophic turbulence (GT) in agreement with rotating convection theory. The formation of large-scale coherent structures occurs via 3D upscale energy transfers. Our simulations generate polar characteristics qualitatively similar to those seen by Juno and Cassini: They match the structure of cyclonic vortices seen on Jupiter; or can account for the existence of a strong polar vortex extending downwards to lower latitudes with a marked spiral morphology, and the hexagonal pattern seen on Saturn. Our findings indicate that these vortices can be generated deep in the planetary interior. A transition differentiating these two polar flows regimes is described, interpreted in terms of force balances and compared with shallow atmospheric models characterizing polar vortex dynamics in giant planets. In addition, heat transport properties are investigated, confirming recent scaling laws obtained with reduced models of GT.

Relativistic electrons generated by the interaction of petawatt-class short laser pulses with solid targets can be used to generate bright X-rays via bremsstrahlung. The efficiency of laser energy transfer into these electrons depends on multiple parameters including focused intensity and pre-plasma level. This paper describes the experimental results from the interaction of a high intensity petawatt-class glass laser with solid targets at a maximum intensity of 10^19 W/cm^2. In-situ measurements of specularly reflected light were used to provide an upper bound of laser absorption and to characterize focused laser intensity, the pre-plasma level and the generation mechanism of second harmonic light. The measured spectrum of electrons and bremsstrahlung radiation provide information about the efficiency of laser energy transfer.

Biohydrometallurgy is one of many different processes for metal recovery. As a highly interdisciplinary field, biohydrometallurgy combines microorganisms and their metabolites (-bio) in a mainly aquatic environment (-hydro) and the treatment of metal containing materials or solutions (-metallurgy) for metal production and treatment. It is applied to many different metal-rich materials from primary mineral sources, secondary mining products and numerous manufactured resources (Watling, 2015). Biohydrometallurgy is using biological tools for the processing of primary ores for many years – especially in case of bioleaching. Besides that, special biological tools can enhance the metal recovery from manufactured resources such as technical waste products, processing wastes, industrial waste waters and other secondary sources (Pollmann et al. 2018). In nature multiple processes exist that influence biogeochemical cycles of elements. These microorganism driven processes contribute to bioaccumulation, bio weathering, biomineralization and precipitation or microbial reduction. Using these bio-inspired processes promotes biological recycling strategies as well as several clean industrial processes, bio-based materials and bioremediation. Modern bio-based approaches that are currently being developed for the recycling of value elements found in technical products contributing to a “green” circular economy. Main processes in biohydrometallurgy are bioleaching, biosorption, bioflotation and bioreduction.

Direct numerical simulations of a liquid metal filling the gap between two concentric spheres are presented. The flow is governed by the interplay between the rotation of the inner sphere (measured by the Reynolds number Re) and a weak externally applied axial magnetic field (measured by the Hartmann number Ha). By varying the latter, a rich variety of flow features, both in terms of spatial symmetry and temporal dependence, is obtained. Flows with two or three independent frequencies describing their time evolution are found as a result of Hopf bifurcations. They are stable on a sufficiently large interval of Hartmann numbers where regions of multistability of two, three, and even four types of these different flows are detected. The temporal character of the solutions is analyzed by means of an accurate frequency analysis and Poincaré sections. An unstable branch of flows undergoing a period doubling cascade and frequency locking of three-frequency solutions is described as well.

Printed electronics are attractive due to their low-cost and large-area processing features, which have been successfully extended to magnetoresistive sensors and devices. Here, we introduce and characterize a new kind of magnetoresistive paste based on the anisotropic magnetoresistive (AMR) effect. The paste is a composite of 100-nm-thick permalloy/tantalum flakes embedded in an elastomer matrix, which promotes the formation of appropriately conductive percolation networks. Sensors printed with this paste showed stable magnetoresistive properties upon mechanical bending. The AMR value of this sensor is 0.34% in the field of 400 mT. Still, the response is stable and allows to resolve sub-mT field steps. When printed on ultra-thin 2.5-μm-thick Mylar foil, the sensor can be completely folded without losing magnetoresistive performance and mechanically withstand 20 μm bending radius. The developed compliant printed AMR sensor would be attractive to implement on curved and/or dynamic bendable surfaces for on-skin applications and interactive printed electronics.

Indium kann sekundärmetallurgisch aus der Prozessierung von Schlacken, Flugstäuben und metallischen Zwischenprodukten aus der Zinkdarstellung gewonnen werden. Eine weitere Möglichkeit der Indiumgewinnung stellt sich in der Aufbereitung von Rückständen des Bergbaus durch Biolaugungsprozesse dar. Höhere Konzentrationen an Eisen und Zink sind in den gewinnbaren Laugen im Vergleich zu sehr niedrigen Indiumkonzentrationen häufig präsent. Ein Trenn- und Aufbereitungsverfahren für die Verarbeitung von hydro-metallurgischen Prozesslösungen und die Gewinnung von Einsatzstoffen aus diesen wird durch Ionenaustauscherharze realisiert. Sowohl kationische als auch anionische feste Ionenaustauscherharze zeigen für Indium in sauren wässrigen Lösungen eine Affinität für die Indiumadsorption. Hinsichtlich der selektiven Adsorption von Indium gegenüber Eisen und Zink mittels festen Anionenaustauschern wurde der Einfluss der selektiven Komplexbildung von Indium durch die Zugabe von Iod in der Form von Kaliumiodid auf die Adsorption untersucht. Als Referenzsystem wurde die Indiumadsorption an festen Kationen-austauscherharzen gewählt. Die selektive Adsorption für Indium wurde aus einer vereinfachten Modelllösung der Biolaugungslösung des „ReMining“-Projektes hinsichtlich der Faktorgrößen des pH-Wertes, der Kaliumiodid- und Indiumadsorption im kleineren Maßstab im Becherglas untersucht. Die bestimmten Optima wurden auf die Prozessierung der realen Biolaugungslösung in Ionenaustauschersäulen angewandt. Indium kann in vergleichbaren quantitativen Mengen sowohl als anionischer Komplex ([InI4]-) von den getesteten Anionenaustauscherharzen A 111 und A 500 als auch von dem Kationenaustauscherharz MTS 9300 als Kation In3+ durch Adsorption aus der Modell- und Biolaugungslösung extrahiert werden. Beide Ionenaustauscher zeigen höchste Selektivitäten gegenüber Eisen, Arsen und Aluminium. Der Vergleich der Konzentrationsverhältnisse von Feed und dem Eluat zeigt, dass Eisen zu ~ 700 Mal mehr wie Indium (Fe/In = ~ 700) im Feed vorhanden ist und nach dem Ionenaustausch ~ 0,7 Mal so viel wie Indium (Fe/In = ~ 0,7) im Eluat verbleibt. Kupfer und Cadmium konnten von dem Anionenaustauscherharz A 111 nicht mit destilliertem Wasser und 0,1 M Schwefelsäure eluiert werden. In der Gesamtbetrachtung der selektiven Adsorption und Eluation von Indium aus der realen Biolaugungslösung ist das Kationenaustauscherharz MTS 9300 dem Anionenaustauscherharz A 111 vorzuziehen.

The transformative emergence of smart electronics, human-friendly robotics and supplemented or virtual reality will revolutionize the interplay with our surrounding. The complexity that is involved in the manipulation of objects in these emerging technologies is dramatically increased, which calls for electronic skins (e-skin) that can conduct tactile and touchless sensing events in a simultaneous and unambiguous way. Integrating multiple functions in a single sensing unit offers the most promising path towards simple, scalable and intuitive-to-use e-skin architectures. However, by now, this path has always been hindered by the confusing overlap of signals from different stimuli.
Here, we put forward the field of soft, flexible electronics by developing a compliant magnetic microelectromechanical platform (m-MEMS), which is able to transduce both tactile (via mechanical pressure) and touchless (via magnetic field) stimulations simultaneously and discriminate them in real time [1]. For the first time, the electric signals from tactile and touchless interactions are intrinsically separated into two different regions, allowing the m-MEMS, a single sensor unit, to unambiguously distinguish the two modes without knowing the signal history.
Owing to its intrinsic magnetic functionality, our complaint m-MEMS platform is able to discriminate magnetic vs. non-magnetic objects already upon touchless interaction. With this intrinsic selectivity, we address the long-standing problem in the field of touchless interaction – namely, the issue of interference with objects, which are irrelevant or even disturbing the interaction process. In addition, the interaction process is programmable. The sensitivity of the two interaction modes could be tuned by adjusting the magnetic field of the objects able to meet the requirements of different interaction tasks.
By using tactile and touchless sensing functions simultaneously, our m-MEMS e-skins enable complex interactions with a magnetically functionalized physical object that is supplemented with content data appearing in the virtual reality. We demonstrated data selection and manipulation with our m-MEMS e-skins leading to the realization of a multi-choice for augmented reality through three dimensional (3D) touch. Beyond the field of augmented reality, our m-MEMS will bring great benefits for healthcare, e.g. to ease surgery operations and manipulation of medical equipment, as well as for humanoid robots to overcome the challenging task of grasping.

[1] J. Ge, X. Wang, M. Drack, O. Volkov, M. Liang, G. S. Cañón Bermúdez, R. Illing, C. Wang, S. Zhou, J. Fassbender, M. Kaltenbrunner, and D. Makarov. A bimodal soft electronic skin for tactile and touchless interaction in real time. Nature Communications 10, 4405 (2019).

Mechanical flexibility and even stretchability of functional elements is a key enabler of numerous applications including wearable electronics, healthcare and medical appliances. The magnetism community developed the family of high-performance shapeable magnetoelectronics [1], which contain flexible [2-4], printable [5-7], stretchable [8-11] and even mechanically imperceptible [12-16] magnetic field sensorics. The technology relies on a smart combination of thin inorganic functional elements prepared directly on flexible or elastomeric supports. The concept of shapeable magnetoelectronics is explored for various applications ranging from automotive [17] through consumer electronics and point of care [2,18] to virtual and augmented reality [14-16] applications.
Here, we will focus on the use of compliant magnetosensitive skins [14-16] for augmented reality systems. We demonstrate that e-skin compasses [14] allow humans to orient with respect to earth’s magnetic field ubiquitously. The biomagnetic orientation enables the realization of a touchless control of virtual units in a game engine using omnidirectional magnetosensitive skins (Fig. 1).
This concept was further extended by demonstrating a compliant magnetic microelectromechanical platform (m-MEMS), which is able to transduce both tactile (via mechanical pressure) and touchless (via magnetic field) stimulations simultaneously and discriminate them in real time [16] (Fig. 2). We demonstrate data selection and manipulation with our m-MEMS e-skins leading to the realization of a multi-choice menu for augmented reality through three dimensional (3D) touch. Beyond the field of augmented reality, our m-MEMS will bring great benefits for healthcare, e.g. to ease surgery operations and manipulation of medical equipment, as well as for humanoid robots to overcome the challenging task of grasping.
[1] D. Makarov et al., Appl. Phys. Rev. (Review) 3, 011101 (2016).
[2] G. Lin, D. Makarov et al., Lab Chip 14, 4050 (2014).
[3] N. Münzenrieder, D. Makarov et al., Adv. Electron. Mater. 2, 1600188 (2016).
[4] M. Melzer, D. Makarov et al., Adv. Mater. 27, 1274 (2015).
[5] D. Makarov et al., ChemPhysChem (Review) 14, 1771 (2013).
[6] D. Karnaushenko, D. Makarov et al., Adv. Mater. 24, 4518 (2012).
[7] D. Karnaushenko, D. Makarov et al., Adv. Mater. 27, 880 (2015).
[8] M. Melzer, D. Makarov et al., J. Phys. D: Appl. Phys. (Review) 53, 083002 (2020).
[9] M. Melzer, D. Makarov et al., Nano Lett. 11, 2522 (2011).
[10] M. Melzer, D. Makarov et al., Adv. Mater. 24, 6468 (2012).
[11] M. Melzer, D. Makarov et al., Adv. Mater. 27, 1333 (2015).
[12] M. Melzer, D. Makarov et al., Nat. Commun. 6, 6080 (2015).
[13] P. N. Granell, D. Makarov et al., npj Flexible Electronics 3, 3 (2019).
[14] G. S. Cañón Bermúdez, D. Makarov et al., Nature Electronics 1, 589 (2018).
[15] G. S. Cañón Bermúdez, D. Makarov et al., Science Advances 4, eaao2623 (2018).
[16] J. Ge, D. Makarov et al., Nature Communications 10, 4405 (2019).
[17] M. Melzer, D. Makarov et al., Adv. Mater. 27, 1274 (2015).
[18] G. Lin, D. Makarov et al., Lab Chip (Review) 17, 1884 (2017).

Gedruckte Elektronik wird das Gebiet der konventionellen Elektronik revolutionieren und eine kostengünstige, großflächige Produktion mit hohem Durchsatz ermöglichen. Durch Hinzufügen eines Magnetfeldsensors zur Familie der druckbaren Elektronik [1] wollen wir energieeffiziente kontaktlose Schalter für intelligente Verpackungen oder Postkarten sowie intelligente und schützende Kleidung (z. B. für Feuerwehrleute, Sportler) realisieren mit einem In- Stoff integrierte Navigations- und Positionsverfolgungsmodule. Für dieses Konzept wurden hochleistungsfähige druckbare Magnetfeldsensoren realisiert, die auf dem Riesenmagnetowiderstandseffekt (GMR) beruhen [2]. Diese Sensoren werden aus einer Paste gedruckt, die GMR-Flocken enthält, welche mittels Dünnschichttechnologien hergestellt wurden. Solche GMR-Sensoren können auch auf flexiblen Substraten siebgedruckt werden und bleiben in einem Temperaturbereich von -10°C bis +95°C [3] gemäß den Anforderungen an die Unterhaltungselektronik voll funktionsfähig.
In dieser Präsentation werden wir die aktuelle Technologie zur Realisierung von druckbaren Hochleistungs-Magnetfeldsensoren überprüfen. Wir werden zeigen, dass GMR-Sensoren auf ultradünne Polymerfolien mit einer Foliendicke von bis zu 6 µm gedruckt werden können. Die Verwendung eines geeigneten Polymerbindemittels für die GMR-Paste gewährleistet hervorragende Perkolationskontakte zwischen GMR-Mikroflocken und ermöglicht eine hohe Sensorempfindlichkeit von 3 T-1 bei einem niedrigen Magnetfeld von etwa 1 mT. Die Haftung zwischen dem gedruckten Sensor und der Polymerfolie ist ausreichend stark, um einer Biegung des Sensors auf einen Krümmungsradius von 16 µm standzuhalten, ohne die mechanische Integrität der Vorrichtung zu beeinträchtigen. Mit dieser Leistung können unsere gedruckten GMR-Sensoren für interaktive Elektronik auf der Haut verwendet werden, die wir mit einer berührungslosen Steuerung virtueller Objekte für die praktische Anwendung in tragbaren Geräten, künstlicher Prothetik, Robotik und im Internet der Dinge präsentieren.
[1] D. Makarov et al., ChemPhysChem (Review) 14, 1771 (2013).
[2] D. Karnaushenko, D. Makarov et al., Adv. Mater. 24, 4518 (2012).
[3] D. Karnaushenko, D. Makarov et al., Adv. Mater. 27, 880 (2015).

Thin film magnetoelectric antiferromagnets (AF) have potential to revolutionize spintronics due to their inherently magnetic-field stable magnetic order and high-frequency operation. To explore their application potential, it is necessary to understand modifications of the magnetic properties of AF thin films with respect to their bulk counterparts. We will outline our developments of zero-offset anomalous Hall magnetometry [1] applied to study the physics of insulating magnetoelectric Cr2O3 antiferromagnets. The analysis of the transport data is backed up by the real space imaging of AF domain patterns using NV microscopy [2,3]. Considering grainy morphology of thin films, we address questions regarding the change of the intergranular exchange [3], criticality behavior and switching of the order parameter [1] and physics of the readout signal in α-Cr2O3 interfaced with Pt [4]. The possibility to read-out the antiferromagnetic order parameter all-electrically enabled a new recording concept of antiferromagnetic magnetoelectric random access memory (AF-MERAM) [2].
[1] T. Kosub et al., Phys. Rev. Lett. 115, 097201 (2015).
[2] T. Kosub et al., Nat. Commun. 8, 13985 (2017).
[3] P. Appel et al., Nano Lett. 19, 1682 (2019)
[4] R. Schlitz et al., Appl. Phys. Lett. 112, 132401 (2018).

The recent rapid advance and eagerness of portable consumer electronics stimulate the development of functional elements towards being lightweight, flexible, and wearable. Next generation flexible appliances aim to become fully autonomous and will require ultra-thin and flexible navigation modules, body tracking and relative position monitoring systems. Key building blocks of navigation and position tracking devices are magnetic field sensors.
Although there is a remarkable progress in the field of shapeable magnetoelectronics [1], until recently there was no technology available that can enable sensitivities to geomagnetic fields of 50 µT and, ultimately, magnetic fields of smaller than 1 µT in a mechanically compliant form factor. If available, these devices would contribute greatly to the realization of high-performance on-skin interactive electronics [2,3] and point of care applications [4,5].
Here, we will present technological platforms allowing to realize not only mechanically imperceptible electronic skins, which enable perception of the geomagnetic field (e-skin compasses) [6], but also enable sensitivities down to ultra-small fields of sub-50 nT [7]. We demonstrate that e-skin compasses allow humans to orient with respect to earth’s magnetic field ubiquitously. Furthermore, biomagnetic orientation enables novel interactive devices for virtual and augmented reality applications. We showcase this by realizing touchless control of virtual units in a game engine using omnidirectional magnetosensitive skins (fig. 1). This concept was further extended by demonstrating a compliant magnetic microelectromechanical platform (m-MEMS), which is able to transduce both tactile (via mechanical pressure) and touchless (via magnetic field) stimulations simultaneously and discriminate them in real time [8]. Those devices are crucial for interactive electronics, human-machine interfaces, but also for the realization of smart soft robotics with highly compliant integrated feedback system as well as in medicine for physicians and surgeons.

[1] D. Makarov et al., Applied Physics Reviews 3 (2016), 011101.
[2] G. S. Canon Bermudez et al., Science Advances 4 (2018), eaao2623.
[3] M. Melzer et al., Nature Communications 6 (2015), 6080.
[4] G. Lin et al., Lab Chip 14 (2014), 4050.
[5] G. Lin et al., Lab Chip 17 (2017), 1884.
[6] G. S. Canon Bermudez et al., Nature Electronics 1 (2018), 589.
[7] P. N. Granell et al., npj Flexible Electronics 3 (2019), 3.
[8] J. Ge et al., Nature Communications (2019). doi:10.1038/s41467-019-12303-5

We will review the recent progress in the field of shapeable magnetoelectronics [1] allowing to realize not only mechanically imperceptible electronic skins [2-4], which enable perception of the geomagnetic field (e-skin compasses) [5], but also enable sensitivities down to ultra-small fields of sub-50 nT [6]. We demonstrate that e-skin compasses allow humans to orient with respect to earth’s magnetic field ubiquitously. The biomagnetic orientation enables novel interactive devices for virtual and augmented reality applications, which is showcased by realizing touchless control of virtual units in a game engine using omnidirectional magnetosensitive skins. This concept was further extended by demonstrating a compliant magnetic microelectromechanical platform (m-MEMS), which is able to transduce both tactile (via mechanical pressure) and touchless (via magnetic field) stimulations simultaneously and discriminate them in real time [7]. Those devices are crucial for interactive electronics, human-machine interfaces, but also for the realization of smart soft robotics with highly compliant integrated feedback system as well as in medicine for physicians and surgeons
[1] D. Makarov et al., Applied Physics Reviews (Review) 3, 011101 (2016).
[2] M. Melzer, DM et al., J. Phys. D: Appl. Phys. (Review) 53, 083002 (2020).
[3] S. Canon, DM et al., Science Advances 4, eaao2623 (2018).
[4] M. Melzer, DM et al., Nature Communications 6, 6080 (2015).
[5] S. Canon, DM et al., Nature Electronics 1, 589 (2018).
[6] P.N. Granell, DM et al., npj Flexible Electronics 3, 3 (2019).
[7] J. Ge, DM et al., Nature Communications 10, 4405 (2019).

The portable consumer electronics necessitates functional elements to be lightweight, flexible, and wearable [1-4]. The unique possibility to adjust the shape of the devices offered by this alternative formulation of the electronics provides vast advantages over the conventional rigid devices particularly in medicine and consumer electronics. There is already a remarkable number of available flexible devices starting from interconnects, sensing elements towards complex platforms consisting of communication and diagnostic components.
We developed shapeable magnetoelectronics [5] – namely, flexible [6-8], printable [9,10], stretchable [11,12] and even imperceptible [13] magnetosensitive large area elements, which were completely missing in the family of flexible electronics. The unique mechanical properties open up new application potentials for smart skins, allowing to equip the recipient with a “sixth sense” providing new experiences in sensing and manipulating the objects of the surrounding us physical as well as digital world [7,13]. On the other hand, we realized self-assembled compact tubular microchannels based on strain engineering [14] with integrated passive sensory elements [15-17] and communication antenna devices [18] for on-chip and bio-medical applications, e.g. smart implants [19,20].
Combining these two research directions carried out at different length scales into a single truly interdisciplinary topic opens up the novel field of smart biomimetics [20]. In this respect, we demonstrated mechanically and electrically active compact biomimetic microelectronics, which can serve as a base for realization of novel regenerative neuronal cuff implants with unmatched functionalities. The biomimetic microelectronics can mechanically adapt to and impact the environment possessing the possibility to assess, adopt and communicate the environmental changes and even stimulate the environment electrically.
In my talk, these recent developments will be covered.

[1] M. G. Lagally, MRS Bull., 32, 57 (2007).
[2] J. A. Rogers et al., Nature, 477, 45 (2011).
[3] S. Bauer et al., Adv. Mater., 26, 149 (2014).
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[5] D. Makarov et al., Appl. Phys. Rev., 3, 011101 (2016).
[6] G. Lin, D. Makarov et al., Lab Chip, 14, 4050 (2014).
[7] M. Melzer, D. Makarov et al., Adv. Mater., 27, 1274 (2015).
[8] N. Münzenrieder, D. Makarov et al., Adv. Electron. Mater., 2, 1600188 (2016).
[9] D. Karnaushenko, D. Makarov et al., Adv. Mater., 27, 880 (2015).
[10] D. Karnaushenko, D. Makarov et al., Adv. Mater., 24, 4518 (2012).
[11] M. Melzer, D. Makarov et al., Adv. Mater., 27, 1333 (2015).
[12] M. Melzer, D. Makarov et al., Nano Lett., 11, 2522 (2011).
[13] M. Melzer, D. Makarov et al., Nat. Commun., 6, 6080 (2015).
[14] O. G. Schmidt et al., Nature, 410, 168 (2001).
[15] I. Mönch, D. Makarov et al., ACS Nano, 5, 7436 (2011).
[16] C. Müller, D. Makarov et al., Appl. Phys. Lett., 100, 022409 (2012).
[17] E. J. Smith, D. Makarov et al., Lab Chip, 12, 1917 (2012).
[18] D. D. Karnaushenko, D. Makarov et al., NPG Asia Materials, 7, e188 (2015).
[19] D. Karnaushenko, D. Makarov et al., Adv. Mater., 27, 6582 (2015).
[20] D. Karnaushenko, D. Makarov et al., Adv. Mater., 27, 6797 (2015).

The main origin of the chiral symmetry breaking in magnetic materials is associated with the intrinsic Dzaloshinskii-Moriya interaction (DMI). At present, tailoring of DMI is done rather conventionally by optimizing materials, either doping a bulk single crystal or adjusting interface properties of thin films and multilayers. A viable alternative to the conventional material screening approach can be the exploration of the interplay between geometry and topology. The research field in magnetism, which is dealing with the study of the impact of geometrical curvature on magnetic responses of curved 1D wires and 2D shells is known as curvilinear magnetism [1]. The perspective of the development of curvilinear magnetism is outlined in the 2017 and 2020 Magnetism Roadmaps [2,3]. In this presentation, we will discuss on the recent achievements in the field and address the following topics:

A fully 3D approach to treat curvilinear effects in ferromagnetic nanowires and thin shells of arbitrary shape is established by Gaididei et al. back in 2014 [4] and was recently extended by Sheka et al. [5] to properly account for effects of non-locality due to the presence of long-range magnetostatic interaction. Volkov et al. has proven that the exchange-driven chiral effects in curvilinear ferromagnets are experimental observables [6] and can be used to realize nanostructures with tunable magnetochiral properties from standard magnetic materials.
In contrast to the intrinsic DMI, a concept of mesoscale Dzyaloshinskii-Moriya interaction was put forth, which is a result of the interplay between the intrinsic (spin-orbit-driven) and extrinsic (curvature-driven) DMI terms [7]. The mesoscale DMI governs the magnetochiral properties of any curvilinear ferromagnetic nanosystem and depends both on the material and geometrical parameters. Its strength and orientation can be tailored by properly choosing the geometry, which allows stabilizing distinct magnetic chiral textures including skyrmion and skyrmionium states as well as skyrmion lattices [8-10]. Interestingly, skyrmion states can be formed in a material even without an intrinsic DMI [8,10].
Sheka et al. [5] discovered a novel non-local chiral symmetry breaking effect, which does not exist in planar magnets: it is essentially non-local and manifests itself even in static spin textures living in curvilinear magnetic nanoshells. To identify this new interaction, a generalized micromagnetic theory of curvilinear ferromagnets was constructed accounting for local and nonlocal effects. The curvature leads to the emergence of the new magnetostatic charge, the geometrical charge, determined by the local characteristics of the surface. This newcomer is responsible for the appearance of novel fundamental chiral symmetry breaking effect.
The field of curvilinear magnetism was recently extended towards curvilinear antiferromagnets. Pylypovskyi et al. [11] demonstrated that intrinsically achiral one-dimensional curvilinear antiferromagnet behaves as a chiral helimagnet with geometrically tunable DMI, orientation of the Neel vector and the helimagnetic phase transition. This positions curvilinear antiferromagnets as a novel platform for the realization of geometrically tunable chiral antiferromagnets for antiferromagnetic spinorbitronics.

[1] Streubel et al., J. Phys. D: Appl. Phys. 49, 363001 (2016).
[2] Sander et al., J. Phys. D: Appl. Phys. 50, 363001 (2017).
[3] Vedmedenko et al., J. Phys. D: Appl. Phys. 53, 453001 (2020).
[4] Gaididei et al., PRL 112, 257203 (2014).
[5] Sheka et al., Communications Physics 3, 128 (2020).
[6] Volkov et al., PRL 123, 077201 (2019).
[7] Volkov et al., Scientific Reports 8, 866 (2018).
[8] Kravchuk et al., PRB 94, 144402 (2016).
[9] Kravchuk et al., PRL 120, 067201 (2018).
[10] Pylypovskyi et al., Phys. Rev. Appl. 10, 064057 (2018).
[11] Pylypovskyi et al., Nano Letters (2020). doi:10.1021/acs.nanolett.0c03246.

Extending 2D structures into 3D space has become a general trend in multiple disciplines, including electronics, photonics, plasmonics and magnetics. This approach provides means to modify conventional or to launch novel functionalities by tailoring curvature and 3D shape. We study fundamentals of 3D curved magnetic thin films [1] and explore their application potential for flexible electronics, eMobility and health. For these applications, we developed a technology platform known as shapeable magnetoelectronics [2], which relies on a smart combination of ultrathin polymeric foils and metallic thin films featuring magnetoresistive and Hall effects. The mechanically compliant magnetic field sensors are designed and fabricated to address the specific needs of different applications including automotive (monitoring and control of electrical machines and drives) [3-5], biosensing technologies (flexible microfluidic devices) [6,7], consumer electronics (interactive printed electronics) [8,9], orientation in space [10] as well as virtual and augmented reality devices (motion tracking and touchless human-machine interaction) [10-13].
In this presentation, we will review the approaches to fabricate mechanically shapeable magnetic field sensors as well as their magnetoresistive and mechanical performance. On the application side, we will focus on the demonstration of the shapeable sensor devices for the emerging technological fields of smart skins, soft robotics and human-machine interfaces.

[1] R. Streubel, D. Makarov et al.: Magnetism in curved geometries. Journal of Physics D: Applied Physics (Topical Review) 49, 363001 (2016).
[2] D. Makarov et al.: Shapeable magnetoelectronics. Applied Physics Reviews 3, 011101 (2016).
[3] M. Melzer, D. Makarov et al.: Wearable magnetic field sensors for flexible electronics. Advanced Materials 27, 1274 (2015).
[4] D. Ernst, D. Makarov et al.: Packaging technologies for (ultra-)thin sensor applications in active magnetic bearings. IEEE Proceedings of the 37th International Spring Seminar on Electronics Technology (ISSE), pp. 125-129 (2014). doi:10.1109/ISSE.2014.6887577
[5] I.J. Mönch, D. Makarov et al.: Flexible Hall sensorics for flux based control of magnetic levitation. IEEE Trans. Magn. 51, 4004004 (2015).
[6] G. Lin, D. Makarov et al.: Magnetic sensing platform technologies for biomedical applications. Lab Chip 17, 1884 (2017).
[7] G. Lin, D. Makarov et al.: A highly flexible and compact magnetoresistive analytic device. Lab Chip 14, 4050 (2014).
[8] D. Makarov et al.: Printable magnetoelectronics. ChemPhysChem 14, 1771 (2013).
[9] D. Karnaushenko, D. Makarov et al.: High-performance magnetic sensorics for printable and flexible electronics. Advanced Materials 27, 880 (2015).
[10] G. S. Cañón Bermúdez, D. Makarov et al.: Electronic-skin compasses for geomagnetic field driven artificial magnetoception and interactive electronics. Nature Electronics 1, 589 (2018).
[11] G. S. Cañón Bermúdez, D. Makarov et al.: Magnetosensitive e-skins with directional perception for augmented reality. Science Advances 4, eaao2623 (2018).
[12] J. Ge, D. Makarov et al.: A bimodal soft electronic skin for tactile and touchless interaction in real time. Nature Communications 10, 4405 (2019).
[13] P. N. Granell, D. Makarov et al.: Highly compliant planar Hall effect sensor with sub 200 nT sensitivity. npj Flexible Electronics 3, 3 (2019).

In this talk I will review our activities on the realization of magnetoceptive smart skins.

Non-collinear magnetic textures like spin spirals, chiral domain walls or skyrmions are typically stabilized by the intrinsic spin-orbit induced Dzyaloshinskii-Moriya interaction (DMI) [1]. Curvature effects emerged as a novel mean to design chiral magnetic responses relying on extrinsic parameters, i.e. geometrical curvature of thin films [2-4]. The lack of an inversion symmetry and the emergence of a curvature induced effective anisotropy and DMI are characteristic of curved surfaces, leading to curvature-driven magnetochiral effects and topologically induced magnetization patterning [5-7]. Vast majority of activities are dedicated to curved ferromagnets, where recent achievements include the development of the theory of curvilinear micromagnetism [3] and the first experimental confirmation of curvature-driven chiral effects stemming from the exchange interaction [4]. Only very recently, the focus was put also on curvilinear antiferromagnets. Pylypovskyi et al. [8] demonstrated that intrinsically achiral one-dimensional curvilinear antiferromagnets behave as a chiral helimagnet with geometrically tunable DMI and orientation of the Neel vector.
The application potential of 3D-shaped magnetic thin films is currently being explored as mechanically shapeable magnetic field sensors [9] for automotive applications, magnetoelectrics for memory devices, spin-wave filters, high-speed racetrack memory devices as well as on-skin interactive electronics [10-12].
The fundamentals as well as application relevant aspects of curvilinear ferro- and antiferromagnets will be covered in this presentation.

References

[1] D. Sander, DM et al., J. Phys. D 50, 363001 (2017)
[2] R. Streubel, DM et al., J. Phys. D 49, 363001 (2016)
[3] D. Sheka, DM et al., Communications Physics 3, 128 (2020)
[4] O. M. Volkov, DM et al., Phys. Rev. Lett. 123, 077201 (2019)
[5] V. Kravchuk, DM et al., Phys. Rev. Lett. 120, 067201 (2018)
[6] O. Pylypovskyi, DM et al., Phys. Rev. Appl. 10, 064057 (2018)
[7] O. Pylypovskyi, DM et al., Phys. Rev. Lett. 114, 197204 (2015)
[8] O.Pylypovskyi, DM et al., Nano Lett. (2020) doi:10.1021/acs.nanolett.0c03246
[9] D. Makarov et al., Appl. Phys. Rev. 3, 011101 (2016)
[10] S. Canon Bermudez, DM et al., Science Advances 4, eaao2623 (2018)
[11] S. Canon Bermudez, DM et al., Nature Electronics 1, 589 (2018)
[12] J. Ge, DM et al., Nature Communications 10, 4405 (2019).

Recent advances in the field of flexible electronics have opened the door for this technology to deeply impact the health care sector. The development of sensors and actuators which are lightweight and mechanically compliant enables them to be used for continuous health monitoring, on-site therapies or soft chirurgical ads. The key feature of these novel gadgets is their ability to provide targeted treatment and diagnosis without constraining the natural motion of the body or its internal organs.
Though many of these flexible diagnostic or therapeutic devices have been successfully demonstrated already, cancer treatment remains relatively unexplored in this field. In particular, hepatocellular carcinoma (HCC, liver cancer) is one of the leading causes of cancer related mortalities worldwide with a constantly growing incidence. Numerous efforts have been devoted to the development of targeted cancer treatments which selectively destroy cancer cells and spare the healthy tissue.
We propose and develop an implantable, multifunctional and highly compliant device for targeted thermal treatment of cancerous tissues [1]. The device is fabricated on a 6-µm-thick polymeric foil, which seamlessly conforms to the soft liver tissue and allows for precisely controlled joule heating without on-site rigid parts. Its high mechanical compliance provides stable readings even upon severe mechanical deformations, enabling temperature accuracies of 0.1°C at bending radii of 2.5 mm, characteristic for mouse liver tissues. This heating device can treat tissue over the whole range of temperatures leading to fever, hyperthermia and ablation, while using a driving current as low as 10 mA. We demonstrate the electro-thermal and mechanical characterization of the devices and study various heat impact scenarios on normal and cancerous tissue using autochthonous murine HCC models.
Due to their high mechanical compliance, stability and thermal treatment versatility, the here developed devices can become a complement or alternative solution to radio frequency ablation (RFA) techniques for cancer treatment.

[1] G. S. Cãnón Bermudez, A. Kruv, T. Voitsekhivska, I. Hochnadel, A. Lebanov, A. Potthoff, J. Fassbender, T. Yevsa, and D. Makarov, “Implantable Highly Compliant Devices for Heating of Internal Organs: Toward Cancer Treatment”. Adv. Eng. Mater. 21, 1900407 (2019).

Extending 2D structures into 3D space has become a general trend in multiple disciplines, including electronics, photonics, plasmonics and magnetics. This approach provides means to modify conventional or to launch novel functionalities by tailoring curvature and 3D shape. We study fundamentals of 3D curved magnetic thin films [1] and explore their application potential for flexible electronics, eMobility and health. We put forth the concept of shapeable magnetoelectronics [2] for various applications ranging from automotive [3-5] through consumer electronics to virtual and augmented reality [6-9] applications. These activities impact several emerging research fields of smart skins, soft robotics and human-machine interfaces. In this talk, recent fundamental and technological advancements in this research field will be reviewed.

[1] R. Streubel, D. Makarov et al., J. Phys. D: Appl. Phys. (Review) 49, 363001 (2016).
[2] D. Makarov et al., Appl. Phys. Rev. (Review) 3, 011101 (2016).
[3] M. Melzer, D. Makarov et al., Adv. Mater. 27, 1274 (2015).
[4] I. J. Mönch, D. Makarov et al., IEEE Trans. Magn. 51, 4004004 (2015).
[5] D. Ernst, D. Makarov et al., IEEE Proceedings of the 37th International Spring Seminar on Electronics Technology (ISSE), pp. 125-129 (2014). doi:10.1109/ISSE.2014.6887577
[6] G. S. Cañón Bermúdez, D. Makarov et al., Science Advances 4, eaao2623 (2018).
[7] G. S. Cañón Bermúdez, D. Makarov et al., Nature Electronics 1, 589 (2018).
[8] P. N. Granell, D. Makarov et al., npj Flexible Electronics 3, 3 (2019).
[9] J. Ge, D. Makarov et al., Nature Communications 10, 4405 (2019).

Extending 2D structures into 3D space has become a general trend in multiple disciplines including electronics, photonics, and magnetics. This approach provides means to enrich conventional or to launch novel functionalities by tailoring curvature and 3D shape. We study 3D curved magnetic thin films and nanowires where new fundamental effects emerge from the interplay of the geometry of an object and topology of a magnetic sub-system [1-3]. On the other hand, we explore the application potential of 3D magnetic architectures for the realization of mechanically shapeable magnetoelectronics [4] for automotive but also virtual and augmented reality appliances [5-7]. In this respect, we will present technological platforms allowing to realize not only mechanically imperceptible electronic skins, which enable perception of the geomagnetic field (e-skin compasses) [6], but also enable sensitivities down to ultra-small fields of sub-200 nT [8]. We demonstrate that e-skin compasses allow humans to orient with respect to earth’s magnetic field ubiquitously. Furthermore, biomagnetic orientation enables novel interactive devices for virtual and augmented reality applications. We showcase this by realizing touchless control of virtual units in a game engine using omnidirectional magnetosensitive skins. This concept was further extended by demonstrating a compliant magnetic microelectromechanical platform (m-MEMS), which is able to transduce both tactile (via mechanical pressure) and touchless (via magnetic field) stimulations simultaneously and discriminate them in real time [7]. Those devices are crucial for interactive electronics, human-machine interfaces, but also for the realization of smart soft robotics with highly compliant integrated feedback system as well as in medicine for physicians and surgeons. In this talk, recent fundamental and technological advancements in this novel research field will be reviewed.

[1] R. Streubel, DM et al., Magnetism in curved geometries. J. Phys. D: Appl. Phys. (Review) 49, 363001 (2016).
[2] D. Sander, DM et al., The 2017 magnetism roadmap. J. Phys. D: Appl. Phys. (Review) 50, 363001 (2017).
[3] O. M. Volkov, DM et al., Experimental observation of exchange-driven chiral effects in curvilinear magnetism. Phys. Rev. Lett. 123, 077201 (2019).
[4] D. Makarov et al., Shapeable magnetoelectronics. Appl. Phys. Rev. (Review) 3, 011101 (2016).
[5] G. S. Cañón Bermúdez, DM et al., Magnetosensitive e-skins with directional perception for augmented reality. Science Advances 4, eaao2623 (2018).
[6] G. S. Cañón Bermúdez, DM et al., Electronic-skin compasses for geomagnetic field driven artificial magnetoception and interactive electronics. Nature Electronics 1, 589 (2018).
[7] J. Ge, DM et al., A bimodal soft electronic skin for tactile and touchless interaction in real time. Nature Communications 10, 4405 (2019).
[8] P. Granell, DM et al., Highly compliant planar Hall effect sensor with sub 200 nT sensitivity. npj Flexible Electronics 3, 3 (2019).

Extending 2D structures into 3D space has become a general trend in multiple disciplines including electronics, photonics, and magnetics. This approach provides means to enrich conventional or to launch novel functionalities by tailoring curvature and 3D shape. We study 3D curved magnetic thin films and nanowires where new fundamental effects emerge from the interplay of the geometry of an object and topology of a magnetic sub-system [1-4]. On the other hand, we explore the application potential of 3D magnetic architectures for the realization of mechanically shapeable magnetoelectronics [5] for automotive but also virtual and augmented reality appliances [6-8]. In this respect, we will present technological platforms allowing to realize not only mechanically imperceptible electronic skins, which enable perception of the geomagnetic field (e-skin compasses) [7], but also enable sensitivities down to ultra-small fields of sub-200 nT [9]. We demonstrate that e-skin compasses allow humans to orient with respect to earth’s magnetic field ubiquitously. Furthermore, biomagnetic orientation enables novel interactive devices for virtual and augmented reality applications. We showcase this by realizing touchless control of virtual units in a game engine using omnidirectional magnetosensitive skins. This concept was further extended by demonstrating a compliant magnetic microelectromechanical platform (m-MEMS), which is able to transduce both tactile (via mechanical pressure) and touchless (via magnetic field) stimulations simultaneously and discriminate them in real time [8]. Those devices are crucial for interactive electronics, human-machine interfaces, but also for the realization of smart soft robotics with highly compliant integrated feedback system as well as in medicine for physicians and surgeons. In this talk, recent fundamental and technological advancements in this novel research field will be reviewed.

[1] R. Streubel, DM et al., Magnetism in curved geometries. J. Phys. D: Appl. Phys. (Review) 49, 363001 (2016).
[2] D. Sander, DM et al., The 2017 magnetism roadmap. J. Phys. D: Appl. Phys. (Review) 50, 363001 (2017).
[3] O. M. Volkov, DM et al., Experimental observation of exchange-driven chiral effects in curvilinear magnetism. Phys. Rev. Lett. 123, 077201 (2019).
[4] V. P. Kravchuk, DM et al., Multiplet of Skyrmion states on a curvilinear defect: Reconfigurable Skyrmion lattices. Phys. Rev. Lett. 120, 067201 (2018).
[5] D. Makarov et al., Shapeable magnetoelectronics. Appl. Phys. Rev. (Review) 3, 011101 (2016).
[6] G. S. Cañón Bermúdez, DM et al., Magnetosensitive e-skins with directional perception for augmented reality. Science Advances 4, eaao2623 (2018).
[7] G. S. Cañón Bermúdez, DM et al., Electronic-skin compasses for geomagnetic field driven artificial magnetoception and interactive electronics. Nature Electronics 1, 589 (2018).
[8] J. Ge, DM et al., A bimodal soft electronic skin for tactile and touchless interaction in real time. Nature Communications 10, 4405 (2019).
[9] P. Granell, DM et al., Highly compliant planar Hall effect sensor with sub 200 nT sensitivity. npj Flexible Electronics 3, 3 (2019).

There is one aspect, which is in common to the majority of fundamentally appealing and technologically relevant novel magnetic materials, namely their non-collinear magnetic textures like spin spirals, chiral domain walls or skyrmions [1]. These textures are typically driven by the Dzyaloshinskii-Moriya interaction (DMI). Recently, curvature effects emerged as a novel mean to design chiral magnetic properties by relying on extrinsic parameters, e.g. geometry of thin films [2]. In particular, novel effects occur when the magnetization is modulated by curvature leading to new magnetization configurations and is implications on the spin dynamics due to topological constraints. Advances in this novel field solely rely on the understanding of the fundamentals behind the modifications of magnetic responses of 3D-curved magnetic thin films [3-5] and nanowires [6,7]. The lack of an inversion symmetry and the emergence of a curvature induced effective anisotropy and DMI are characteristic of curved surfaces, leading to curvature-driven magnetochiral effects and topologically induced magnetization patterning [8,9]. The application potential of 3D-shaped objects is currently being explored as mechanically reshapeable magnetic field sensorics [10] for flexible interactive electronics [11-13], magnetic field sensors [14-18], curvilinear magnetoelectrics for memory devices [19], spin-wave filters and high-speed racetrack memory devices [20]. To advance in this research field, novel theoretical methods and fabrication/characterization techniques [21-24]. The fundamentals as well as application relevant aspects of curvilinear nanomagnets will be covered in this presentation.

[1] D. Sander, DM et al., “The 2017 Magnetism Roadmap”, J. Phys. D 50, 363001 (2017).
[2] R. Streubel, DM et al., “Magnetism in curved geometries”, J. Phys. D 49, 363001 (2016).
[3] Y. Gaididei et al., “Curvature Effects in Thin Magnetic Shells”, Phys. Rev. Lett. 112, 257203 (2014).
[4] V. Kravchuk, DM et al., “Multiplet of Skyrmion States on a Curvilinear Defect: Reconfigurable Skyrmion Lattices”, Phys. Rev. Lett. 120, 067201 (2018).
[5] O. V. Pylypovskyi, DM et al., “Chiral Skyrmion and Skyrmionium States Engineered by the Gradient of Curvature”, Phys. Rev. Appl. 10, 064057 (2018).
[6] O. M. Volkov, DM et al., “Mesoscale Dzyaloshinskii-Moriya interaction: geometrical tailoring of the magnetochirality”, Scientific Reports 8, 866 (2018).
[7] O. M. Volkov, DM et al., “Experimental observation of exchange-driven chiral effects in curvilinear magnetism”, Phys. Rev. Lett. 123, 077201 (2019).
[8] O. V. Pylypovskyi, DM et al., “Coupling of Chiralities in Spin and Physical Spaces: The Möbius Ring as a Case Study”, Phys. Rev. Lett. 114, 197204 (2015).
[9] J. A. Otalora et al., “Curvature-Induced Asymmetric Spin-Wave Dispersion”, Phys. Rev. Lett. 117, 227203 (2016).
[10] D. Makarov et al., “Shapeable magnetoelectronics”, Appl. Phys. Rev. 3, 011101 (2016).
[11] S. Canon Bermudez, DM et al., “Magnetosensitive e-skins with directional perception for augmented reality”, Science Advances 4, eaao2623 (2018).
[12] S. Canon Bermudez, DM et al., “Electronic-skin compasses for geomagnetic field driven artificial magnetoreception and interactive electronics”, Nature Electronics 1, 589 (2018).
[13] J. Ge, DM et al., “A bimodal soft electronic skin for tactile and touchless interaction in real time”, Nature Comm. 10, 4405 (2019).
[14] D. Karnaushenko, DM et al., “Self-assemled on-chip integrated giant magneto-impedance sensorics”, Adv. Mater. 27, 6582 (2015).
[15] G. Lin, DM et al., “A highly flexible and compact magnetoresistive analytic device”, Lab Chip 14, 4050 (2014).
[16] N. Münzenrieder, DM et al., “Entirely flexible on-site conditioned magnetic sensorics”, Adv. Electron. Mater. 2, 1600188 (2016).
[17] C. Becker et al., “Self-assembly of highly sensitive 3D magnetic field vector angular encoders”, Science Advances 5, eaay7459 (2019).
[18] M. Kondo et al., “Imperceptible magnetic sensor matrix system integrated with organic driver and amplifier circuits”, Science Advances 6, eaay6094 (2020)
[19] O. M. Volkov, DM et al., “Concept of artificial magnetoelectric materials via geometrically controlling curvilinear helimagnets”, J. Phys. D: Appl. Phys. 52, 345001 (2019).
[20] M. Yan et al., “Beating the Walker Limit with Massless Domain Walls in Cylindrical Nanowires”, Phys. Rev. Lett. 104, 057201 (2010).
[21] R. Streubel, DM et al., “Retrieving spin textures on curved magnetic thin films with full-field soft X-ray microscopies”, Nature Comm. 6, 7612 (2015).
[22] T. Kosub, DM et al., “Purely antiferromagnetic magnetoelectric random access memory”, Nature Comm. 8, 13985 (2017).
[23] M. Huth et al., “Focused electron beam induced deposition meets materials science”, Microelectron. Engineering 185-186, 9 (2018).
[24] M. Nord, DM et al., “Strain Anisotropy and Magnetic Domains in Embedded Nanomagnets”, Small 15, 1904738 (2019).

Recent rapid advances and eagerness of portable consumer electronics stimulate the development of functional elements towards being lightweight, flexible, and wearable. Next generation flexible appliances aim to become fully autonomous and will require ultra-thin and flexible navigation modules, body tracking and relative position monitoring systems. Key building blocks of navigation and position tracking devices are magnetic field sensors. In this presentation, recent developments in the emerging field of flexible magnetic field sensorics and its applications in printed electronics, eMobility, virtual and augmented reality settings will be reviewed.

As the dimensions of a material shrink from an extended bulk solid to a nanoscale structure, size and quantum confinement effects become dominant, altering the properties of the material. Materials with nanoscale curved geometries, such as rolled-up nanomembranes and zigzag-shaped nanowires, have recently been found to exhibit a number of intriguing electronic and magnetic properties due to shape-driven modifications of charge motion or confinement effects. Local strain generated by curvature can also lead to changes in material properties due to electromechanical coupling. Here we review the development of electronic materials with nanoscale curved geometries. We examine the origin of shape-, confinement- and strain-induced effects and explore how to exploit these in electronic, spintronic and superconducting devices. We also consider the methods required to synthesize and characterize curvilinear nanostructures, and highlight key areas for the future development of curved electronics.

The growth of electronics and computer science in the last years has brought humans and machines closer than ever before. As this trend continues, new kinds of human-machine interactions are needed in a hyperconnected world. A key element for these interactions is flexible electronics, which attempts to seamlessly link living and artificial entities using electronic skins (e-skins). E-skins merge the functionality of standard electronics with the soft, stretchable, and biocompatible qualities of human skin or tissue. So far, the focus has been to reproduce the traditional functions associated with human skin, such as, temperature, pressure, and chemical detection. New developments have also introduced nonstandard sensing capabilities like magnetic field detection, to give birth to the field of magnetosensitive e-skins. Adding a supplementary information channel—an electronic sixth sense—could trigger new applications in the fields of cognitive psychology and human-machine interactions. Here, we review recent advances in magnetosensitive e-skins, which utilize the full interaction potential of the magnetic field vector to detect position, orientation, and mechanical stimuli. These magnetosensitive e-skins open exciting possibilities for touchless and gestural control in virtual and augmented reality, sensory substitution, and multimodal sensing; beyond the limitations of optics-based systems.

We have studied thermally driven magnon spin transport (spin Seebeck e_ect, SSE) in heterostructures of antiferromagnetic Cr2O3 and Pt at low temperatures. Monitoring the amplitude of the local and nonlocal SSE signals as a function of temperature, we found that both decrease with increasing temperature and disappear above 100 K and 20 K, respectively. Additionally, both SSE signals show a tendency to saturate at low temperatures. The nonlocal SSE signal decays exponentially for intermediate injector-detector separation, consistent with magnon spin current transport in the relaxation regime. We estimate the magnon relaxation length of our Cr2O3 films to be around 500 nm at 3 K. This short magnon relaxation length along with the strong temperature dependence of the SSE signal indicates that temperature-dependent inelastic magnon scattering processes play an important role in the intermediate range magnon transport. Our observation is relevant to low-dissipation antiferromagnetic magnon memory and logic devices involving thermal magnon generation and transport.

In rare-earth compounds with valence fluctuation, the proximity of the 4f level to the Fermi energy leads to instabilities of the charge configuration and the magnetic moment. For Eubased valence fluctuation materials, the intra-atomic interactions, may play an important role to affect the magnetic ground state since the multielectron configurations 4f6 (Eu3+, L=3, S=3 and J=0) and 4f7 (Eu2+, L=0, S=7/2 and J=7/2) which are dominantly defining the macroscopic magnetic properties. Here, we provide direct experimental evidence for an induced magnetic polarization of the Eu3+ atomic shell with J=0, due to intra-atomic exchange and spin-orbital coupling interactions with Eu2+ atomic shell. By applying external pressure, a transition from antiferromagnetic to a fluctuating behavior in a EuNiGe3 single crystals is probed by element- and orbital-specific x-ray absorption spectroscopy and x-ray magnetic circular dichroism at Eu L2-edge. The enhanced mixing of Eu 4f and 5d electronic orbitals under pressure leads to a monotonic increase of the mean valence of Eu ions, with the onset of a thermally fluctuating state at 30 GPa. Magnetic polarization is observed for both valence states of Eu2+ and Eu3+ across the entire pressure range, with a clear occurrence of an electronic phase transition revealed by a linewidth change of the Eu3+ resonance at 30 GPa. The anomalous magnetic order is discussed in terms of a homogeneous intermediate valence state where frustrated Dzyaloshinskii-Moriya couplings are enhanced by the onset of spin-orbital interaction and engender a chiral spin-liquid-like precursor.

Tetherless nano/-micromotors powered by chemical reactions and/or external fields generate motive forces and perform biomedical tasks, such as delivery of cargo payloads, minimally-invasive surgery, deactivation of pathogens and isolation of cancer cells. Micromotors can significantly expand short-range dynamic responses of passive biomedical micro-carriers, however, several major challenges, including biocompatibility, biodegradability, biochemical reactions as a fuel, deep tissue imaging methods must be addressed before micromotors can be translated into clinical uses. Nanozymes are nanomaterials that display enzyme-like characteristics (e.g., catalase, oxidase, peroxidase, superoxide dismutase) and represent a potentially revolutionary way to interconnect catalytic reactions, micromotors and biological systems. Today, a convergence of nanozymes and micromotors with microfluidics can lead to a paradigm shift in the fabrication of micro/-emulsions, drops, capsules and bubbles in reasonable quantities, encapsulation of sub-systems and shell-core engineering with desired tuneable biological, physical, chemical and mechanical properties. Additional functionalization methods, such as Layer-by-Layer assembly, can be used as a basis for multimodality of micro/-bubbles and capsules with a combination of ultrasound, optoacoustic, fluorescent, magnetic resonance imaging modalities, and desired surface properties. This review aims are to discuss recent achievements, challenges, and opportunities of micromotors, nanozymes, and microfluidics, which originated as separate disciplines and currently can be combined towards development of advanced Micro-Bio-Chemo-Mechanical-Systems for diverse bio-applications.

Utilizing the magnetic interactions between microparticle building blocks allows creating long‐range ordered structures and constructing smart multifunctional systems at different scales. The elaborate control over the inter‐particle magnetic coupling interaction is entailed to unlock new magnetoactuation functionalities. Herein, dimer‐type microstructures consisting of a pair of magnetic emulsions with tailorable dimension and magnetic coupling strength are fabricated using a microfluidic emulsion‐templated assembly approach. The magnetite nanoparticles dispersed in vinylbenzene monomers are partitioned into a pair of emulsions with conserved volume, which are wrapped by an aqueous hydrogel shell and finally polymerized to form discrete structures. Tunable synchronous–asynchronous rotation over 60 dB is unlocked in magnetic dimers, which is shown to be dependent on the magnetic moments induced. This leads to a new class of magnetic actuators for the parallelized assay of distinctive virus DNAs and the dynamic optical evaluation of 3D cell cultures. The work suggests a new perspective to design smart multifunctional microstructures and devices by exploring their natural variance in magnetic coupling.

Polymer microspheres can be fluorescently-coded for multiplexing molecular analysis, but their usage has been limited by the fluorescent quenching and bleaching and crowded spectral domain with issues of cross-talks and background interference. Each bioassay step of mixing and separation of analytes and reagents require off-line particle handling procedures. Here, we report stray magnetic fields can code and decode a collection of hierarchically-assembled beads. By the microfluidic assembling of mesoscopic superparamagnetic cores, diverse and non-volatile stray magnetic field response can be built in the series of microscopic spheres, dumbbells, pears, chains and triangles. Remarkably, the set of stray magnetic field fingerprints are readily discerned by a compact giant magnetoresistance sensor for parallelised screening of multiple distinctive pathogenic DNAs. This opens up the magneto-multiplexing opportunity and could enable streamlined assays to incorporate magneto-mixing, washing, enrichment and separation of analytes.
This strategy therefore suggests a potential point-of-care testing solution for efficient kinetic assay.

Materials with strong perpendicular magnetic anisotropy (PMA) are fundamentally appealing and also relevant for numerous applications especially reconsidering their practical relevance for the enhancement of the energy product for thin film based permanent magnets and realization of energy efficient and miniaturized spintronic devices. In contrast to materials exhibiting PMA due to surface anisotropy, these applications would benefit from thin films where PMA stems from a strong uniaxial magnetocrystalline anisotropy (Ku). In this regard, magnetic thin films with chemically ordered L10 structure, representing alternation of A and B atomic planes along the c direction, are considered as most promising due to the high Ku values and finely tunable magnetic properties. Typical representatives of L10 structures are ordered binary phases, e.g. FePt, FePd, MnAl, MnGa, or NiFe, etc. phases. In the case when the c axes of the L10 structure is normal to the film plane, remarkably strong PMA could can be achieved. Another important property of L10 structures is their thermodynamic stability providing resistance of corresponding devices against thermal processing. Here, we will review the application prospects of L10 ordered magnetic thin films for spintronic and permanent magnet technologies.

In the point-projection hard x-ray radiography of dense matter, for example, an inertial confinement fusion implosion capsule at stagnation time, a picosecond laser driven gold microwire is used to produce a short pulse point, bremsstrahlung hard x-ray source. The microwire was held by a low-Z CH thin substrate commonly used to promote experimental performance. We explored the influence of the low-Z thin substrate on the microwire bremsstrahlung hard x-ray source via particle-in-cell and Monte Carlo simulations. It was shown that both of the microwires, with or without the low-Z thin substrate, could emit more intense hard x-ray radiation than the radiator buried in the equal-density substrate, which benefited from efficient electron recirculation. The freestanding microwire exhibited further enhanced electron recirculation compared to that with the low-Z thin substrate, while the increased hot electrons were only present for the energetic electrons of >1 MeV. Thus, the freestanding microwire could produce significantly more intense MeV gamma x-ray emission with respect to that with the substrate, but an ignorable increment was exhibited at the softer x-ray emission of 10–200 keV. These results provided valuable insights into the design of backlighter targets in point-projection x-ray radiography, such as a freestanding microwire being preferred in MeV gamma-ray radiography, while the microwire with the CH thin substrate could be used in the 10–200 keV hard x-ray Compton radiography of an implosion capsule.

This talk present recent performance and scaling results of Particle-in-Cell code PIConGPU on the Summit supercomputer. PIConGPU is an open-source plasma simulation code for the Exascale era. It implements a wide range of core Particle-in-Cell numerical schemes and extensions, in-situ diagnostics, and high-performance I/O. Using single-source programming approach powered by alpaka library, PIConGPU runs on a variety of modern hardware, including both CPUs and GPUs. We demonstrate that it scales from a single workstation up to the full Summit supercomputer.

PIConGPU is an open-source Particle-in-Cell simulation code for the Exascale era. It implements a wide range of core Particle-in-Cell numerical schemes and extensions, in-situ diagnostics, and high-performance I/O. With a single source code base, PIConGPU runs on a variety of modern hardware, including both CPUs and GPUs, and scales from a single workstation up to the largest supercomputers. Following up the two recent talks concerning physical and numerical features of PIConGPU, this talk will focus on the computer science and software design aspects of the code and the underlying software stack. The talk concerns PIConGPU core data structures, typical patterns of parallel processing, and software design approach to enable efficient and scalable single-source implementation. It also presents performance and scaling results on the Summit supercomputer.

Alpaka (Abstraction Library for Parallel Kernel Acceleration) provides a library and tools for programming compute accelerators on a device agnostic level. This online tutorial will give an introduction to Alpaka combined with online exercises.

alpaka is a header-only C++ library for developing portable high-performance programs. Much like SYCL, it aims to abstract away the differences between accelerator types and vendors. In 2019 an experimental SYCL backend was developed in order to target FPGAs. In my talk I will focus on the challenges I faced during the SYCL backend development as well as conceptual differences between SYCL and other heterogeneous programming platforms.

The adenosine A2A receptor (A2AR) has emerged as a potential non-dopaminergic target for the treatment of Parkinson’s disease and thus, the non-invasive imaging with positron emission tomography (PET) is of utmost importance to monitor the receptor expression and occupancy during an A2AR-tailored therapy. Aiming at the development of a PET radiotracer, we herein report the design of a series of novel fluorinated analogs based on the structure of the A2AR antagonist tozadenant, and the preclinical evaluation of [18F]TOZ1. Autoradiography proved A2AR-specific in vitro binding of [18F]TOZ1 to striatum of mouse and pig brain. Investigations of the metabolic stability in mice revealed parent fractions of more than 76% and 92% of total activity in plasma and brain samples, respectively. Dynamic PET/magnetic resonance imaging (MRI) studies in mice revealed a brain uptake but no A2AR-specific in vivo binding.

The adenosine A2A receptor (A2AR) represents a potential therapeutic target for neurodegenerative diseases. Aiming at the development of a positron emission tomography (PET) radiotracer to monitor changes of receptor density and/or occupancy during the A2AR-tailored therapy, we designed a library of fluorinated analogs based on a recently published lead compound. Among those, the highly affine 4-fluorobenzyl derivate (PPY1; Ki(hA2AR) = 5.3 nM) and the 2-fluorobenzyl derivate (PPY2; Ki(hA2AR) = 2.1 nM) were chosen for 18F-labeling via an alcohol-enhanced copper-mediated procedure starting from the corresponding boronic acid pinacol ester precursors. Investigations of the metabolic stability of [18F]PPY1 and [18F]PPY2 in CD-1 mice by radio-HPLC analysis revealed parent fractions of more than 76% of total activity in the brain. Specific binding of [18F]PPY2 on mice brain slices was demonstrated by in vitro autoradiography. In vivo PET/magnetic resonance imaging (MRI) studies in CD-1 mice revealed a reasonable high initial brain uptake for both radiotracers, followed by a fast clearance.

Purpose: The adenosine A2A receptor has emerged as a therapeutic target for multiple diseases, and thus the non-invasive imaging of the expression or occupancy of the A2A receptor has potential to contribute to diagnosis and drug development. We aimed at the development of a metabolically stable A2A receptor radiotracer and report herein the preclinical evaluation of [18F]FLUDA, a deuterated isotopologue of [18F]FESCH.
Methods: [18F]FLUDA was synthesized by a two-step one-pot approach and evaluated in vitro by autoradiographic studies as well as in vivo by metabolism and dynamic PET/MRI studies in mice and piglets under baseline and blocking conditions. A single-dose toxicity study was performed in rats.
Results: [18F]FLUDA was obtained with a radiochemical yield of 19% and molar activities of 72 180 GBq/µmol. Autoradiography proved A2A receptor-specific accumulation of [18F]FLUDA in the striatum of mouse and pig brain. In vivo evaluation in mice revealed improved stability of [18F]FLUDA compared to [18F]FESCH, resulting in the absence of brain-penetrant radiometabolites. Furthermore, the radiometabolites detected in piglets are expected to have a low tendency for brain penetration. PET/MRI studies confirmed high specific binding of [18F]FLUDA towards striatal A2A receptor with a maximum specific-to-non-specific binding ratio in mice of 8.3. The toxicity study revealed no adverse effects of FLUDA up to 30 µg/kg, ~ 4000-fold the dose applied in human PET studies using [18F]FLUDA.
Conclusions: The new radiotracer [18F]FLUDA is suitable to detect the availability of the A2A receptor in the brain with high target specificity. It is regarded ready for human application.

Water adsorption on transition metal dichalcogenides and other 2D materials is generally governed by weak van der Waals interactions. This results in a hydrophobic character of the basal planes, and defects may play a significant role in water adsorption and water cluster nucleation. However, there is a lack of detailed experimental investigations on water adsorption on defective 2D materials. Here, by combining low-temperature scanning tunneling microscopy (STM) experiments and density functional theory (DFT) calculations, we study in that context the well-defined mirror twin boundary (MTB) networks separating mirror-grains in 2D MoSe2. These MTBs are dangling bond-free extended crystal modifications with metallic electronic states embedded in the 2D semiconducting matrix of MoSe2. Our DFT calculations indicate that molecular water also interacts similarly weak with these MTBs as with the defect-free basal plane of MoSe2. However, in low temperature STM experiments, nanoscopic water structures are observed that selectively decorate the MTB network. This localized adsorption of water is facilitated by functionalization of the MTBs by hydroxyls formed by dissociated water. Hydroxyls may form by dissociating of water at undercoordinated defects or adsorbing of radicals from the gas phase in the UHV chamber. Our DFT analysis indicates that the metallic MTBs adsorb these radicals much stronger than on the basal plane due to charge transfer from the metallic states into the molecular orbitals of the OH groups. Once the MTBs are functionalized with hydroxyls, molecular water can attach to them, forming water channels along the MTBs. This study demonstrates the role metallic defect states play in the adsorption of water even in the absence of unsaturated bonds that have been so far considered to be crucial for adsorption of hydroxyls or water.

Optically active spin centers associated with atomic-scale defects in SiC are promising candidates for quantum technology owing to their outstanding optical and spin properties. Photoluminescence as a mature optical investigating tool is widely used for the identification of spin defects and exploration of their properties. However, in the case of spectrally overlapped contributions from different types of defects, the traditional photoluminescence measurement cannot be used to separately obtain their optical and vibrational properties, such as the local phonon energy and the Debye-Waller factor. Here, we apply spin resonant microwave-assisted spectroscopy to investigate the optical and vibrational properties of silicon vacancies in 6H-SiC and divacancies in 4H- and 6H-SiC. We isolate contributions from each type of defect, investigate their local vibrational modes and obtain the Debye-Waller factor. This work proves that microwave-assisted spectroscopy is a suitable tool for the investigation of optical and vibrational properties of a large variety of spin defects.

Remote sensing is commonly defined either as the data acquisition about an object or a process at a distance or the scanning of the Earth by satellite or high-flying aircraft. In the present article, we will consider all the relevant sensors and techniques that allow the imaging, measurement and monitoring of the Earth’s surface from a distance greater than 10 m.

CoFeB alloys are highly relevant materials for spintronic applications. In this work, the crystallization of CoFeB alloys triggered by thermal annealing was investigated by x-ray diffraction techniques and scanningelectron microscopy, as well as spectroscopic ellipsometry and magneto-optical Kerr effect spectroscopy forannealing temperatures ranging from 300 to 600◦C. The transformation of∼100-nm-thick CoxFe(80−x)B20filmsfrom amorphous to polycrystalline was revealed by the sharpening of spectral features observed in optical andmagneto-optical dielectric functions spectra. The influence of B on the dielectric function was assessed bothexperimentally and by optical modeling. By analyzing the Drude component of the optical dielectric function,a consistent trend between the charge-carrier scattering time/resistivity and the annealing temperature wasobserved, in agreement with the electrical investigations by means of the four-point-probe method.

He+-ion irradiation enables controlled postdeposition modification of layered magnetic thin-film sys-tems. The degree of modification and its depth profile can be tuned by the irradiation dose and energy.Here, we use magnetometry and magnetic force microscopy to explore the impact of gentle He+-ion irra-diation on synthetic antiferromagnets, consisting of ferromagnetic Co/Pt multilayers with perpendicularmagnetic anisotropy, which are antiferromagnetically (AF) coupled via Ru interlayers. This system showsa rich variety of magnetic domain patterns due to the strong competition between different magnetic ener-gies. We show that AF interlayer exchange and perpendicular interface anisotropy energy are graduallyreduced by the ion irradiation while the demagnetization energy is mainly preserved, which thus results inmultiple successive magnetic-phase transitions.

We study the spin-wave dynamics in nanoscale antidot lattices based on Co/Pd multilayers with perpendicularmagnetic anisotropy. Using time-resolved magneto-optical Kerr effect measurements we demonstrate that thevariation of the antidot shape introduces significant change in the spin-wave spectra, especially in the lowerfrequency range. By employing micromagnetic simulations we show that additional peaks observed in themeasured spectra are related to narrow shell regions around the antidots, where in-plane domain structures areformed. This is because the magnetic anisotropy in these regions is reduced due to the Ga(+)ion irradiation duringthe focused ion beam milling process of the antidot fabrication. The results point at possibilities for exploitationof localized spin waves in out-of-plane magnetized thin films, which are easily tunable and suitable for magnonicapplications.

Der Vortrag gibt einen Überblick über die Simulation von Flüssigmetallbatterien.

Due to the constantly growing utilization of wind and solar energy, the demand for technologies for temporal and spatial decoupling of energy provision and consumption is steadily increasing. The application of proton-conducting high temperature solid oxide electrolysis cells (p-SOECs) has been a main concern in recent research activities since they offer an environmentally friendly and efficient technique for the single step conversion of excess energy from renewables into pure hydrogen. As renewables occur intermittently, SOEC designs and all employed materials have to be capable of withstanding large electrical transients and therefore harsh operating conditions. Tubular SOEC designs are characterized by inherent advantages: They offer rapid start-up capabilities, a high resistance to thermal stresses and are usable for high-pressure application. Combined with suitable downstream syntheses units (e.g. methanol synthesis), innovative power-to-X systems can be provided for the production of valuable liquid or gaseous chemicals from H2 and anthropogenic CO2 as a chemical storage of excess energy. This work aims to modify the existing dynamic system model and extend it with regard to specific peripheral system components (compressors, evaporators, heat exchangers, pre-heaters, super-heaters and condensers). These peripheral components are ought to be modelled as simplified dynamic 0D black-box models and basic design specifications (dimensions, power demands, etc.) are to be determined with the given system parameters for the conditioning of all employed reactant, intermediate and product gas streams. Furthermore, the extended system model should be used to evaluate a given heat integration concept, which is fully utilizing all waste, intermediate and product gas streams of the power-to-methanol system. The overall system efficiency is to be determined for different load cases during intermittent operation.

In einem urbanen Industrieareal wird die zukunftsweisende Technologie der solargespeisten Wasserstofferzeugung entwickelt. Dabei wird ein PEM-Elektrolyseur (Proton-Exchange Membrane) mit einer Leistung von 1 MW an eine auf den Industriedächern geplante Photovoltaikanlage mit einer Peakleistung von etwa 4,5 MWp angeschlossen. Somit soll eine Grundauslastung der Elektrolyse-anlage innerhalb der Erzeugungsregion erreicht werden.

An der Hochschule Esslingen wird an Regelungsstrategien für hybride elektrische Energiesysteme (HEES) geforscht. Im Rahmen der Diplomarbeit soll eine robuste Regelung für eine Elektrolyse-Anlage in MATLAB/Simulink am Beispiel des o.g. Projekts inklusive Photovoltaikanlage und einer Batterie sowie Wasserstofftanks implementiert werden. Dabei sollen Stör- und Einflussgrößen (Witterungseinflüsse etc.) beschrieben und Betriebspunktverschiebungen berücksichtigt werden.

Geologic mapping in arctic regions faces demanding challenges, from accessibility to weather, light and infrastructure conditions. Field expeditions need to cover substantial area, and mostly are supported by satellite and airborne data. While named methods offer large-scaled insights, they often lack the required resolution for precise ground investigations. The rise of unmanned aerial systems (UAS) as new state-of-the-art platform in geoscience provides the means needed to close that scale gap.

Fieldwork within the frame of the EIT project MULSEDRO focused on the Paleocene flood basalt province of Disko Island (West Greenland). On the example of the Qullissat area, we demonstrate how UAS can bring new insights into strategies for magmatic Ni-PGE exploration in the area. Mineralization is associated to basalt sills of the Asuk Member, emplaced locally in coal-bearing cretaceous sandstones. We conducted photogrammetric outcrop modelling, interpretation of orthoimagery, multi- and hyperspectral based lithological classification and analysis of magnetic data. While magnetics give the location, orientation and subsurface extension of the basaltic sills, spectral imaging, in particular with focus on the iron absorption feature, reveals mineral proxies due to sulphide weathering. A total of 216 line-km for magnetics and 18.5 km2 of multi- and hyperspectral data was covered.

First results show that integration of drone-borne spectroscopic and magnetic data highlights potential local mineralization. Based on our results, possible indications for mineralization are linear features in the first vertical derivative of the magnetic data and specific iron absorptions in the spectral data. Resulting maps are validated using handheld spectroscopy, ground magnetics, susceptibility measurements, combined with geochemistry and mineralogy of rock samples examined in the laboratory. Conclusively, the study solidifies UAS as highly valuable tool for exploration.

Over the last ten years, unmanned aerial vehicles (UAV), commonly called drones, and related systems have rapidly developed. Everyman’s drones are available on store shelves to take photos and videos of holidays, one’s own house and garden, and for many other private reasons. With the general advances in robotics and digitalization, drones have also been increasingly utilized for various commercial applications. This trend can additionally be seen in geosciences. A key question arising for many geoscience applications is whether drones could be used as platforms to carry out more demanding surveys with remote sensing and geophysical sensors that have traditionally been mounted on aircraft or have been carried by workers on the ground. The answer is yes, drones can be used, although the integration of such sensors on drones is not straightforward. The methods themselves are typically well developed, but drones as an aspiring platform pose challenges for operating sensors and performing measurement procedures in proper ways. In particular, the need for small and lightweight sensors with a low power consumption for UAV platforms plays an important role, because they allow flexible low-cost measurements to be performed without a long preparation phase. Another aspect is that legislation, which varies from country to country, affects drone operations. Therefore, it is often difficult in practice for drone-operating companies to provide international services, and it is generally easier to obtain permission for small drones flying at low altitude only. To find a remedy for this, a common EU-wide regulation is currently in preparation. EU drone regulation (EU) 2019/947 defines the rules and procedures for different types of drone operations and is intended to be applied according to the transition period of the regulation on 31 December 2020.

In recent years, many groups all over the world – both in academia and industry – have worked on the integration of various sensor types on drones that are relevant for geological mapping and mineral exploration. Drone-borne survey systems are considered to be especially appropriate for small to medium-sized surveys that are smaller than those carried out with traditional aircraft, but larger than ground-based surveys. The goal of many companies is to offer drone-based services for this market niche.

Combining both spectral and spatial information with enhanced resolution provides not only elaborated qualitative information on surfacing mineralogy but also mineral interactions of abundance, mixture, and structure. This enhancement in the resolutions helps geomineralogic features such as small intrusions and mineralization become detectable. In this paper, we investigate the potential of the resolution enhancement of hyperspectral images (HSIs) with the guidance of RGB images for mineral mapping. In more detail, a novel resolution enhancement method is proposed based on component decomposition. Inspired by the principle of the intrinsic image decomposition (IID) model, the HSI is viewed as the combination of a reflectance component and an illumination component. Based on this idea, the proposed method is comprised of several steps. First, the RGB image is transformed into the luminance component, blue-difference and red-difference chroma components (YCbCr), and the luminance channel is considered as the illumination component of the HSI with an ideal high spatial resolution. Then, the reflectance component of the ideal HSI is estimated with the downsampled HSI image and the downsampled luminance channel. Finally, the HSI with high resolution can be reconstructed by utilizing the obtained illumination and the reflectance components. Experimental results verify that the fused results can successfully achieve mineral mapping, producing better results qualitatively and quantitatively over single sensor data.

Important technological developments of our time such as the energy transition or digitalization require new materials and more efficient manufacturing processes. The processes of ultra-short time annealing such as flash lamp annealing (FLA) and laser annealing have the potential to make an important contribution. During such processes high temperatures are applied for very short times (nano- to milliseconds), so that only near surface regions of the material are exposed to the maximum temperature. Compared to conventional thermal treatments, ultra-short time annealing enables energy and process time savings, the use of temperature-sensitive substrates, and the synthesis of new materials in thermal non-equilibrium.
The talk is divided into two parts. After an introduction, the first part discusses the main features of FLA in comparison with other short time annealing techniques, namely rapid thermal annealing and laser annealing. Special focus is set on temperature and its determination, as this is a complex and challenging issue on short time scales. The second part deals with various examples of applying FLA to materials, ranging from semiconductor applications over printed electronics to energy materials for batteries.

Der Vortrag stellt das Helmholtz Innovation Lab blitzlab vor und geht danach auf die Blitzlampenausheilung als ein innovatives Verfahren zur thermischen Behandlung von Materialien und Werkstoffen ein.

Silicon substrates with stripe-patterned surface-near electrostatic forces (SNEF) were prepared by local implantation of boron ions into n-type silicon wafers and of phosphorus ions into p-type silicon wafers in a stripe pattern of 12 µm periodicity. The dependence of SNEF on the concentration of implanted ions, post-annealing conditions, and generation of charge carriers under illumination was investigated by measuring the 1st and 2nd harmonics of the SNEF in the dark and under illumination using Kelvin probe force microscopy. The self-organized immobilization of biomolecules on silicon regions with positive charges occupying the interface states between the silicon and the native SiO2 has been demonstrated for the negatively charged single stranded deoxyribonucleic acid (DNA) and bovine serum albumin (BSA) proteins.

Small molecules with no or little charge are considered to have minimal impact on signals measured by field effect transistor (FET) sensors. This fact typically excludes steroids from the family of analytes, detected by FETs. We present a portable multiplexed platform based on an array of nanowire sensors for label-free monitoring of daytime levels of the stress hormone cortisol in saliva samples, obtained from multiple donors. To achieve an effective quantification of the cortisol with FETs, we rely on the specific DNA aptamer sequences as receptors, bringing the complex “target-receptor” closer to the nanowire surface. Upon binding, cortisol induces conformational changes of negatively charged aptamers, wrapping it into a close proximity to the silicon nanowires, to efficiently modulate their surface potential. Thus, the sensors allow for a real-time assessment of the steroid biomarkers at low nanomolar concentration. The measurement platform is designed in a building-block concept, consisting of a modular measuring unit and a customizable biochip board, and operates using a complementary metal-oxide-semiconductor-integrated multiplexer. The platform is capable of continuous and simultaneous measurement of samples from multiple patients. Cortisol levels detected with the presented platform agreed well with the results obtained with a commercial high-sensitivity immunoassay

Laser-driven plasmas – generated in the interaction of a focused high power laser pulse with a solid surface – can sustain electrical field strengths of ~TV/m, allowing for compact and efficient particle acceleration of proton pulses with up to 100 MeV kinetic energies on ~µm spatial scales. The particle pulses feature a picosecond pulse duration at the source and extremely high pulse currents and dose, making them an ideal tool for laboratory-scale high dose-rate radiobiology research.
At the Draco Petawatt laser source, we have established and now successfully operate a source-to-sample just meter-scale setup for high dose-rate in vivo radiobiological studies based on a laser-driven proton source and a compact, versatile pulsed high-field magnetic beamline.
The setup development and preliminary experimental results will be presented, also intending to foster collaborations within HZDR for a wide range of high dose-rate applications.

Semantic interpretation of the 3D scene is one of the most challenging problems in point cloud processing, which also deems as an essential task in a wide variety of point cloud applications. The core task of semantic interpretation is semantic labeling, namely, obtaining a unique semantic label for each point in the point cloud. Despite several reported approaches, semantic labeling continues to be a challenge owing to the complexity of scenes, objects of various scales, and the non-homogeneity of unevenly distributed points. In this paper, we propose a novel method for obtaining semantic labels of airborne laser scanning (ALS) point clouds involving the embedding of local context information for each point with multi-scale deep learning, nonlinear manifold learning for feature dimension reduction, and global graph-based optimization for refining the classification results. Specifically, we address the tasks of learning discriminative features and global labeling smoothing. The key contribution of our study is threefold. First, a hierarchical data augmentation strategy is applied to enhance the learning of deep features based on the PointNet++ network and simultaneously eliminate the artifacts caused by division and sampling while dealing with large-scale datasets. Subsequently, the learned hierarchical deep features are globally optimized and embedded into a low-dimensional space with a nonlinear manifold-based joint learning method with the removal of redundant and disturbing information. Finally, a graph-structured optimization based on the Markov random fields algorithm is performed to achieve global optimization of the initial classification results that are obtained using the embedded deep features by constructing a weighted indirect graph and solving the optimization problem with graph-cuts. We conducted thorough experiments on ALS point cloud datasets to assess the performance of our framework. Results indicate that compared to other commonly used advanced classification methods, our method can achieve high classification accuracy. The overall accuracy (OA) of our approach on the ISPRS benchmark dataset can scale up to 83.2% for classifying nine semantic classes, thereby outperforming other compared point-based strategies. Additionally, we evaluated our framework on a selected portion of the AHN3 dataset, which provided OA up to 91.2%.

Sun glint in hyperspectral images (HSIs) leads to undesirable spectral interference, which severely affects subsequent image interpretation, such as environmental monitoring of coastal areas. Sun glint removal methods aim to recover a high quality image without sun glint from the original image. Most methods depend on an assumption that the near infrared band is strongly absorbed by water. However, this assumption is not always reliable because the infrared radiation in shallow or turbid water can be reflected back by the seabed or sediment, rather than being fully absorbed. Therefore, the reflected infrared radiation still contains sun glint and these methods cannot sufficiently remove sun glint from HSIs. To address this problem, a texture-aware total variation (TATV)-based method is proposed to remove sun glint from HSIs. The original HSI first is formulated as a desired clean image and a sun glint image. Then, in order to remove the sun glint, we propose a variational model where the different spectral characteristics of sun glint and other surrounding materials are considered. Specifically, we propose a texture-aware total variation regularized method to heavily penalize the variation of the sun glint areas. Experiments performed on simulated and real data sets demonstrate that our method can greatly outperform other state-of-the-art methods in removing sun glint.

This paper reviews the most important information fusion data-driven algorithms based on Machine Learning (ML) techniques for problems in Earth observation. Nowadays we observe and model the Earth with a wealth of observations, from a plethora of different sensors, measuring states, fluxes, processes and variables, at unprecedented spatial and temporal resolutions. Earth observation is well equipped with remote sensing systems, mounted on satellites and airborne platforms, but it also involves in-situ observations, numerical models and social media data streams, among other data sources. Data-driven approaches, and ML techniques in particular, are the natural choice to extract significant information from this data deluge. This paper produces a thorough review of the latest work on information fusion for Earth observation, with a practical intention, not only focusing on describing the most relevant previous works in the field, but also the most important Earth observation applications where ML information fusion has obtained significant results. We also review some of the most currently used data sets, models and sources for Earth observation problems, describing their importance and how to obtain the data when needed. Finally, we illustrate the application of ML data fusion with a representative set of case studies, as well as we discuss and outlook the near future of the field.

With the development of convolutional neural networks (CNNs), the semantic understanding of remote sensing (RS) scenes has been significantly improved based on their prominent feature encoding capabilities. While many existing deep-learning models focus on designing different architectures, only a few works in the RS field have focused on investigating the performance of the learned feature embeddings and the associated metric space. In particular, two main loss functions have been exploited: the contrastive and the triplet loss. However, the straightforward application of these techniques to RS images may not be optimal in order to capture their neighborhood structures in the metric space due to the insufficient sampling of image pairs or triplets during the training stage and to the inherent semantic complexity of remotely sensed data. To solve these problems, we propose a new deep metric learning approach, which overcomes the limitation on the class discrimination by means of two different components: 1) scalable neighborhood component analysis (SNCA) that aims at discovering the neighborhood structure in the metric space and 2) the cross-entropy loss that aims at preserving the class discrimination capability based on the learned class prototypes. Moreover, in order to preserve feature consistency among all the minibatches during training, a novel optimization mechanism based on momentum update is introduced for minimizing the proposed loss. An extensive experimental comparison (using several state-of-the-art models and two different benchmark data sets) has been conducted to validate the effectiveness of the proposed method from different perspectives, including: 1) classification; 2) clustering; and 3) image retrieval. The related codes of this article will be made publicly available for reproducible research by the community.

Superresolution (SR) has provided an effective solution to the increasing need for high-resolution images in remote sensing applications. Among various SR methods, deep learning-based SR (DLSR) has made a significant breakthrough. However, supervised DLSR methods require a considerable amount of training data, which is hardly available in the remote sensing field. To address this issue, some research works have recently proposed and revealed the capability of deep learning in unsupervised SR. This article presents an efficient unsupervised SR (EUSR) deep learning model using dense skip connections, which boosts the reconstruction performance in parallel with the reduction of computational burden. To do this, several blocks containing densely connected convolutional layers are implemented to increase the depth of the model. Some skip connections also concatenate feature maps of different blocks to enable better SR performance. Moreover, a bottle-neck block abstracts the feature maps in fewer feature maps to remarkably reduce the computational burden. According to our experiments, the proposed EUSR leads to better results than the state-of-the-art DLSR method in terms of reconstruction quality with less computational burden. Furthermore, results indicate that the EUSR is more robust than its rival in dealing with images of different classes and larger sizes.

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and these models are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models need to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to susceptible–infected–recovered (SIR) and susceptible-exposed-infectious-removed (SEIR) models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP; and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior across nations, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. This paper further suggests that a genuine novelty in outbreak prediction can be realized by integrating machine learning and SEIR models.

Several epidemiological models are being used around the world to project the number of infected individuals and the mortality rates of the COVID-19 outbreak. Advancing accurate prediction models is of utmost importance to take proper actions. Due to the lack of essential data and uncertainty, the epidemiological models have been challenged regarding the delivery of higher accuracy for long-term prediction. As an alternative to the susceptible-infected-resistant (SIR)-based models, this study proposes a hybrid machine learning approach to predict the COVID-19, and we exemplify its potential using data from Hungary. The hybrid machine learning methods of adaptive network-based fuzzy inference system (ANFIS) and multi-layered perceptron-imperialist competitive algorithm (MLP-ICA) are proposed to predict time series of infected individuals and mortality rate. The models predict that by late May, the outbreak and the total morality will drop substantially. The validation is performed for 9 days with promising results, which confirms the model accuracy. It is expected that the model maintains its accuracy as long as no significant interruption occurs. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research.

The amount of remote sensing and ancillary datasets captured by diverse airborne and spaceborne sensors has been tremendously increased, which opens up the possibility of utilizing multimodal datasets to improve the performance of processing approaches with respect to the application at hand. However, developing a generic framework with high generalization capability that can effectively fuse diverse datasets is a challenging task since the current approaches are usually only applicable to two specific sensors for data fusion. In this paper, we propose an accurate fusion-based technique called SubFus with capability to integrate diverse remote sensing data for land cover classification. Here, we assume that a high dimensional multisensor dataset can be represented fused features that live in a lower-dimensional space. The proposed classification methodology includes three main stages. First, spatial information is extracted by using spatial filters (i.e., morphology filters). Then, a novel low- rank minimization problem is proposed to represent the multisensor datasets in subspaces using fused features. The fused features in the lower-dimensional subspace are estimated using a novel iterative algorithm based on the alternative direction method of multipliers. Third, the final classification map is produced by applying a supervised spectral classifier (i.e., random forest) on the fused features. In the experiments, the proposed method is applied to a three-sensor (RGB, multispectral LiDAR, and hyperspectral images) dataset captured over the area of the University of Houston, the USA, and a two-sensor (hyperspectral and LiDAR) dataset captured over the city of Trento, Italy. The land-cover maps generated using SubFus are evaluated based on classification accuracies. Experimental results obtained by SubFus confirm considerable improvements in terms of classification accuracies compared with the other methods used in the experiments. The proposed fusion approach obtains 85.32% and 99.25% in terms of overall classification accuracy on the Houston (the training portion of the dataset distributed for the data fusion contest of 2018) and trento datasets, respectively.

Several machine-learning algorithms have been proposed for remote sensing image classification during the past two decades. Among these machine learning algorithms, Random Forest (RF) and Support Vector Machines (SVM) have drawn attention to image classification in several remote sensing applications. This article reviews RF and SVM concepts relevant to remote sensing image classification and applies a meta-analysis of 251 peer-reviewed journal papers. A database with more than 40 quantitative and qualitative fields was constructed from these reviewed papers. The meta-analysis mainly focuses on 1) the analysis regarding the general characteristics of the studies, such as geographical distribution, frequency of the papers considering time, journals, application domains, and remote sensing software packages used in the case studies, and 2) a comparative analysis regarding the performances of RF and SVM classification against various parameters, such as data type, RS applications, spatial resolution, and the number of extracted features in the feature engineering step. The challenges, recommendations, and potential directions for future research are also discussed in detail. Moreover, a summary of the results is provided to aid researchers to customize their efforts in order to achieve the most accurate results based on their thematic applications.

This paper provides a comprehensive state-of-the-art investigation of the recent advances in data science in emerging economic applications. The analysis is performed on the novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a broad and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, is used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which outperform other learning algorithms. It is further expected that the trends will converge toward the evolution of sophisticated hybrid deep learning models.

Convolutional neural network (CNN) has demonstrated to be a powerful tool for hyperspectral images (HSIs) classification. Previous CNN-based HSI classification methods only adopt the fixed-size patches to train the CNN model, and such single scale patches may not reflect the complex spatial structural information in the HSIs. In addition, although different layers of CNN can extract features of multiple scales, the traditional CNN model can only utilize features from the highest level for the classification task. These features, however, do not fully consider the strong complementary yet correlated information among different layers. To address these issues, in this paper, a multiscale densely-connected convolutional network (MS-DenseNet) framework is proposed to sufficiently exploit multiple scales information for the HSIs classification. Specifically, for each pixel, the MS-DenseNet, first, extracts its surrounding patches of multiple scales. These patches can separately constitute multiple scale training and testing samples. Within each specific scale sample, instead of using the forward convolutional layers, the MS-DenseNet adopts the dense blocks, which can connect each layer to other layers in a feed-forward fashion and thus can exploit the information among different layers for training and testing. Furthermore, since high correlations exist in patches of different scales, the MS-DenseNet introduces several dense blocks to fuse the multiscale information among different layers for the final HSI classification. Experimental results on several real HSIs demonstrate the superiority of the proposed MS-DenseNet over single scale-based CNN classification model and several well-known classification methods.

The inclusion of spatial information into spectral classifiers for fine-resolution hyperspectral imagery has led to significant improvements in terms of classification performance. The task of spectral-spatial hyperspectral image classification has remained challenging because of high intraclass spectrum variability and low interclass spectral variability. This fact has made the extraction of spatial information highly active. In this work, a novel hyperspectral image classification framework using the fusion of dual spatial information is proposed, in which the dual spatial information is built by both exploiting pre-processing feature extraction and post-processing spatial optimization. In the feature extraction stage, an adaptive texture smoothing method is proposed to construct the structural profile (SP), which makes it possible to precisely extract discriminative features from hyperspectral images. The SP extraction method is used here for the first time in the remote sensing community. Then, the extracted SP is fed into a spectral classifier. In the spatial optimization stage, a pixel-level classifier is used to obtain the class probability followed by an extended random walker-based spatial optimization technique. Finally, a decision fusion rule is utilized to fuse the class probabilities obtained by the two different stages. Experiments performed on three data sets from different scenes illustrate that the proposed method can outperform other state-of-the-art classification techniques. In addition, the proposed feature extraction method, i.e., SP, can effectively improve the discrimination between different land covers.

Temporary changes in precipitation may lead to sustained and severe drought or massive floods in different parts of the world. Knowing the variation in precipitation can effectively help the water resources decision-makers in water resources management. Large-scale circulation drivers have a considerable impact on precipitation in different parts of the world. In this research, the impact of El Niño-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), and North Atlantic Oscillation (NAO) on seasonal precipitation over Iran was investigated. For this purpose, 103 synoptic stations with at least 30 years of data were utilized. The Spearman correlation coefficient between the indices in the previous 12 months with seasonal precipitation was calculated, and the meaningful correlations were extracted. Then, the month in which each of these indices has the highest correlation with seasonal precipitation was determined. Finally, the overall amount of increase or decrease in seasonal precipitation due to each of these indices was calculated. Results indicate the Southern Oscillation Index (SOI), NAO, and PDO have the most impact on seasonal precipitation, respectively. Additionally, these indices have the highest impact on the precipitation in winter, autumn, spring, and summer, respectively. SOI has a diverse impact on winter precipitation compared to the PDO and NAO, while in the other seasons, each index has its special impact on seasonal precipitation. Generally, all indices in different phases may decrease the seasonal precipitation up to 100%. However, the seasonal precipitation may increase more than 100% in different seasons due to the impact of these indices. The results of this study can be used effectively in water resources management and especially in dam operation.

In this article, we propose an efficient and effective framework to fuse hyperspectral and light detection and ranging (LiDAR) data using two coupled convolutional neural networks (CNNs). One CNN is designed to learn spectral-spatial features from hyperspectral data, and the other one is used to capture the elevation information from LiDAR data. Both of them consist of three convolutional layers, and the last two convolutional layers are coupled together via a parameter-sharing strategy. In the fusion phase, feature-level and decision-level fusion methods are simultaneously used to integrate these heterogeneous features sufficiently. For the feature-level fusion, three different fusion strategies are evaluated, including the concatenation strategy, the maximization strategy, and the summation strategy. For the decision-level fusion, a weighted summation strategy is adopted, where the weights are determined by the classification accuracy of each output. The proposed model is evaluated on an urban data set acquired over Houston, USA, and a rural one captured over Trento, Italy. On the Houston data, our model can achieve a new record overall accuracy (OA) of 96.03%. On the Trento data, it achieves an OA of 99.12%. These results sufficiently certify the effectiveness of our proposed model.

This paper presents a technique for the fusion of multispectral LiDAR and hyperspectral data. The proposed method is based on the fusion of the features of multispectral LiDAR and hyperspectral data projected in two different subspaces. First, the spatial features are extracted from both data using morphological filters. Then, the fused features are estimated by proposing a novel constraint penalized cost function. The estimated fused features are used for the purpose of mapping. The classification accuracies obtained by applying a random forest classifier on the fused data confirm considerable improvements compared with the other methods used in the experiments.

Air pollution, and especially atmospheric particulate matter (PM), has a profound impact on human mortality and morbidity, environment, and ecological system. Accordingly, it is very relevant predicting air quality. Although the application of the machine learning (ML) models for predicting air quality parameters, such as PM concentrations, has been evaluated in previous studies, those on the spatial hazard modeling of them are very limited. Due to the high potential of the ML models, the spatial modeling of PM can help managers to identify the pollution hotspots. Accordingly, this study aims at developing new ML models, such as Random Forest (RF), Bagged Classification and Regression Trees (Bagged CART), and Mixture Discriminate Analysis (MDA) for the hazard prediction of PM10 (particles with a diameter less than 10 µm) in the Barcelona Province, Spain. According to the annual PM10 concentration in 75 stations, the healthy and unhealthy locations are determined, and a ratio 70/30 (53/22 stations) is applied for calibrating and validating the ML models to predict the most hazardous areas for PM10. In order to identify the influential variables of PM modeling, the simulated annealing (SA) feature selection method is used. Seven features, among the thirteen features, are selected as critical features. According to the results, all the three-machine learning (ML) models achieve an excellent performance (Accuracy > 87% and precision > 86%). However, the Bagged CART and RF models have the same performance and higher than the MDA model. Spatial hazard maps predicted by the three models indicate that the high hazardous areas are located in the middle of the Barcelona Province more than in the Barcelona’s Metropolitan Area.

So far, a large number of advanced techniques have been developed to enhance and extract the spatially semantic information in hyperspectral image processing and analysis. However, locally semantic change, such as scene composition, relative position between objects, spectral variability caused by illumination, atmospheric effects, and material mixture, has been less frequently investigated in modeling spatial information. Consequently, identifying the same materials from spatially different scenes or positions can be difficult. In this article, we propose a solution to address this issue by locally extracting invariant features from hyperspectral imagery (HSI) in both spatial and frequency domains, using a method called invariant attribute profiles (IAPs). IAPs extract the spatial invariant features by exploiting isotropic filter banks or convolutional kernels on HSI and spatial aggregation techniques (e.g., superpixel segmentation) in the Cartesian coordinate system. Furthermore, they model invariant behaviors (e.g., shift, rotation) by the means of a continuous histogram of oriented gradients constructed in a Fourier polar coordinate. This yields a combinatorial representation of spatial-frequency invariant features with application to HSI classification. Extensive experiments conducted on three promising hyperspectral data sets (Houston2013 and Houston2018) to demonstrate the superiority and effectiveness of the proposed IAP method in comparison with several state-of-the-art profile-related techniques. The codes will be available from the website: https://sites.google.com/view/danfeng-hong/data-code.

Multi-sensor data on the same area provide complementary information, which is helpful for improving the discrimination capability of classifiers. In this work, a novel multilevel structure extraction method is proposed to fuse multi-sensor data. This method is comprised of three steps: First, multilevel structure extraction is constructed by cascading morphological profiles and structure features, and is utilized to extract spatial information from multiple original images. Then, a low-rank model is adopted to integrate the extracted spatial information. Finally, a spectral classifier is employed to calculate class probabilities, and a maximum posteriori estimation model is used to decide the final labels. Experiments tested on three datasets including rural and urban scenes validate that the proposed approach can produce promising performance with regard to both subjective and objective qualities.

Deep metric learning has recently received special attention in the field of remote sensing (RS) scene characterization, owing to its prominent capabilities for modeling distances among RS images based on their semantic information. Most of the existing deep metric learning methods exploit pairwise and triplet losses to learn the feature embeddings with the preservation of semantic-similarity, which requires the construction of image pairs and triplets based on the supervised information (e.g., class labels). However, generating such semantic annotations becomes a completely unaffordable task in large-scale RS archives, which may eventually constrain the availability of sufficient training data for this kind of models. To address this issue, we reformulate the deep metric learning scheme in a semi-supervised manner to effectively characterize RS scenes. Specifically, we aim at learning metric spaces by utilizing the supervised information from a small number of labeled RS images and exploring the potential decision boundaries for massive sets of unlabeled aerial scenes. In order to reach this goal, a joint loss function, composed of a normalized softmax loss with margin and a high-rankness regularization term, is proposed, as well as its corresponding optimization algorithm. The conducted experiments (including different state-of-the-art methods and two benchmark RS archives) validate the effectiveness of the proposed approach for RS image classification, clustering and retrieval tasks. The codes of this paper are publicly available.

Multi-sensor remote sensing image classification has been considerably improved by deep learning feature extraction and classification networks. In this paper, we propose a novel multi-sensor fusion framework for the fusion of diverse remote sensing data sources. The novelty of this paper is grounded in three important design innovations: 1- a unique adaptation of the coupled residual networks to address multi-sensor data classification; 2- a smart auxiliary training via adjusting the loss function to address classifications with limited samples; and 3- a unique design of the residual blocks to reduce the computational complexity while preserving the discriminative characteristics of multi-sensor features. The proposed classification framework is evaluated using three different remote sensing datasets: the urban Houston university datasets (including Houston 2013 and the training portion of Houston 2018) and the rural Trento dataset. The proposed framework achieves high overall accuracies of 93.57%, 81.20%, and 98.81% on Houston 2013, the training portion of Houston 2018, and Trento datasets, respectively. Additionally, the experimental results demonstrate considerable improvements in classification accuracies compared with the existing state-of-the-art methods.

Three different crystallographic orientations of the wurtzite ZnO structure (labeled as c-plane, a-plane and m-plane) were implanted with Au + ions using various energies and fluences to form gold nanoparticles (GNPs). The ion implantation process was followed by annealing at 600°C in an oxygen atmosphere to decrease the number of unwanted defects and improve luminescence properties. With regard to our previous publications, the paper provides a summary of theoretical and experimental results, i.e., both DFT and FLUX simulations, as well as experimental results from TEM, HRTEM, RBS, RBS/C, Raman spectroscopy and photoluminescence. From the results, it follows that in the ZnO structure, implanted gold atoms are located in random interstitial positions—experimentally, the amount of interstitial gold atoms increased with increasing ion implantation fluence. During ion implantation and subsequent annealing, the metal clusters and nanoparticles with sizes from 2 to 20 nm were formed. The crystal structure of the resulting gold was not cubic (confirmed by diffraction patterns), but it had a hexagonal close-packed (hcp) arrangement. The ion implantation of gold leads to the creation of Zn and O interstitial defects and extended defects with distinct character in various crystallographic cuts of ZnO, where significant O-sublattice disordering occurred in m-plane ZnO.

This release adds a new field absorber for the Yee solver, convolutional perfectly matched layer (PML). Compared to the still supported exponential damping absorber, PML provides better absorption rate and much less spurious reflections.

We added new plugins for computing emittance and transition radiation, particle rendering with the ISAAC plugin, Python tools for reading and visualizing output of a few plugins.

The release also adds a few quality-of-life features, including a new memory calculator, better command-line experience with new options and bash-completion, improved error handling, cleanup of the example setups, and extensions to documentation.

Please refer to our ChangeLog for a full list of features, fixes and user interface changes before getting started.

Thanks to Igor Andriyash, Sergei Bastrakov, Xeinia Bastrakova, Andrei Berceanu, Finn-Ole Carstens, Alexander Debus, Jian Fuh Ong, Marco Garten, Axel Huebl, Sophie Rudat (Koßagk), Anton Lebedev, Felix Meyer, Pawel Ordyna, Richard Pausch, Franz Pöschel, Adam Simpson, Sebastian Starke, Klaus Steiniger, René Widera for contributions to this release!