Nuclear resonant surface diffraction

Nuclear resonant x-ray diffraction in grazing incidence geometry is used to determine the lateral magnetic configuration in a one-dimensional lattice of ferromagnetic nanostripes. During magnetic reversal, strong nuclear superstructure diffraction peaks appear in addition to the electronic ones due to an antiferromagnetic order in the nanostripe lattice. We show that the analysis of the angular distribution of the resonantly diffracted x-rays together with the time-dependence of the coherently diffracted nuclear signal reveals surface spin structures with very high sensitivity. This novel scattering technique provides a unique access to laterally correlated spin configurations in magnetically ordered nanostructures and, in perspective, also to their dynamics

Spin precession mapping at ferromagnetic resonance via nuclear resonant scattering

We probe the spin dynamics in a thin magnetic film at ferromagnetic resonance by nuclear resonant scattering of synchrotron radiation at the 14.4 keV resonance of $^{57}$Fe. The precession of the magnetization leads to an apparent reduction of the magnetic hyperfine field acting at the $^{57}$Fe nuclei. The spin dynamics is described in a stochastic relaxation model adapted to the ferromagnetic resonance theory by Smit and Beljers to model the decay of the excited nuclear state. From the fits of the measured data the shape of the precession cone of the spins is determined. Our results open a new perspective to determine magnetization dynamics in layered structures with very high depth resolution by employing ultrathin isotopic probe layers

Effect of dopants on thermal stability and self-diffusion in iron nitride thin films

We studied the effect of dopants (Al, Ti, Zr) on the thermal stability of iron nitride thin films prepared using a dc magnetron sputtering technique. Structure and magnetic characterization of deposited samples reveal that the thermal stability together with soft magnetic properties of iron nitride thin films get significantly improved with doping. To understand the observed results, detailed Fe and N self-diffusion measurements were performed. It was observed that N self-diffusion gets suppressed with Al doping whereas Ti or Zr doping results in somewhat faster N diffusion. On the other hand Fe self-diffusion seems to get suppressed with any dopant of which heat of nitride formation is significantly smaller than that of iron nitride. Importantly, it was observed that N self-diffusion plays only a trivial role, as compared to Fe self-diffusion, in affecting the thermal stability of iron nitride thin films. Based on the obtained results effect of dopants on self-diffusion process is discussed.Comment: 10 pages, 9 fig

Origin of exchange bias in [Co/Pt]ML/Fe multilayer with orthogonal magnetic anisotropies

Magnetization reversal of soft ferromagnetic Fe layer, coupled to [Co/Pt]ML multilayer [ML] with perpendicular magnetic anisotropy (PMA), has been studied in-situ with an aim to understand the origin of exchange bias (EB) in orthogonal magnetic anisotropic systems. The interface remanant state of the ML is modified by magnetic field annealing, and the effect of the same on the soft Fe layer is monitored using the in-situ magneto-optical Kerr effect (MOKE). A considerable shift in the Fe layer hysteresis loop from the centre and an unusual increase in the coercivity, similar to exchange bias phenomena, is attributed to the exchange coupling at the [Co/Pt]ML and Fe interface. The effect of the coupling on spin orientation at the interface is further explored precisely by performing an isotope selective grazing incident nuclear resonance scattering (GINRS) technique. Here, the interface selectivity is achieved by introducing a 2 nm thick Fe57 marker between [Co/Pt]ML and Fe layers. Interface sensitivity is further enhanced by performing measurements under the x-ray standing wave conditions. The combined MOKE and GINRS analysis revealed the unidirectional pinning of the Fe layer due to the net in-plane magnetic spin at the interface caused by magnetic field annealing. Unidirectional exchange coupling or pinning at the interface, which may be due to the formation of asymmetrical closure domains, is found responsible for the origin of EB with an unusual increase in coercivity.Comment: 9 figures, 1 tabl

Caroline - ein autonom fahrendes Fahrzeug im Stadtverkehr

We have previously shown that the physiological size of postsynaptic currents maximises energy efficiency rather than information transfer across the retinothalamic relay synapse. Here, we investigate information transmission and postsynaptic energy use at the next synapse along the visual pathway: from relay neurons in the thalamus to spiny stellate cells in layer 4 of the primary visual cortex (L4SS). Using both multicompartment Hodgkin-Huxley-type simulations and electrophysiological recordings in rodent brain slices, we find that increasing or decreasing the postsynaptic conductance of the set of thalamocortical inputs to one L4SS cell decreases the energy efficiency of information transmission from a single thalamocortical input. This result is obtained in the presence of random background input to the L4SS cell from excitatory and inhibitory corticocortical connections, which were simulated (both excitatory and inhibitory) or injected experimentally using dynamic-clamp (excitatory only). Thus, energy efficiency is not a unique property of strong relay synapses: even at the relatively weak thalamocortical synapse, each of which contributes minimally to the output firing of the L4SS cell, evolutionarily-selected postsynaptic properties appear to maximise the information transmitted per energy used

Machine learning based prediction of COVID-19 mortality suggests repositioning of anticancer drug for treating severe cases

Despite available vaccinations COVID-19 case numbers around the world are still growing, and effective medications against severe cases are lacking. In this work, we developed a machine learning model which predicts mortality for COVID-19 patients using data from the multi-center ‘Lean European Open Survey on SARS-CoV-2-infected patients’ (LEOSS) observational study (>100 active sites in Europe, primarily in Germany), resulting into an AUC of almost 80%. We showed that molecular mechanisms related to dementia, one of the relevant predictors in our model, intersect with those associated to COVID-19. Most notably, among these molecules was tyrosine kinase 2 (TYK2), a protein that has been patented as drug target in Alzheimer's Disease but also genetically associated with severe COVID-19 outcomes. We experimentally verified that anti-cancer drugs Sorafenib and Regorafenib showed a clear anti-cytopathic effect in Caco2 and VERO-E6 cells and can thus be regarded as potential treatments against COVID-19. Altogether, our work demonstrates that interpretation of machine learning based risk models can point towards drug targets and new treatment options, which are strongly needed for COVID-19