High-Resolution Nanoscale Solid-State Nuclear Magnetic Resonance Spectroscopy

We present a new method for high-resolution nanoscale magnetic resonance imaging (nano-MRI) that combines the high spin sensitivity of nanowire-based magnetic resonance detection with high spectral resolution nuclear magnetic resonance (NMR) spectroscopy. By applying NMR pulses designed using optimal control theory, we demonstrate a factor of $500$ reduction of the proton spin resonance linewidth in a $(50\text{-nm})^{\text{3}}$ volume of polystyrene and image proton spins in one dimension with a spatial resolution below $2~\text{nm}$.Comment: Main text: 8 pages, 6 figures; supplementary information: 10 pages, 10 figure

Crystal growth and magnetic structure of MnBi2Te4

Millimeter-sized MnBi$_2$Te$_4$ single crystals are grown out of Bi-Te flux and characterized by measuring magnetic and transport properties, scanning tunneling microscope (STM) and spectroscopy (STS). The magnetic structure of MnBi$_2$Te$_4$ below T$_N$ is determined by powder and single crystal neutron diffraction measurements. Below T$_N$=24\,K, Mn$^{2+}$ moments order ferromagnetically in the \textit{ab} plane but antiferromagnetically along the crystallographic \textit{c} axis. The ordered moment is 4.04(13) $\mu_{B}$/Mn at 10\,K and aligned along the crystallographic \textit{c}-axis. The electrical resistivity drops upon cooling across T$_N$ or when going across the metamagnetic transition in increasing fields below T$_N$. A critical scattering effect was observed in the vicinity of T$_N$ in the temperature dependence of thermal conductivity. However, A linear temperature dependence was observed for thermopower in the temperature range 2K-300K without any anomaly around T$_N$. These indicate that the magnetic order in Mn-Te layer has negligible effect on the electronic band structure, which makes possible the realization of proposed topological properties in MnBi$_2$Te$_4$ after fine tuning of the electronic band structure

Event-driven simulations of a plastic, spiking neural network

We consider a fully-connected network of leaky integrate-and-fire neurons with spike-timing-dependent plasticity. The plasticity is controlled by a parameter representing the expected weight of a synapse between neurons that are firing randomly with the same mean frequency. For low values of the plasticity parameter, the activities of the system are dominated by noise, while large values of the plasticity parameter lead to self-sustaining activity in the network. We perform event-driven simulations on finite-size networks with up to 128 neurons to find the stationary synaptic weight conformations for different values of the plasticity parameter. In both the low and high activity regimes, the synaptic weights are narrowly distributed around the plasticity parameter value consistent with the predictions of mean-field theory. However, the distribution broadens in the transition region between the two regimes, representing emergent network structures. Using a pseudophysical approach for visualization, we show that the emergent structures are of "path" or "hub" type, observed at different values of the plasticity parameter in the transition region.Comment: 9 pages, 6 figure