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R. Palm

Publications and source records attributed to R. Palm.

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Magnetism and Ion Diffusion in Honeycomb Layered Oxide K$_2$Ni$_2$TeO$_6$: First Time Study by Muon Spin Rotation & Neutron Scattering

In the quest of finding novel and efficient batteries, a great interest has raised in K-based honeycomb layer oxide materials both for their fundamental properties and potential applications. A key issue in the realization of efficient batteries based on such compounds, is to understand the K-ion diffusion mechanism. However, investigation of potassium-ion (K$^+$) dynamics in materials using magneto-spin properties has so far been challenging, due to its inherently weak nuclear magnetic moment, in contrast to other alkali ions such as lithium and sodium. Spin-polarised muons, having a high gyromagnetic ratio, make the muon spin rotation and relaxation ($\mu$+SR) technique ideal for probing ions dynamics in weak magneto-spin moment materials. Here we report the magnetic properties and K+ dynamics in honeycomb layered oxide material of the K$_2$Ni$_2$TeO$_6$ using $\mu$+SR measurements. Our low-temperature $\mu$+SR results together with, with complementary magnetic susceptibility, find an antiferromagnetic transition at 26 K. Further $\mu$+SR studies performed at higher temperatures reveal that potassium ions (K$^+$) become mobile above 250 K and the activation energy for the diffusion process is Ea = 121(13) meV. This is the first time that K+ dynamics in potassium-based battery materials has been measured using $\mu$+SR. Finally our results also indicate an interesting possibility that K-ion self diffusion occurs predominantly at the surface of the powder particles. This opens future possibilities for improving ion diffusion and device performance using nano-structuring.

cond-mat.mtrl-sci

A ferrofluid based neural network: design of an analogue associative memory

We analyse an associative memory based on a ferrofluid, consisting of a system of magnetic nano-particles suspended in a carrier fluid of variable viscosity subject to patterns of magnetic fields from an array of input and output magnetic pads. The association relies on forming patterns in the ferrofluid during a trainingdphase, in which the magnetic dipoles are free to move and rotate to minimize the total energy of the system. Once equilibrated in energy for a given input-output magnetic field pattern-pair the particles are fully or partially immobilized by cooling the carrier liquid. Thus produced particle distributions control the memory states, which are read out magnetically using spin-valve sensors incorporated in the output pads. The actual memory consists of spin distributions that is dynamic in nature, realized only in response to the input patterns that the system has been trained for. Two training algorithms for storing multiple patterns are investigated. Using Monte Carlo simulations of the physical system we demonstrate that the device is capable of storing and recalling two sets of images, each with an accuracy approaching 100%.

cond-mat.dis-nn