Determination of the thin film structure of zwitterion doped poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate): a neutron reflectivity study

Doping poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) is known to improve its conductivity, however little is known about the thin film structure of PEDOT:PSS when doped with an asymmetrically charged dopant. In this study, PEDOT:PSS was doped with diferent concentrations of the zwiterion 3-(N,N Dimethylmyristylammonio)propanesulfonate (DYMAP), and its effect on the bulk structure of the films characterized by neutron reflectivity. The results show that at low doping concentration, the film separates into a quasi bi-layer structure with lower roughness, (10%), increased thickness (18%), and lower electrical conductivity compared to the undoped sample. However when the doping concentration increases the film forms into a homogeneous layer and experiences an enhanced conductivity by more than an order of magnitude, a 20% smoother surface, and a 60% thickness increase relative to the pristine sample. Atomic force microscopy and profilometry measurements confirmed these findings, and AFM height and phase images showed the gradually increasing presence of DYMAP on the film surface as a function of the concentration. Neutron reflectivity also showed that the quasi bi-layer structure of the lowest concentration doped PEDOT:PSS is separated by a graded rather than a well defined interface. Our findings provide an understanding of the layer structure modification for doped PEDOT:PSS films that should be prove important for device applications

Structural and magnetic properties of ultra-thin Fe films on metal-organic chemical vapour deposited GaN(0001)

Structural and magnetic properties of 1-10 nm thick Fe films deposited on GaN(0001) were investigated. In-situ reflecting high energy electron diffraction images indicated a α-Fe(110)/GaN(0001) growth of the 3D Volmer-Weber type. The α-Fe(110) XRD peak showed a 1° full-width at half-maximum, indicating ≈ 20 nm grain sizes. A significant reduction in Fe atomic moment from its bulk value was observed for films thinner than 4 nm. Both GaN/Fe interface roughness and Fe film coercivity increased with Fe thickness, indicating a possible deterioration of Fe crystalline quality. Magnetic anisotropy was mainly uniaxial for all films while hexagonal anisotropies appeared for thicknesses higher than 3.7 nm

Quantifying the computational capability of a nanomagnetic reservoir computing platform with emergent magnetization dynamics

Devices based on arrays of interconnected magnetic nano-rings with emergent magnetization dynamics have recently been proposed for use in reservoir computing applications, but for them to be computationally useful it must be possible to optimise their dynamical responses. Here, we use a phenomenological model to demonstrate that such reservoirs can be optimised for classification tasks by tuning hyperparameters that control the scaling and input rate of data into the system using rotating magnetic fields. We use task-independent metrics to assess the rings' computational capabilities at each set of these hyperparameters and show how these metrics correlate directly to performance in spoken and written digit recognition tasks. We then show that these metrics, and performance in tasks, can be further improved by expanding the reservoir's output to include multiple, concurrent measures of the ring arrays magnetic states

Dynamically‐driven emergence in a nanomagnetic system

Emergent behaviors occur when simple interactions between a system's constituent elements produce properties that the individual elements do not exhibit in isolation. This article reports tunable emergent behaviors observed in domain wall (DW) populations of arrays of interconnected magnetic ring‐shaped nanowires under an applied rotating magnetic field. DWs interact stochastically at ring junctions to create mechanisms of DW population loss and gain. These combine to give a dynamic, field‐dependent equilibrium DW population that is a robust and emergent property of the array, despite highly varied local magnetic configurations. The magnetic ring arrays’ properties (e.g., non‐linear behavior, “fading memory” to changes in field, fabrication repeatability, and scalability) suggest they are an interesting candidate system for realizing reservoir computing (RC), a form of neuromorphic computing, in hardware. By way of example, simulations of ring arrays performing RC approaches 100% success in classifying spoken digits for single speakers

Data underpinning: Remotely induced magnetism in a normal metal using a superconducting spin-valve

This dataset comprises the scanning Hall probe microscopy data presented in the supplementary information that accompanies the article titled "Remotely induced magnetism in a normal metal using a superconducting spin-valve" Flokstra et al, Nature Physics (2015)

Data underpinning: Remotely induced magnetism in a normal metal using a superconducting spin-valve

This dataset comprises the scanning Hall probe microscopy data presented in the supplementary information that accompanies the article titled "Remotely induced magnetism in a normal metal using a superconducting spin-valve" Flokstra et al, Nature Physics (2015)