Numerical study of anisotropic diffusion in Turing patterns based on Finsler geometry modeling.

We numerically study the anisotropic Turing patterns (TPs) of an activator-inhibitor system described by the reaction-diffusion (RD) equation of Turing, focusing on anisotropic diffusion using the Finsler geometry (FG) modeling technique. In FG modeling, the diffusion coefficients are dynamically generated to be direction dependent owing to an internal degree of freedom (IDOF) and its interaction with the activator and inhibitor. Because of this dynamical diffusion coefficient, FG modeling of the RD equation sharply contrasts with the standard numerical technique in which direction-dependent coefficients are manually assumed. To find the solution of the RD equations in FG modeling, we use a hybrid numerical technique combining the Metropolis Monte Carlo method for IDOF updates and discrete RD equations for steady-state configurations of the activator-inhibitor variables. We find that the newly introduced IDOF and its interaction are a possible origin of spontaneously emergent anisotropic patterns of living organisms, such as zebra and fishes. Moreover, the IDOF makes TPs controllable by external conditions if the IDOF is identified with the direction of cell diffusion accompanied by thermal fluctuations.

Tactile Sensing Using Magnetic Foam.

For biomedical applications, smart materials that are used as sensors or actuators have to match some criteria, especially bio-compatibility and softness. Smart polymers are candidates that fulfill these two criteria. A sensitivity to compression is created by adding magnetic particles to a compressible foam polymer. A foam-based composite is fabricated for its small Poisson's ratio, which enables significant compression, up to 50%. This large compression induces a change in its magnetic properties, which can be detected using coils. By setting the sensing coils as a compact array of 3 × 3, the sensor successfully detected and localized an applied deformation.

Micromechanical Force Sensor Using the Stress-Impedance Effect of Soft Magnetic FeCuNbSiB.

By using the stress-impedance (SI) effect of a soft magnetic amorphous FeCuNbSiB alloy, a micromachined force sensor was fabricated and characterized. The alloy was used as a sputtered thin film of 500 nm thickness. To clarify the SI effect in the used material as a thin film, its magnetic and mechanical properties were first investigated. The stress dependence of the magnetic permeability was shown to be caused by the used transducer effect. The sputtered thin film also exhibited a large yield strength of 983 GPa. Even though the fabrication technology for the device is very simple, characterization revealed a gauge factor (GF) of 756, which is several times larger than that achieved with conventional transducer effects, such as the piezoresistive effect. The fabricated device shows great application potential as a tactile sensor.

Coarse-Grained Lattice Modeling and Monte Carlo Simulations of Stress Relaxation in Strain-Induced Crystallization of Rubbers.

Two-dimensional triangulated surface models for membranes and their three-dimensional (3D) extensions are proposed and studied to understand the strain-induced crystallization (SIC) of rubbers. It is well known that SIC is an origin of stress relaxation, which appears as a plateau in the intermediate strain region of stress-strain curves. However, this SIC is very hard to implement in models because SIC is directly connected to a solid state, which is mechanically very different from the amorphous state. In this paper, we show that the crystalline state can be quite simply implemented in the Gaussian elastic bond model, which is a straightforward extension of the Gaussian chain model for polymers, by replacing bonds with rigid bodies or eliminating bonds. We find that the results of Monte Carlo simulations for stress-strain curves are in good agreement with the reported experimental data of large strains of up to 1200%. This approach allows us to intuitively understand the stress relaxation caused by SIC.

Energy conversion in magneto-rheological elastomers.

Magneto-rheological (MR) elastomers contain micro-/nano-sized ferromagnetic particles dispersed in a soft elastomer matrix, and their rheological properties (storage and loss moduli) exhibit a significant dependence on the application of a magnetic field (namely MR effect). Conversely, it is reported in this work that this multiphysics coupling is associated with an inverse effect (i.e. the dependence of the magnetic properties on mechanical strain), denoted as the pseudo-Villari effect. MR elastomers based on soft and hard silicone rubber matrices and carbonyl iron particles were fabricated and characterized. The pseudo-Villari effect was experimentally quantified: a shear strain of 50 % induces magnetic induction field variations up to 10 mT on anisotropic MR elastomer samples, when placed in a 0.2 T applied field, which might theoretically lead to potential energy conversion density in the mJ cm-3 order of magnitude. In case of anisotropic MR elastomers, the absolute variation of stiffness as a function of applied magnetic field is rather independent of matrix properties. Similarly, the pseudo-Villari effect is found to be independent to the stiffness, thus broadening the adaptability of the materials to sensing and energy harvesting target applications. The potential of the pseudo-Villari effect for energy harvesting applications is finally briefly discussed.