Laser-induced electrical signal filtering by multilayer reduced graphene oxide decorated with Au nanoparticles.

Nanoscale plasmonic particles represent a crucial transformation on optical and electronic properties exhibited by advanced materials. Herein are reported remarkable interferometric optical effects with dependence on polarization for filtering or modulating electronic signals in multilayer nanostructures. Metallic nanoparticles were incorporated in randomly distributed networks of reduced graphene oxide by an in-situ vapor-phase deposition method. The polarization-selectable nonlinear optical absorption contribution on the photoconductivity of reduced graphene oxide decorated with gold nanoparticles was analyzed. Nanosecond pulses at 532 nm wavelength were employed in a two-wave mixing experiment to study photoconduction and nonlinear optical absorption in this nanohybrid material. The ablation threshold of the sample was measured in 0.4 J/cm2. Electrochemical impedance spectroscopy measurements revealed a capacitive response that can be enhanced by gold decoration in carbon nanostructures. A strong two-photon absorption process characterized by 5 × 10-7 m/W was identified as a physical mechanism responsible for the nonlinear photoconductive behavior of the nanostructures. Experimental shift of 1 MHz for the cutoff frequency associated with an electrical filter function performed by the sample in film form was demonstrated. Moreover, amplitude modulation of electronic signals controlled by the polarization of a two-wave mixing experiment was proposed. All-optical and optoelectronic nanosystems controlled by multi-photonic interactions in carbon-based materials were discussed. The key role of the vectorial nature of light in two-wave mixing experiments is a fascinating tool for the exploration of low-dimensional systems.

Adaptive neural network motion control of manipulators with experimental evaluations.

A nonlinear proportional-derivative controller plus adaptive neuronal network compensation is proposed. With the aim of estimating the desired torque, a two-layer neural network is used. Then, adaptation laws for the neural network weights are derived. Asymptotic convergence of the position and velocity tracking errors is proven, while the neural network weights are shown to be uniformly bounded. The proposed scheme has been experimentally validated in real time. These experimental evaluations were carried in two different mechanical systems: a horizontal two degrees-of-freedom robot and a vertical one degree-of-freedom arm which is affected by the gravitational force. In each one of the two experimental set-ups, the proposed scheme was implemented without and with adaptive neural network compensation. Experimental results confirmed the tracking accuracy of the proposed adaptive neural network-based controller.