A critical review of Glucose biosensors based on Carbon nanomaterials: Carbon nanotubes and graphene

There has been an explosion of research into the physical and chemical properties of carbon-based nanomaterials, since the discovery of carbon nanotubes (CNTs) by Iijima in 1991. Carbon nanomaterials offer unique advantages in several areas, like high surface-volume ratio, high electrical conductivity, chemical stability and strong mechanical strength, and are thus frequently being incorporated into sensing elements. Carbon nanomaterial-based sensors generally have higher sensitivities and a lower detection limit than conventional ones. In this review, a brief history of glucose biosensors is firstly presented. The carbon nanotube and grapheme-based biosensors, are introduced in Sections 3 and 4, respectively, which cover synthesis methods, up-to-date sensing approaches and nonenzymatic hybrid sensors. Finally, we briefly outline the current status and future direction for carbon nanomaterials to be used in the sensing area. © 2012 by the authors; licensee MDPI, Basel, Switzerland.

Dispersion of carbon nanotubes: Mixing, sonication, stabilization, and composite properties

Advances in functionality and reliability of carbon nanotube (CNT) composite materials require careful formulation of processing methods to ultimately realize the desired properties. To date, controlled dispersion of CNTs in a solution or a composite matrix remains a challenge, due to the strong van der Waals binding energies associated with the CNT aggregates. There is also insufficiently defined correlation between the microstructure and the physical properties of the composite. Here, we offer a review of the dispersion processes of pristine (non-covalently functionalized) CNTs in a solvent or a polymer solution. We summarize and adapt relevant theoretical analysis to guide the dispersion design and selection, from the processes of mixing/sonication, to the application of surfactants for stabilization, to the final testing of composite properties. The same approaches are expected to be also applicable to the fabrication of other composite materials involving homogeneously dispersed nanoparticles. © 2012 by the authors; licensee MDPI, Basel, Switzerland.

Carbon nanostructure-based field-effect transistors for label-free chemical/biological sensors.

Over the past decade, electrical detection of chemical and biological species using novel nanostructure-based devices has attracted significant attention for chemical, genomics, biomedical diagnostics, and drug discovery applications. The use of nanostructured devices in chemical/biological sensors in place of conventional sensing technologies has advantages of high sensitivity, low decreased energy consumption and potentially highly miniaturized integration. Owing to their particular structure, excellent electrical properties and high chemical stability, carbon nanotube and graphene based electrical devices have been widely developed for high performance label-free chemical/biological sensors. Here, we review the latest developments of carbon nanostructure-based transistor sensors in ultrasensitive detection of chemical/biological entities, such as poisonous gases, nucleic acids, proteins and cells.

Guided self-organization in a dynamic embodied system based on attractor selection mechanism

Guided self-organization can be regarded as a paradigm proposed to understand how to guide a self-organizing system towards desirable behaviors, while maintaining its non-deterministic dynamics with emergent features. It is, however, not a trivial problem to guide the self-organizing behavior of physically embodied systems like robots, as the behavioral dynamics are results of interactions among their controller, mechanical dynamics of the body, and the environment. This paper presents a guided self-organization approach for dynamic robots based on a coupling between the system mechanical dynamics with an internal control structure known as the attractor selection mechanism. The mechanism enables the robot to gracefully shift between random and deterministic behaviors, represented by a number of attractors, depending on internally generated stochastic perturbation and sensory input. The robot used in this paper is a simulated curved beam hopping robot: a system with a variety of mechanical dynamics which depends on its actuation frequencies. Despite the simplicity of the approach, it will be shown how the approach regulates the probability of the robot to reach a goal through the interplay among the sensory input, the level of inherent stochastic perturbation, i.e., noise, and the mechanical dynamics. © 2014 by the authors; licensee MDPI, Basel, Switzerland.