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.

ANALYTICAL DEVICE FOR TEST FLUIDS, COMPRISES A CONDUCTIVE POLYMER COMPOSITION

A sensor for chemical species or biological species or radiation presenting to test fluid a polymer composition comprises polymer and conductive filler metal, alloy or reduced metal oxide and having a first level of electrical conductance when quiescent and being convertible to a second level of conductance by change of stress applied by stretching or compression or electric field, in which the polymer composition is characterised by at least one of the features in the form of particles at least 90% w/w held on a 100 mesh sieve; and/or comprising a permeable body extending across a channel of fluid flow; and/or affording in-and-out diffusion of test fluid and/or mechanically coupled to a workpiece of polymer swellable by a constituent of test fluid.

A species conserving genetic algorithm for multimodal function optimization

This paper introduces a new technique called species conservation for evolving parallel subpopulations. The technique is based on the concept of dividing the population into several species according to their similarity. Each of these species is built around a dominating individual called the species seed. Species seeds found in the current generation are saved (conserved) by moving them into the next generation. Our technique has proved to be very effective in finding multiple solutions of multimodal optimization problems. We demonstrate this by applying it to a set of test problems, including some problems known to be deceptive to genetic algorithms.