Amperometric and spectrophotometric determination of carbaryl in natural waters and commercial formulations

The work presented describes the development and evaluation of two flow-injection analysis (FIA) systems for the automated determination of carbaryl in spiked natural waters and commercial formulations. Samples are injected directly into the system where they are subjected to alkaline hydrolysis thus forming 1-naphthol. This product is readily oxidised at a glassy carbon electrode. The electrochemical behaviour of 1-naphthol allows the development of an FIA system with an amperometric detector in which 1-naphthol determination, and thus measurement of carbaryl concentration, can be performed. Linear response over the range 1.0×10–7 to 1.0×10–5 mol L–1, with a sampling rate of 80 samples h–1, was recorded. The detection limit was 1.0×10–8 mol L–1. Another FIA manifold was constructed but this used a colorimetric detector. The methodology was based on the coupling of 1-naphthol with phenylhydrazine hydrochloride to produce a red complex which has maximum absorbance at 495 nm. The response was linear from 1.0×10–5 to 1.5×10–3 mol L–1 with a detection limit of 1.0×10–6 mol L–1. Sample-throughput was about 60 samples h–1. Validation of the results provided by the two FIA methodologies was performed by comparing them with results from a standard HPLC–UV technique. The relative deviation was <5%. Recovery trials were also carried out and the values obtained ranged from 97.0 to 102.0% for both methods. The repeatability (RSD, %) of 12 consecutive injections of one sample was 0.8% and 1.6% for the amperometric and colorimetric systems, respectively.

Crossing genetic and swarm intelligence algorithms to generate logic circuits

Genetic Algorithms (GAs) are adaptive heuristic search algorithm based on the evolutionary ideas of natural selection and genetic. The basic concept of GAs is designed to simulate processes in natural system necessary for evolution, specifically those that follow the principles first laid down by Charles Darwin of survival of the fittest. On the other hand, Particle swarm optimization (PSO) is a population based stochastic optimization technique inspired by social behavior of bird flocking or fish schooling. PSO shares many similarities with evolutionary computation techniques such as GAs. The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. PSO is attractive because there are few parameters to adjust. This paper presents hybridization between a GA algorithm and a PSO algorithm (crossing the two algorithms). The resulting algorithm is applied to the synthesis of combinational logic circuits. With this combination is possible to take advantage of the best features of each particular algorithm.