Multicomponent gas-mixture measurements using an array of gas sensors and an artificial neural-network

An Electronic Nose is being jointly developed between the University of Greenwich and the Institute of Intelligent Machines to detect the gases given off from an oil filled transformer when it begins to break down. The gas sensors being used are very simple, consisting of a layer of Tin Oxide (SnO2) which is heated to approximately 640 K and the conductivity varies with the gas concentrations. Some of the shortcomings introduced by the commercial gas sensors available are being overcome by the use of an integrated array of gas sensors and the use of artificial neural networks which can be 'taught' to recognize when the gas contains several components. At present simulated results have achieved up to a 94% success rate of recognizing two component gases and future work will investigate alternative neural network configurations to maintain this success rate with practical measurements.

Surface-enhanced Raman scattering studies of rhodanines: Evidence for substrate surface-induced dimerization

The surface-enhanced Raman scattering (SERS) spectra of rhodanine adsorbed on silver nanoparticles have been examined using 514.5 and 632.8 nm excitation. There is evidence that, under the experimental conditions used, rhodanine undergoes a nanoparticle surface-induced reaction resulting in the formation of a dimeric species via the active methylene group in a process which is analogous to the Knoevenagel reaction. The experimental observations are supported by DFT calculations at the B3-LYP/cc-pVDZ level. Calculated energies for the interaction of the E and Z isomers of the dimers of rhodanine with silver nanoparticles support a model in which the (intra-molecular hydrogen bonded) E isomer dimer is of lower energy than the Z isomer. A strong band, at 1566 cm(-1), in the SERS spectrum of rhodanine is assigned to the nu(C=C) mode of the dimer species.