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A preliminary examination of a simple and rapid screening method for quantifying a range of toxic organohalides directly in aqueous solution based on their electrocatalytic reduction with a metalloporphyrin catalyst is described. Homogenous catalysis is described as well as heterogeneous catalysis using precipitated cobalt(II) tetraphenylporphine ((TPP)Co) at a graphite foil electrode which permitted the sensitive detection of a wide range of different organohalides, including a number of chemically diverse industrial pollutants such as 1,2,3,4,5,6-hexachlorocyclohexane (lindane) and carbon tetrachloride, representative of haloalkane compounds, haloalkenes such as perchloroethylene, and aromatics, such as 2,4-dichlorophenoxyacetic acid, pentachlorophenol, and the insecticide DDT. The coordinating effect of solvent on the thermodynamics of the Co(II)/(I) electrode reaction is used to practical advantage to build an amperometric detector that is insensitive to interference from oxygen, a parameter that varies considerably in environmental samples. Devices also appear relatively insensitive to the ionic composition of the analyte sample. The work provides the basis for developing a prototype sensor for screening toxic organohalogen pollutants for use in environmental monitoring situations.

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Dual pulse staircase voltammetry (DPSV)a combination of pulsed electrochemical detection and staircase voltammetryis investigated for the simultaneous determination of glucose, fructose, and ethanol in mixtures. Each analyte is found to elicit a distinctive response at a platinum electrode in an alkaline solution. A method is devised for visualizing the electrochemical responses of numerous mixtures of the three compounds simultaneously, and application of this method reveals that the mixed responses contain characteristics of the individual analytes approximately in proportion to their concentrations but that the combination of the individual responses is not a simple summation. Extraction of individual analyte concentrations from combined DPSV responses is subsequently achieved using artificial neural networks (ANNs). The effects of the amount of training data, the number of hidden neurons, the hidden neuron transfer function, and the network training time are investigated. Large amounts of training data and a hidden layer with log-sigmoidal transfer functions are found to give the best results. Networks with relatively small hidden layers and relatively little training are found to produce the most generalized models, giving the most accurate concentration predictions when tested on analyte concentrations not present in the training data. The lowest rms errors achieved were 40 μM, 40 μM, and 0.5 mM for fructose, glucose, and ethanol, respectively, over a range of approximately 0−700 μM for the sugars and a range of 0−12 mM for ethanol. The success of this novel combination of DPSV and ANNs opens new possibilities for the simultaneous detection of mixtures of aliphatic compounds, which are traditionally considered difficult to detect.