Theoretical treatment of gradient chamber and single-bead string reactor flow injection systems with emphasis on evaluation of an error-compensating method for data-processing

James Madison Jordan, Purdue University

Abstract

The evaluation of an error-compensating predictive kinetic method for computing steady-state signals from physicochemical transient data is described. Gradient chamber and single-bead string reactor flow-injection systems are used to evaluate curve-fitting methods as compared to conventional methods of data-processing. The curve-fitting methods use the leading edges of time-dependent response profiles to compute the steady-state signal from a fit of the data to a first-order model. The results of these curve-fitting methods were compared to results from conventional data-processing methods for flow injection signals, namely peak-height, peak-width and peak-area, applied to the same data sets. These methods were evaluated on the basis of error coefficients for injection volume, flow rate, and chamber volume. A direct-computational predictive method that does not compensate for errors in flow rate and chamber volume can offer up to a 200-fold improvement in error coefficient for injection volume over the conventional methods. The more rugged, iterative curve-fitting predictive method can offer up to a 150-fold improvement in error coefficient for flow rate and has the additional advantage of being able to extend the linear dynamic range of detection, 5-fold in this study, and can compensate for distorted signals due to slow reaction kinetics. A theoretical treatment of a gradient chamber flow injection system is presented to provide insight into the limitations and variable dependencies of the data-processing methods. These equations are experimentally validated using the amperometric detection of triiodide. These equations could also be used with the predictive method for fitting signals other than first-order. This treatment was extended to the single-bead string reactor system and a preliminary evaluation of the first-order predictive method is presented.

Degree

Ph.D.

Advisors

Pardue, Purdue University.

Subject Area

Analytical chemistry

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