Date of Award

May 2019

Degree Type

Dissertation

Degree Name

Doctor of Philosophy

Department

Chemistry

First Advisor

Joseph H Aldstadt

Second Advisor

Alan Schwabacher

Committee Members

Peter Geissinger, Robert Olsson, Andy Pacheco

Keywords

Azo, Metals, process monitoring, Spectrophotometric

Abstract

I will describe studies in developing an on-line system for process monitoring that is based upon spectrophotometric methods. Heavy metals that are discarded into the environment may pose severe threats to public health, and are thus heavily regulated. Currently, there is no reasonably affordable, reliable, real-time monitoring method for heavy metals in water. This lack of feedback for industrial applications often leads to wasted resources due to massive overtreatment to avoid costly penalties. Spectrophotometry is a viable analysis option assuming a suitable sensor molecule is available as seemingly conflicting requirements may occur. Characterization of the sensor is one of the most important considerations when developing an analytical method. For this application, the sensor must be well understood in order to accurately quantify the analyte(s). This involves studying pKa values and binding affinities to fully understand the responses that are seen. Classic titrimetric methods can be used along with multivariate analysis to determine these values. The metal response of a library of novel azo dyes was studies and full characterization is shown on one potential candidate. This characterization includes defining the working regions of the dye and binding affinities to the metals it complexes.

Additionally, a partial Least Squares approach can be used to build a predictive model. This can then simultaneously predict the concentrations of multiple metals in an aqueous sample with exquisite accuracy. This was shown with commercially available, 4-(2-Pyridylazo)resorcinol and 4 metals (Cu,Ni,An,Pb). The mean prediction error for replicate measurements was 0.19 µM (12 ppb) for copper, 0.36 µM (21 ppb) for nickel, 0.26 µM (17 ppb) for zinc, and is 0.14 µM (29 ppb) for lead, and the corresponding standard deviations were below 10 ppb.

The predictive model that was used in solution studies needed to be adapted for flow experiments for use in a remote sensor application. To do this, the sensor dyes were attached to a solid support for use in a flow cell. Flow studies were completed with 2 types of flow systems. The first, was an industrial style that had large volumes and flow rates run by multiple pumps. This flow system was shown to have a stable response to low ppb metal concentrations over several hours. The other flow system used was a sequential injection analysis (SIA) platform. This setup was particularly useful when identifying factors that needed to be included when attempting to model binding and quantify metals. Initial results suggest low ppm detection limits for the monitor being developed with Sequential Injection Analysis (SIA) techniques being explored for a possible microscale device.

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