Date of Award

3-10-2019

Publication Type

Doctoral Thesis

Degree Name

Ph.D.

Department

Chemistry and Biochemistry

First Advisor

Keith Taylor

Rights

info:eu-repo/semantics/embargoedAccess

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Abstract

Along with other contaminants, heterocyclic aromatic compounds have found their way to our water sources in concentrations up to tens of µg/L. Contrary to their carbon counterparts, there is a gap of information regarding nitrogen, sulfur and oxygen heterocyclic aromatic contaminants. This information gap also extends to the feasibility of enzymatic treatment of such compounds. In this dissertation, a survey of various heterocyclic aromatic families has been conducted to explore the possibility of removing them from synthetic wastewater by the oxidative polymerization action of soybean peroxidase enzyme. The experiments were designed for ≥ 95% removal of the target compound as the most important parameters pH, enzyme activity, peroxide concentration and reaction time were optimized. In most cases, 85-90% removal efficiency was achieved under the studied conditions. In some cases, the cost of enzyme and peroxide used was considered in determining the optimal treatment conditions. Mass spectral (MS) analysis was conducted on the supernatant and precipitate of the reaction under optimal conditions for preliminary identification of reaction products. Plausible structures were assigned to related empirical formulae derived from MS analysis. Lastly, computational tools were applied to investigate the most favored polymerization positions on the substrates and were compared to structurally related non-substrates. Furthermore, using computational studies, ionization energies and standard reduction potentials of all substrates and some non-substrates were calculated and ranked to investigate a possible trend in SBP specificity based on these two factors.

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