Date of Award

12-2024

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Chemistry and Chemical Engineering

First Advisor

Dr. Pavithra Pathirathna

Second Advisor

Dr. Manolis M. Tomadakis

Third Advisor

Dr. James R. Brenner

Fourth Advisor

Dr. Jessica Smeltz

Abstract

Recent urbanization has induced a rapid increase in heavy metal exposure to humans. The exploitation of heavy metals for economic gains coupled with poor and economically infeasible recycling methods have led to poor management of heavy metal wastes, which has opened up several pathways for heavy metals to enter the human body. Such heavy metals target many vital organs, but the early symptoms are common and difficult to diagnose. Severe symptoms appear in later stages and are very expensive to treat and the damage caused to the health are not typically reversable in such later stages. To counter this issue, it is pivotal to develop early detection systems for such heavy metal contamination. While it is essential to develop a reliable and robust heavy metal sensor, achieving it in a cost-effective manner will aid in widespread reach and adoption. While conventional methods have been well investigated and used, electrochemical methods for such analysis have gained growing interest in recent years due to their fast-processing times, ease of use, and cost-effectiveness. This dissertation presents a series of studies aimed at achieving these goals using electrochemical detection. The research is divided into three projects, each building upon the findings of previous research. The first project introduces a novel nanopipette-based ion transfer between two immiscible electrolyte solutions (ITIES) sensors for the detection of Cd(II) ions. Utilizing 1,10-phenanthroline as an ionophore, the sensor demonstrates high sensitivity and selectivity in various complex matrices, including tris buffer and artificial seawater. The sensor successfully quantified the amount of Cd(II) in an environmental sample, which was validated using inductively coupled plasma mass spectrometry, which is the gold standard or detection of metals in aqueous solutions. The second project expands the sensor's capabilities to simultaneously detect multiple metal ions, specifically Cd(II) and Ca(II), in complex matrices such as artificial blood and urine. A simplified single-bore, single-ionophore configuration was studied to examine the practicality and ease of fabrication of the sensor. The performance of the sensor is used to evaluate the effectiveness of a common chelation agent used in chelation therapy during early stages of heavy metal exposure. The last project addresses the challenge of data interpretation in electrochemical sensing by integrating artificial intelligence (AI) with ITIES-based sensors. This unique approach integrates Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN) to analyze cyclic voltammograms (CVs). The CNN model is capable of differentiating Cd(II) from Cu(II) by using the shape of the CV alone and can identify faulty CVs from the good ones. The ANN model is used to determine the analyte concentration. This integrated approach represents a significant advancement in electrochemical data analysis, making it more accessible to non-experts, thus aiding the widespread use of the sensor.

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