Date of Award
Spring 2023
Project Type
Dissertation
Program or Major
Chemical Engineering
Degree Name
Doctor of Philosophy
First Advisor
Jeffrey Halpern
Second Advisor
Kyung Jae Jeong
Third Advisor
Linqing Li
Abstract
Sepsis is a complicated medical emergency and critically ill patients suffering from infectious diseases are at a high risk for developing and dying from sepsis. According to a recent (2019) cohort study from six United States hospitals, suboptimal care (e.g., delay in antibiotics, inappropriate antibiotic therapy) is responsible for 22.7 % of in-hospital sepsis-associated deaths. In September 2020, the World Health Organization called on the scientific community to develop rapid, effective, and affordable tools to improve diagnosis, surveillance, and treatment of sepsis. Our long-term goal throughout this project is to develop a cyclodextrin (CD) impedimetric tongue that can provide early warning of sepsis by continuous monitoring the course of the disease and real-time profiling of urine samples in hospitalized patients. The cyclodextrin impedimetric tongue will also enable healthcare professionals to closely monitor the patients, predict sensitivity and resistance to therapies, decide about the dose of medications, and develop more effective personalized therapies for septic patients.Cyclodextrins, oligosaccharides with a hydrophobic cavity and a hydrophilic surface, are promising biorecognition elements in development of reusable impedimetric tongues. Cyclodextrins can semi-selectively detect different metabolites in the solutions through hydrophobic interactions, Van der Waals forces, and hydrogen bonding. The first aim of this thesis was to develop the first reusable nanostructured cyclodextrin platform using αCD and a weak surface αCD mediator: polyethylene glycol (PEG). To create the Gold-PEG:αCD surface, gold surface was modified with PEG via thiol-gold chemistry and the PEG support enabled reversible immobilization of αCD. We investigated the performance of this platform for detection of a model hydrophobic analyte, trans-resveratrol. Non-faradaic electrochemical impedance spectroscopy (EIS) measurement of the surface suggested that when αCD surfaces are introduced to a solution containing trans-resveratrol, αCD molecules leave the PEG support to interact with trans-resveratrol in the solution. After use, the surface could be regenerated by reloading of αCD. The second aim of this thesis was to improve the stability and reusability of cyclodextrin sensing platform by replacing gold-thiol bonds with carbon-carbon covalent bonds between glassy carbon (GC) and 4-carboxyphenyl diazonium salt. The GC-carboxyphenyl was modified with polypropylene glycol (PPG) through EDC/NHS chemistry. The PPG surface was then loaded with βCD. We used the GC-carboxyphenyl-PPG:βCD surface for sensitive detection of cortisol in biofluids (i.e., urine and saliva), and demonstrated the successful regeneration and reuse of the GC-carboxyphenyl-PPG:βCD surface for ten times. Finally, we employed sensitive, stable, and reusable cyclodextrin nanostructured surfaces to develop the first-generation cyclodextrin impedimetric tongue for separation and classification of four classes of bioanalytes including creatinine, cortisol, glucose, and fumarate. We applied linear discriminant analysis (LDA) to integrate and map the data, and by using the normalized changes in imaginary capacitance of three cyclodextrin surfaces (γCD at 79 Hz, hydroxypropyl-βCD at 0.25 Hz, and hydroxypropyl-γCD at 63.34 Hz), we achieved the 5-fold cross validation accuracy of 69%. Different methods of data preparation, EIS signal processing, and determining the characteristic frequencies of different analytes and single frequencies of cyclodextrin surfaces affect the accuracy of the impedimetric tongue. By optimizing these parameters, we can improve the performance and accuracy of the impedimetric tongue and apply this device for point of need applications.
Recommended Citation
Panahi, Zahra, "Cyclodextrin Impedimetric Biosensors for the Detection of Hydrophobic Metabolites" (2023). Doctoral Dissertations. 2747.
https://scholars.unh.edu/dissertation/2747