Degree

Doctor of Philosophy (PhD)

Department

Mechanical Engineering

Document Type

Dissertation

Abstract

The mechanisms of various optical and medical applications depend on the characteristics of the surface of the material which is being used to detect the molecule of interest. In this work, we focused on some specific areas of surface engineering that play a vital role to improve the performance of some material characteristics. Firstly, we focused on the wettability of graphene, which is important for surface modification and thermal/fluidic properties. The level of transparency to van der Waals forces, chemical bonds, and electrostatic interactions between atoms and molecules on two sides of graphene single layer is not well understood. Our results showed that the underlying substrate does affect the wettability of graphene monolayer due to van der Waal hydrocarbon, metal-carbide, and silica-carbon bonding. This study illustrates the mechanism of short-range forces between water-graphene-substrate, which is important during the fabrication of adhesives and coatings with controlled fluidic and heating properties. Secondly, we shifted our focus to improve the Raman signal coming out of surface to detect methylated and non-methylated guanine structure with the assistance of surface-enhanced Raman spectroscopy (SERS) and density functional theory (DFT). Because of their vulnerability for causing DNA methylation, the N7 and O6 positions of the guanine structure were the positions of interest with the addition of various adducts such as methyl, hydroxyethyl, and deuterated methyl groups. The results presented in this part demonstrate a potential label-free technique to examine epigenetic modification of DNA. Finally, we used SERS detection method to analyze SARS-COV2 samples and machine learning algorithms for improving the sensitivity and selectivity of the detection approach. The citrate-stabilized gold nanoparticles (AuNPs) were designed and capped with thiol-modified antisense oligonucleotides (ASO-ssDNA) to use as a SARS-COV2 virus sensor. Raman spectroscopy experiments were performed to detect the virus in saliva clinical samples. After performing a statistical analysis with principal component analysis, we developed a machine learning algorithm through MATLAB for gaining higher sensitivity and specificity for our detection method. This generalized approach can be utilized for the analysis of any Raman spectra to perform classifications (e.g. diseased state vs. healthy).

Committee Chair

Gartia, Manas R.

Available for download on Monday, November 04, 2024

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