Date of Award

Summer 8-2026

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Chemistry & Biochemistry

Program/Concentration

Chemistry

Committee Director

John B. Cooper

Committee Member

Alvin A. Holder

Committee Member

Bala Ramjee

Committee Member

Charles I. Sukenik

Abstract

Surface-enhanced Raman spectroscopy (SERS) offers high sensitivity for molecular detection but is limited for quantitative applications by spatial heterogeneity in signal enhancement on dried colloidal substrates. This Dissertation addresses these limitations through the development and evaluation of novel quantitative methods applied to the antiretroviral drugs tenofovir and emtricitabine.

A quality index (Qi) metric was developed to rank individual SERS spectra within each well based on analyte peak prominence relative to local baseline. SERS spectra were acquired from evaporated silver colloid deposits on aluminum well plates using a custom raster-scanning Raman system. Calibration curves were constructed using the top-ranked spectra (Top-Qi method) and compared to a distributional approach based on cumulative distribution functions of Qi values (Σ ΔQCDF). These methods were evaluated in aqueous tenofovir standards using single- and double-aliquot deposition strategies. The optimized protocols were then applied to the quantification of emtricitabine in human plasma following an optimized pretreatment protocol. In a final study, hyperspectral SERS imaging combined with progressive partial least squares (PLS) regression models of increasing spatial complexity was evaluated using adenine as a model analyte.

The Top-Qi and Σ ΔQCDF methods both improved calibration linearity and analytical sensitivity compared to conventional averaging of all acquired spectra, with the Σ ΔQCDF approach providing superior stepwise discrimination between adjacent concentration levels. In plasma, both methods yielded robust calibration performance after sample pretreatment, with negligible impact from non-specific plasma signals. Hyperspectral PLS modeling restricted to high signal-to-noise hotspot regions produced lower and more consistent prediction errors on independent test sets than models using blind averaging or global spectral selection. These findings indicate that targeted spectrum selection and distributional modeling can reduce the impact of substrate heterogeneity, supporting more reliable quantitative SERS measurements in both aqueous and complex biological matrices.

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In Copyright. URI: http://rightsstatements.org/vocab/InC/1.0/ This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).

DOI

10.25777/g2f9-4x09

ISBN

9798193214489

ORCID

0009-0006-5828-8838

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