Graduation Semester and Year
Summer 2026
Language
English
Document Type
Dissertation
Degree Name
Doctor of Philosophy in Chemistry
Department
Chemistry and Biochemistry
First Advisor
Kevin A Schug
Second Advisor
Daniel Armstrong
Third Advisor
Saiful Chowdhury
Fourth Advisor
Junha Jeon
Abstract
Supercritical fluid separations have gained momentum in recent years, driven by a growing emphasis on greener analytical practices and reduced use of toxic solvents. Carbon dioxide is the most widely used supercritical solvent due to its readily accessible critical conditions, non-flammability, non-corrosivity, and low cost. Advances in packed-column instrumentation and integration with conventional HPLC platforms have further broadened its adoption. While SFC has found application across food science, clinical analysis, and steroid characterization, the on-line coupling of supercritical fluid extraction with SFC (SFE-SFC) remains underutilized due to the complexity of method development. Traditional univariate optimization is manageable in low-parameter systems but becomes impractical in high-dimensional platforms like SFE-SFC, where parameter interactions are easily overlooked.
This dissertation addresses these challenges through univariate and surrogate modeling approaches. A univariate strategy first optimizes a novel ionization source for SFC-MS, extending detection to poorly ionizable analytes such as polycyclic aromatic hydrocarbons. Three subsequent studies apply multivariate adaptive regression splines (MARS)-based surrogate optimization to the SFE-SFC-MS platform, progressively expanding from extraction parameter optimization to the inclusion of SFC-specific variables, sequential analyte optimization, and finally optimization across ten compositionally distinct solid matrices. MARS modeling consistently identified near-optimal conditions within approximately 30 experimental runs, substantially reducing experimental effort compared to conventional design-of-experiments approaches.
Keywords
Design of Experiments, Supercritical Fluid Extraction, Supercritical Fluid Chromatography, Multivariate Adaptive Regression Splines
Disciplines
Analytical Chemistry | Data Science
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Bhakta, Niray, "Development and Optimization of Supercritical Fluid Separations and Mass Spectrometry Using Univariate and Surrogate Modeling Methodologies" (2026). Chemistry & Biochemistry Dissertations. 12.
https://mavmatrix.uta.edu/chemistry_dissertations2/12
Comments
I am truly grateful for the many wonderful people who helped me get here. I couldn't have done it without them.
I'm grateful to my advisor, Dr. Kevin Schug. Your support was a big reason I chose UTA, and you've played a significant role in helping me become a better scientist. Your guidance, encouragement, and immeasurable patience truly carried me through the challenging moments. I feel so fortunate to have had such a dedicated mentor, and I look forward to the day I can give back what you've given me.
I'm truly grateful for my labmates; their support and companionship have made this journey more enjoyable and less lonely. A special thank you to my undergraduate mentee, Destini Black. Your energy and curiosity brought so much positivity, and I really appreciated having you involved. The data we collected is a wonderful reflection of your effort, and I'm incredibly thankful to have shared this experience with you.
To my dear friends and family, I feel incredibly fortunate to have you in my life. Dad and Mom, thank you for your unwavering support in celebrating tiny victories and big milestones. To my (non-science) friends, thank you for patiently listening to countless practice presentations. Your kindness and understanding never go unnoticed. I'm sorry for any times I tested your patience, but I am endlessly grateful.
To my amazing wife, Karishma, finding the right words is difficult. You made so many sacrifices so I could pursue this dream, handling everything with grace and love each day. Through late nights, stress, and endless complaints about what isn’t working, you were always by my side, steady and unwavering. Kane, thank you for keeping me company with your snores and running dreams.