Mass Spectrometry and Machine Learning Applied to Abiotic and Biotic Degradation of Dental Composites

Aug 4, 2026

Thesis research applies mass spectrometry together with machine learning to investigate how dental resin composites degrade under abiotic and biotic conditions. The work covers an introduction to resin composite degradation, detailed elution experimental procedures, and the development of a machine-learning framework for mass spectrometric analysis. Author contributions describe how the aging and liquid chromatography tandem mass spectrometry (LC-MS/MS) components are generated and analyzed, including supervised learning focused on monomers released from aged materials using existing tandem mass spectra resources.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages177

Reading Time7 h 26 min

AccessFree to download

Acknowledgements

Contribution of Authors

Chapter 1: Introduction to Dental Resin Composite Degradation

Chapter 2: Experimental Details of Elution Studies

Chapter 3: Machine Learning in Mass Spectrometry

Chapter 4: Published Manuscript and LC-MS/MS Database

Mass Spectrometry and Machine Learning Applied to Abiotic and Biotic Degradation of Dental Composites BY Chien-chia Chen B.S., Saint Cloud State University, 2017 B.A., Saint Cloud State University, 2017 THESIS Submitted as partial fulfillment of the requirements for the degree of Doctor of Philosophy in Chemistry in the Graduate College of the University of Illinois Chicago, 2023 Chicago, Illinois Defense Committee: Luke Hanley, Chair and Advisor Stephanie M. Cologna, Department of Chemistry Xiaojing Yang, Department of Chemistry James L. Drummond, Department of Restorative Dentistry Karl J. Rockne, Department of Civil, Materials and Environmental Engineering This thesis is dedicated to my parents, Hung-Ying Chen and Li-Ling Sun for their unconditional love and support. ACKNOWLEDGEMENTS Words cannot express how grateful I am toward everyone who has helped me throughout my PhD journey, especially my advisor and best mentor Professor Luke Hanley. Dr. Hanley has provided me with tremendous amount of guidance on my research and has always been supportive of my academic goals. He has taught me so much about being a scientist and his guidance has shaped me into the researcher I am today. I would also like to express my sincere gratitude toward my undergraduate advisor Dr. Michael A. Dvorak as well as my organic chemistry instructor Dr. Mark Mechelke from St. Cloud State University for being my role models and believing in my potentials. I would not have become a scientist without them. I would like to thank Dr. Stephanie M. Cologna, Dr. James L. Drummond, Dr. Luke Hanley, Dr. Karl J. Rockne, and Dr. Xiaojing Yang for being on my PhD thesis defense committee and providing their valuable input to my research. I am truly grateful for each member of the dental materials team: Dr. James L. Drummond, Dr. Luke Hanley, Karabi Mondal, Dr. Evan P. O’Brien, and Dr. Karl J. Rockne, for helping me with my project. It has been a pleasure working with each of you. I would also like to acknowledge our collaborators Dr. Philippe Vervliet and Dr. Adrian Covaci from Belgium for sharing their tandem mass spectra database with us and provided valuable input to our project. I would like to give special thanks to Dr. Stephanie M. Cologna for letting me run samples on the instrument in her lab as well as Dr. Chandimal Pathmasiri and Dr. Mi Nguyen for assisting me with my experiments. I would also like to acknowledge Dr. Hui Chen at the RRC for training me on the QTOF and provided guidance on my work. I would like to thank Dr. Carol Ball from Agilent for showing me features in the MassHunter software. I would also like to acknowledge Dr. Yichao Wu for guiding me on the machine learning model for my publication and Dr. Cong Wang for helping me with Python. I truly appreciate every chemistry department staff who has helped me over the years. Our former graduate coordinator Rhondha Staudohar, who is no longer with us, was the first person I got in contact with when I first joined UIC. She was the one who helped me transitioned into the program smoothly when I first moved to Chicago. Her kindness will always be remembered. I would also like to thank Kevin Lynch and Dave Kuntzelman from the machine shop for always being helpful and getting work done efficiently without delays. I am grateful for every current and former members of the Hanley group, Dr. Yeni Yung, Dr. Raveendra C. Wickramasinghe, Dr. A. Chathurya Pulukkody, Dr. Jason M. Gross, Teodora Zagorac, Gabe D. Parker, Reyhane Shavandi and Anna Karagiannakis for going through the PhD journey with me. Finally, I am most thankful for my parents Hung-Ying Chen and Li-Ling Sun as well as my siblings Neil Chen and Claire Chen for supporting me through ups and downs throughout this journey. They are my strongest support system and I would not have succeeded without them. I am also grateful for my “family” in Minnesota, William Erhart, Elizabeth Tuzinski Erha

FAQs

What problem does the thesis address regarding dental composites?

It examines degradation of dental resin composites under abiotic and biotic conditions and focuses on analyzing degradation-related components.

What experimental approach is used to generate data in the thesis?

The thesis describes elution studies followed by liquid chromatography tandem mass spectrometry (LC-MS/MS) analysis of aging solutions and monomers.

How is machine learning incorporated into the mass spectrometry analysis?

Machine learning is introduced as a supervised approach applied to mass spectrometry data, with author-led development of the model under advisor guidance.

Aug 4, 2026

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