My senior project focuses on developing heteroatom-doped carbon dot biosensors embedded in hydrogel matrices for real-time detection of cancer biomarkers. I am conducting this research under the mentorship of Dr. Dake Hao at UC Davis Health's Center for Surgical Bioengineering, where for the past few months I've already been tinkering with fluorescent carbonaceous nanoparticles doped with nitrogen, phosphorus, and boron synthesized via bottom up hydrothermal methods. These carbon dots exhibit biomarker-responsive fluorescence changes that can be analyzed using machine learning algorithms to distinguish between "metabolic signatures" (eg. reactive oxygen/nitrogen species, lactate, and pH) that differentiate cancer cells from normal cells. My research methodology includes various laboratory techniques as well as computational work on which I will be collaborating with 1 person. I will also conduct comprehensive literature review on carbon dot synthesis, tumor metabolism, and biosensor technologies to contextualize my findings within the current scientific landscape.

My final product will be a research manuscript suitable for submission to an academic journal, presenting experimental data on carbon dot synthesis, biomarker detection performance, and machine learning classification accuracy. The manuscript will include quantitative results from cell culture experiments demonstrating the biosensor's ability to detect elevated ROS and altered metabolic states in cancer cells compared to normal cells. Additionally, I will create a presentation for the senior project defense showcasing the translational potential of this technology as a platform for continuous disease monitoring. While the ultimate vision for this technology includes implantable sensors for in vivo tumor monitoring, my senior project scope focuses on establishing proof-of-concept through in vitro validation, demonstrating that carbon dot-hydrogel composites can successfully distinguish cancer cell metabolism from normal cell metabolism using machine learning-enabled fluorescence analysis. This foundational work establishes the technical feasibility and scientific rigor necessary for future translation to animal models and eventual clinical applications.