Lizeth Hernandez

Lizeth Hernandez

Biography

Lizeth Hernandez is a senior at Metropolitan State University of Denver, pursuing a bachelor’s degree in Biochemistry with a minor in Biology. Her passion for research began with a Research Experience for Undergraduates (REU) at Colorado State University, where she worked with Dr. Thomas Santangelo to investigate the DNA repair mechanisms in extremophilic organisms. Motivated by translating scientific research into tangible benefits for human health, she joined Dr. Shailesh Ambre’s research team at MSU Denver, where she contributes to the purification and synthesis of SHAPE probes to study the structure of RNA.At MIT, she works withDr. Mercedes Balcells-Camps to develop a biosensor for real-time monitoring of tissue healing around implanted medical devices. Beyond the lab, she mentors marginalized students through TRIO SSS and serves as secretary of MSU Denver’s Society for the Advancement of Chicanos and Native Americans in Science, working to promote higher education and empower underrepresented voices in STEM. Lizeth’s ultimate goal is to pursue an MD/PhD to unravel disease mechanisms, develop accessible treatments, and expand educational opportunities in underserved communities. Outside of school, Lizeth is learning to play the guitar and is training for her first half marathon.

Abstract

Automated Surface Morphology Characterization of Nanosensors for Medical Implants

Innovations in implantable medical devices have transformed patient care, but complications at then tissue-implant interface such as infection and chronic inflammation remain a persistent challenge. Existing diagnostic tools used to monitor tissue-implant interactions are destructive and cannot track biological processes in real time, limiting our ability to preemptively treat complications. To address these limitations, a titanium substrate coated with gold nanocolumns (AuNCs) was studied as a self-sensing implant material using Surface-Enhanced Raman Scattering (SERS). The surface plasmon resonance of AuNCs amplifies Raman scattering signals at the biomaterial surface, enabling real-time, nondestructive, and multiplexed insights into the implant-tissue interface. Quantifying the degree of surface enhancement is an ever-present challenge, requiring precise nanoscale estimates of SERS substrate surface morphology. In this study, an automated image analysis pipeline was developed using ImageJ to characterize key morphological features of AuNCs from scanning electron microscopy images. These features influence hot-spot formation and are essential for calculating SERS enhancement factors (EFs), which are used to compare the efficacy of SERS sensors. EFs were calculated using automated measurements and compared with manual measurements to validate the automated image analysis pipeline. Preliminary results suggest the automated approach is faster, more scalable, and may reduce human bias introduced during hand measurements of AuNC features.