Research Projects

Heart cancer results from heart tumors or metastatic cancer spreading to the heart. This project develops a revolutionary heart monitoring strategy using nanomaterial-based sensing platforms for effective diagnosis and timely intervention. We will design and fabricate advanced nanomaterials to measure heart signals, extract physiological insights for heart status assessment, and create decision-making algorithms for real-time optimal control in heart cancer studies. Students will gain experience in materials science, biomedical engineering, and signal processing while contributing to potentially revolutionary cardiac cancer diagnosis solutions.

Current diagnostic systems face two major limitations: sensitivity (false negatives) and selectivity (false positives). This project investigates a novel point-of-care electrochemical platform combining shear force with nanoporous capacitive electrodes, meeting WHO's ASSURED criteria for POC diagnosis. The platform detects cancer biomarkers including p53, HER2, BRCA1, and IL-6. Students will develop and optimize chips, establish detection limits, investigate selectivity through interference studies, and gain expertise in electrochemical spectroscopy, statistical analysis, and biological sample handling.

Non-target side effects of systemic therapeutics drive the need for next-generation drug delivery carriers. This project develops self-assembling peptide platforms tailored with EGF-receptor binding domains for breast and ovarian cancers. Students will computationally design EGFr binding peptides using GROMACS+Rosetta, synthesize peptides via solid-phase synthesis, characterize them using LC/MS, circular dichroism, FTIR, AFM, and SEM, and evaluate binding through SPR and fluorescent-tagged peptide studies. The research provides critical feasibility data for rational peptide design in cancer treatment.

A major hurdle for drug delivery nanoparticles is circulation time, as many are quickly removed by the immune system. Protein corona formation may signal particle removal, making accurate measurement crucial for understanding in vivo behavior. This project synthesizes PLGA nanoparticles with EGFR targeting antibodies and PEG ligands at different ratios. Students will use nanoparticle tracking analysis to determine particle size and protein corona formation, study aggregation behavior over 24 hours in blood plasma, and optimize ligand presentation for enhanced circulation time in triple-negative breast cancer treatment.

Neurotoxic organophosphorus compounds (NOPCs) pose major public health threats through dermal absorption. This research develops mathematical descriptions of NOPC transport and inactivation in skin layers. Current decontamination protocols are limited, and new approaches focus on degrading agents within dermal layers. Students will develop numerical methods to select decontamination candidates, create simulation platforms to elucidate transport mechanisms, and identify physicochemical properties of efficient deactivation agents. The research addresses reports of volatile hazardous substances remaining in deeper skin layers for extended periods.

Cancer diagnosis from medical images traditionally relies on specialized medical professionals with years of training. This project develops graph neural networks (GNNs) to differentiate between healthy and cancerous cells in breast tomosynthesis images. Unlike traditional convolutional neural networks, GNNs excel at segmenting images and identifying distinct structures. Students will develop GNNs that process images by accurately identifying spatial regions and detecting abnormalities to determine healthy versus cancerous status. The outcome will be a model capable of processing medical images and providing diagnostic output.

Microfluidics offer potential cancer diagnosis pathways due to high surface area-to-volume ratios, low production costs, and complex fluid handling ease. This project studies electromagnetic field effects on lipid metabolism in healthy and cancer cells using 3D printed microfluidic devices with planar electrodes. Students will fabricate and characterize electrode-equipped microfluidic devices, perform biocompatibility studies, and image healthy and breast cancer cells. The research develops novel non-invasive approaches to cancer diagnosis and treatment while providing experience in 3D printing, microscopy, and biophotonic studies.

Tumor model success depends on collecting cell bioparticles in extracellular matrix systems. Conventional collection methods require harvesting and ECM digestion, hampering efficient tumor cell monitoring. This project develops 3D bioprinted breast cancer cell-laden microfluidic platforms equipped with interdigital transducer surface acoustic wave modules. Students will fabricate microfluidic chip designs, encapsulate cancer cells into ink precursors, and measure cell responses to acoustic fields through PCR measurements. The research validates device sensitivity in isolating bioparticles while characterizing biophysical and biological cell characteristics.

Volatile organic compound detection is crucial for breath diagnostics, especially early lung cancer detection. Lung cancer patients exhale specific VOCs (propane, carbon disulfide, ethylbenzene, etc.) at ppb levels. Current GC-MS approaches require lengthy sample preparation and analysis. This project develops microfluidic electrochemical gas sensors combining microfluidic architecture with electrochemical detection for enhanced sensitivity. Students will model sensor systems using COMSOL Multiphysics, perform electrochemical experiments, and apply ML/AI techniques for VOC differentiation. The research addresses current sensor limitations of low sensitivity and poor selectivity.