Research at the University of Utah is advancing new ways to measure the brain’s chemical messengers—at a fraction of the cost of traditional methods.

Two University of Utah Ph.D. students, Mohammad Mohammadi (Chemical Engineering) and Sima Najafzadehkhoei(Biostatistics), recently had their work accepted in Nanoscale advances. Guided by Dr. Yunshan Wang from the Department of Chemical Engineering, and in collaboration with Dr. George G. Vega Yon from the Department of Internal Medicine, their study introduces a low-cost method that combines electromagnetic sensors and machine learning to detect critical neurochemicals known as catecholamines.

Catecholamines—such as dopamine, norepinephrine, and epinephrine—help regulate mood, stress, and cardiovascular function. When their levels become unbalanced, it can contribute to conditions like hypertension, heart failure, anxiety, or depression. The team’s approach allows these molecules to be measured quickly, frequently, and affordably, using simple blood or urine samples.

“This is an exciting example of how our Ph.D. students are not just contributing to the research enterprise but leading it,” said Dr. Wang. “By working across disciplines—from chemical engineering to health sciences—they’re showing how engineering and data science together can improve real-world healthcare.”

Their work, titled “A novel approach for classifying Monoamine Neurotransmitters by applying Machine Learning on UV plasmonic-engineered Auto Fluorescence Time Decay Series (AFTDS)” in the Journal Nanoscale Advances, highlights how interdisciplinary collaboration at the U continues to drive innovation—turning engineering and data insights into practical tools for better mental and physical health.