ERI: RUI: integrative biosensing platforms for monitoring maternal health

The National Science Foundation Division of Chemical, Bioengineering, Environmental, and Transport Systems awarded $199,998 to Santa Clara University on September 1, 2026, under the Engineering Research Initiation (ERI) program (CFDA 47.041) to develop an integrative biosensing platform for detecting pregnancy complications.

The project will design a paper-based, hyperspectral imaging device that detects biomarkers associated with preeclampsia, peripartum cardiomyopathy, and gestational diabetes from whole blood samples. The system uses vertical flow to separate red blood cells, a patterned sensing membrane capturing target biomarkers, and platinum-palladium nanozymes to enhance signal strength. Hyperspectral imaging generates unique fingerprints for each biomarker, with AI models analyzing the data to distinguish overlapping signals and estimate concentrations. System performance will be validated against standard laboratory methods. The low-cost, rapid-result approach addresses the current gap in early detection of serious pregnancy-related conditions that can affect maternal and fetal health. The project will also provide undergraduate students with hands-on training in biomedical engineering and computing.

Work is performed at Santa Clara University in Santa Clara, California, with a period of performance from September 1, 2026, through August 31, 2028.

Abstract

Some women develop conditions during pregnancy that can affect their health and their baby?S health. These conditions include serious high blood pressure disorders, heart failure, and a form of diabetes that begins during pregnancy. Currently, these conditions are often detected only after symptoms appear, or through tests that require specialized laboratories and multiple visits. This project will develop a low-cost test to measure multiple biomarkers from whole blood samples. The test will use a paper-based device with tiny spots that react to different biomarkers in the sample. A hyperspectral camera will read the colors on the test, and artificial intelligence (AI) will convert these signals into numerical values that reflect patient risk. By providing information earlier in pregnancy, this tool could reduce complications and improve outcomes for families. The project will also provide undergraduate students with hands-on training in biomedical engineering and computing. This engineering research initiation project will develop a hyperspectral imaging-based strategy to sense markers associated with pregnancy complications. The approach uses vertical flow to move a blood sample through stacked layers. The layers separate red blood cells. A patterned sensing membrane captures biomarkers associated with three conditions: preeclampsia, peripartum cardiomyopathy, and gestational diabetes. Signal strength will be enhanced using platinum-palladium nanozymes. Hyperspectral imaging will generate a unique fingerprint for each biomarker. The data will be analyzed using AI models to distinguish overlapping signals and estimate biomarker concentrations. The system will also account for differences in signals over time to improve accuracy. System performance will be validated against standard laboratory methods. This AI-powered imaging and sensing system will deliver a rapid and accurate approach to monitor health during and after pregnancy. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the foundation's intellectual merit and broader impacts review criteria.- subawards are not planned for this award.