FloCync has developed an innovative menstrual blood collection system with integrated filtration technology that enables non-invasive health monitoring and diagnostics research. Our novel solution combines a specially designed menstrual cup with dual-membrane filtration and a “click-it” vacutainer mechanism that preserves critical biomarkers by separating samples immediately at collection, before clotting occurs.
Initially targeting research institutions and clinical trials, FloCync standardizes menstrual blood collection to accelerate biomarker discovery for conditions like endometriosis and PCOS. The goal of the project is to address diagnostic delays affecting millions of women globally.
Beyond research applications, FloCync will expand to consumer diagnostics, empowering women to monitor chronic conditions at home through lateral flow assays, like pregnancy tests but for disease biomarkers. By transforming a discarded biological fluid into valuable health data, FloCync is building the infrastructure for the next era of women’s health innovation.

The Project Design team completed a climate vulnerability assessment and engineering design alternatives for adaptable solutions for this critical water quality infrastructure, the Back River Wastewater Treatment Plant which service 1.3 million residents. The engineering solutions included both natured based and structural options to provide coastal resilience to both built and natural infrastructure up to the year 2100 to protect against sea level rise, storm surge and increasing precipitation. The students also created a multi-criteria decision analysis for the alternatives developed to identify the most effective solutions to carry forward for further design.

Heavy Menstrual Bleeding (HMB), or Menorrhagia, is a menstrual condition defined as greater than 80 mL of blood loss per cycle. HMB can be an indicator for many serious conditions, such as endometriosis, reproductive cancer, and anemia. The most used methods to measure menstrual output volume are inaccurate and subjective. Furthermore, the volume threshold for HMB does not consider the unique characteristics between people who menstruate. This means clinicians need more insightful information about a person who menstruates menstrual output to assist in patient care. We are developing an imaging-based app solution that will utilize a machine learning model to offer an accurate and objective way to measure the menstrual blood output during a period, while also using a personalized approach to establish individual baseline blood loss values in order to predict abnormal menstrual bleeding.

An integrated circuit consisting of the 3 Transistor Active Pixel Sensor models, C2MOS shift registers, and a column read-out circuit. This project denotes the results of our implementation of the 3T APS in the Cadence software.