STTR Phase I: Integrating Machine Learning and Multimodal eCH Sensing for Real-Time Biomarker Detection

The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I aims to develop a novel electrochemical biosensing platform for real-time monitoring of dynamic biomarkers that underlie complex chronic conditions. The project also promotes interdisciplinary innovation, fosters industry-academic collaboration, and supports U.S. competitiveness in digital health technology. Together, these efforts support the development of a transformative platform that bridges molecular insight and personalized care through real-time, intelligent biosensing. This Small Business Technology Transfer (STTR) Phase I project will address major limitations of current biosensors, which rely on unimodal signal detection and suffer from poor analyte specificity, narrow dynamic ranges, and poor sensitivity. This is especially problematic for analytes like levodopa, which requires real-time monitoring within a shifting and narrow therapeutic window, and lactate, which fluctuates rapidly during critical illness. The project will overcome these limitations by combining multiple electrochemical biosensing modalities to improve signal quality and sensitivity and capture biomarker dynamics over broad physiological ranges. Key innovations include the use of engineered direct electron transfer enzymes to ensure substrate-specific responses, and the integration of machine learning models trained to interpret complex signal features while minimizing noise. Phase I will include sensor signal acquisition and machine learning model development, as well as in vitro and ex vivo validation of models using human plasma. This work establishes a modular framework applicable to additional analytes. By leveraging fused multimodal features, this project is among the first to fully integrate machine learning with multimodal electrochemical sensing, advancing both algorithm development and biosensor performance for future clinical applications. 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.