NSF Convergence Accelerator Track L: HEADLINE - HEAlth Diagnostic eLectronIc NosE

Improving global health demands using advanced technologies for diagnostic applications and health monitoring systems, particularly in the form of portable devices for point-of-care applications. With the recent scientific studies showing correlations between gases/volatile organic compounds (VOCs) released from a person’s body/breath and their health status, this project aims to design a hybrid and modular electronic nose (e-Nose) system for non-invasive diagnostic applications. Through an international collaboration, researchers from two universities in the US, one university in Sweden, and a Swedish company work together to converge their research on various technologies related to gas sensing and novel artificial intelligence (AI)/machine learning (ML) models to design an advanced medical tool with the potential of a global impact. The current e-Nose technology based on metal-oxide semiconductors has limitations in the detection and concentration measurements of critical chemicals that needed to be monitored as health status indicators. More importantly, the poor selectivity of those sensors negatively impacts the accuracy of the measurements. This initial project brings together a range of sensor technologies, alongside novel sensing materials, into a hybrid e-Nose that can selectively detect a large range of VOCs. Such a hybrid sensing platform requires different AI/ML models that can process the data from different sensors to identify the composition of the target gases and VOCs with a high enough accuracy and precision needed for medical applications. Hence, the eventual goal is to deliver a hybrid gas/VOC sensor platform and AI/ML models to interpret the signals from various sensors mainly for medical diagnostic 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.