NSF Convergence Accelerator track L: Translating insect olfaction principles into practical and robust chemical sensing platforms

Washington University, Saint Louis MO

Investigators

Abstract

This project seeks to develop a novel sensor that can be used for sensing explosive volatile organic compounds. The sensor will use insect-inspired, nanoparticle-based sensor technology along with artificial intelligence (AI). This work will be the initial step towards the next-generation e-nose technology for non-invasive chemical sensing, with application to biomedicine, homeland security, environmental monitoring, resilient planet technologies, and the flavor and food industry. This convergence project is providing opportunities to (i) identify avenues of integration between chemical, physical, biological, and data sciences to create a translatable technology; (ii) pave the way for increased partnerships between academia, industry, national labs, nonprofit organization, and other stakeholders; (iii) lay the seeds for the development of the next-generation electronic noses with large sensor arrays and incorporation of biologically inspired design and computing principles; and (iv) train the next generation of scientists and entrepreneurs. This research will synthesize highly reliable and reproducible sensing elements based on biological sensing principles of robust odor recognition observed in an insect-olfactory system. To address the key challenges in the field of chemical sensing, the proposed research will facilitate the convergence of two key concepts: (i) a scalable approach for synthesizing a large nanostructured chemical sensor array with diverse functionality, and (ii) incorporation of key sensing and AI principles that the research team has identified in the insect olfactory system over the past 15 years. The short-term goal is to fabricate and demonstrate a proof-of-concept, portable AI-enabled e-nose device that can be used for data collection and validation. Performance metrics such as dose-response curves, limit of detection, classification performance, and receiver operating characteristic curves will be established for a panel of targeted compounds. A library of known signatures for various explosive vapors at appropriate concentration ranges (few parts per billion to tens of parts per million) will be generated. 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.