Propose to develop a functional prototype based on our Stain-to-Composition (S2C) technology, an innovative web-based tool that enables chemical analysis from smartphone photographs of dried liquid droplets. S2C combines experimental chemistry with machine learning to identify and quantify solute compositions based on the visual patterns left by evaporating drops. This image-based approach eliminates the need for laboratory infrastructure, enabling rapid, low-cost diagnostics in environmental, consumer, and healthcare settings. The requested funding will support the creation of a cross-platform app, a secure backend, and an expanded training dataset using an improved version of our autonomous robotic drop imager (RODI-2). Within 12 months, we aim to deliver a fully functional prototype capable of providing real-time results for tap water analysis.
Join the National NSF ART Network – Submit the Membership Request Form
