I-Corps: Translation Potential of an Identification System for Unknown Chemicals in a Mixture

This I-Corps project focuses on the development of a mixture analysis technology using enhanced nuclear magnetic resonance spectroscopy. The ability to accurately analyze complex chemical mixtures is critical in industries such as pharmaceuticals, natural product synthesis, and forensic science. Traditional methods often struggle with overlapping signals and require extensive manual interpretation, leading to inefficiencies and high costs.

This solution introduces an innovative approach that improves the resolution and precision of mixture analysis, enabling users to identify components with greater accuracy and speed. By enhancing chemical identification, this technology reduces the risks associated with product recalls, regulatory compliance issues, and inefficiencies in quality control.

The potential benefits extend beyond industry applications to fields such as environment monitoring and public safety.

This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of a novel analytical method that combines Wavelet Packet Transform with super-resolved proton nuclear magnetic resonance spectroscopy. This method provides superior resolution for overlapping signals, enabling unsupervised analysis without prior knowledge of mixture composition.

Unlike conventional techniques, this approach is highly resistant to noise, making it suitable for challenging experimental conditions. By integrating this advanced signal processing method with existing quality control workflows, the technology offers a scalable and user-friendly solution for industries requiring precise chemical analysis of mixtures.

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.