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Yocto

Team Members: 

Amr Saleh (Postdoc, MSE) and PI: Prof. Jen Dionne (MSE)

The slow, expensive, and inefficient identification of contaminants in complex, nutrient-rich samples such as food, water and soil represent one of the most critical sustainability challenges facing our generation. Industrial chemical waste, bacterial pathogens, antibiotics, and microplastics are increasingly found in consumable products, threatening human, animal and ecological health. Yocto is developing a handheld optical scanner that can screen for contaminants within seconds and with sensitivities of at least parts-per-billion. Our platform is based on Raman spectroscopy - inelastic photon scattering from a sample based on molecular vibrations. Yocto combines this Raman sample fingerprint with machine learning based algorithms that uniquely identify the contaminant and its concentration. Yocto can provide onsite detection of contaminants with minimal-to-no sample preparation and with lower cost, a smaller footprint, and higher sensitivity than existing detection platforms.