Sunthetics is structured around harnessing energy from sunlight whereby to fuel the electrochemical and thermochemical reactions necessary to transform plant waste into the precursor materials needed to produce nylon - among other product materials. Anchored in related work undertaken by an NYU professor with two of his students having joined forces to launch Sunthetics with a view to moving the idea from the lab to the real world. With the judgment being that the process could likely be interest to not just the fashion industry â which produces millions of tons of petrochemical-based nylon each year, thereby generating significant emissions of carbon dioxide â but to anyone in the broader chemical-manufacturing world seeking greener production methods. The assembled team Sunthetics Inc is tackling development of smart electrochemical reactors that enable fast, hyper efficient and sustainable development of new chemicals: machine-learning solutions capable of leveraging small datasets to generate insight, allowing chemists to need fewer experiments for the same information. With the chemistry industry being currently the third largest contributor of greenhouse gas emissions with signification materials ending up in waste streams - in an effort to increase sustainability, the Sunthetics ML tool accelerates the optimization and development of formulations, products, and processes through leveraging data points to quickly predict optimal formulations and process conditions for enhanced performance. The software can improve existing processes, increase efficiencies, and be used to identify anomalies and facilitate diagnostics. The result is chemists need 10X less experiments to come to the same conclusion as traditional experimental campaign
