Machine learning-powered electrochemical aptasensor for simultaneous monitoring of di(2-ethylhexyl) phthalate and bisphenol A in variable pH environments

The research team, led by Professor Hyo-il Jeong from the Department of Mechanical Engineering (co-first authors of this study are Kyungyeon Lee and Seong Min Ha) developed an ML-based aptasensor platform that can precisely analyze the concentration of plastic pollutants di(2-ethylhexyl) phthalate (DEHP) and bisphenol A (BPA) using water samples from 12 river spots. It has been integrated platform with an electrochemical aptasensor modified with AuNF and a hybrid regression algorithm that can quickly predict the concentration of the two substances without being affected by pH, and the concentration prediction result was achieved with an error rate of less than 6%. This research has been published in the renowned international academic journal 'Journal of Hazardous Materials' (Impact Factor: 13.6) in January 2024.

The link: https://doi.org/10.1016/j.jhazmat.2023.132775