Polymer Microparticle Analysis using a Neural Network Model and Single-Entity Electrochemistry (SEE)

Contamination of water resources with polymer microparticulate matter is a global environmental problem the solution of which requires innovative analytical tools. To this effect, we are developing a highly sensitive electrochemical platform that is based on single-entity electrochemical analysis (SEE) of polymer microparticles. SEE works by recording an electrochemical signal change due to a collision of an individual particle with the sensing electrode surface. However, a common problem with this approach is the complication arising from the non-uniform current distribution over the surface of the electrode (“edge effect”) and resulting in the difficulty in identifying the size of the analyte particles. In this presentation I will describe an approach that uses a neural network (NN) to analyze the SEE data and make prediction of the particle size. The use of NN modeling has proven to be accurate, and we expect it to lead the way towards smart electrochemical sensors.