A Neural Network to Decipher Organic Electrochemical Transistors’ Multivariate Responses for Cation Recognition

Abstract

Extracting relevant data from real-world experiments is often challenging with intrinsic materials and device property dispersion, such as in organic electronics. However, multivariate data analysis can often be a mean to circumvent this and to extract more information when larger datasets are used with learning algorithms instead of physical models. Here, we report on identifying relevant information descriptors for organic electrochemical transistors (OECTs) to classify aqueous electrolytes by ionic composition. Applying periodical gate pulses at different voltage magnitudes, we extracted a reduced number of nonredundant descriptors from the rich drain-current dynamics, which provide enough information to cluster electrochemical data by principal component analysis between Ca2+-, K+-, and Na+-rich electrolytes. With six current values obtained at the appropriate time domain of the device charge/discharge transient, one can identify the cationic identity of a locally probed transient current with only a single micrometric device. Applied to OECT-based neural sensors, this analysis demonstrates the capability for a single nonselective device to retrieve the rich ionic identity of neural activity at the scale of each neuron individually when learning algorithms are applied to the device physics.