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Neuromorphic Organic Devices that Specifically Discriminate Dopamine from Its Metabolites by Nonspecific Interactions

Martina Giordani, Matteo Sensi, Marcello Berto, Michele Di Lauro, Carlo Augusto Bortolotti, Henrique L. Gomes, Michèle Zoli, Francesco Zerbetto, Luciano Fadiga, Fabio Biscarini

2020Advanced Functional Materials31 citationsDOIOpen Access PDF

Abstract

Abstract Specific detection of dopamine (DA) is achieved with organic neuromorphic devices with no specific recognition function in an electrolyte solution. The response to voltage pulses consists of amplitude‐depressed current spiking mimicking the short‐term plasticity (STP) of synapses. An equivalent circuit hints that the STP timescale of the device arises from the capacitance and resistance of the poly(3,4‐ethylenedioxythiophene):polystyrenesulfonate (PEDOT:PSS) in series with the electrolyte resistance. Both the capacitance and resistance of PEDOT:PSS change with solution compositions. Dose curves are constructed from the STP timescale for each DA metabolite from pM to mM range of concentrations. The STP response of DA is distinctive from the other metabolites even when differences are by one functional group. Both STP and sensitivity to DA are larger across the patho‐physiological range with respect to those to DA metabolites. Density functional theory calculations hint to a stronger hydrogen bond pattern of DA ammonium compared to cationic metabolites. The exponential correlation between STP and the binding energy of DA metabolites interacting with PEDOT:PSS indicates that the slow dynamics of ionic species in and out PEDOT:PSS is the origin of the neuromorphic STP. The sensing framework discriminates differences of nonspecific interactions of few kcal mol −1 , corresponding to one functional group in the molecule.

Topics & Concepts

Neuromorphic engineeringPEDOT:PSSMaterials scienceCapacitanceElectrolyteDopamineMetaboliteBiophysicsChemical physicsNanotechnologyChemistryElectrodeNeuroscienceBiologyPhysical chemistryBiochemistryComputer scienceArtificial neural networkLayer (electronics)Machine learningAdvanced Memory and Neural ComputingConducting polymers and applicationsNeuroscience and Neural Engineering