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In order to assemble effective protective coatings against corrosion, electrochemical techniques such as linear potentiometry and cyclic voltammetry were performed on a copper surface in 0.1 mol·L−1 HCl solution containing 0.1% polyvinyl alcohol (PVA) in the absence and presence of silver nanoparticles (nAg/PVA). A recent paradigm was used to distinguish the features of the coatings, that is, a deep convolutional neural network (CNN) was implemented to automatically and hierarchically extract the discriminative characteristics from the information given by optical microscopy images. In our study, the material surface morphology, controlled by the CNN without the interference of the human factor, was successfully conducted to extract the similarities/differences between unprotected and protected surfaces in order to establish the PVA and nAg/PVA performance to retard copper corrosion. The CNN results were confirmed by the classical investigation of copper behavior in hydrochloric acid solution in the absence and presence of polyvinyl alcohol and silver nanoparticles. The electrochemical measurements showed that the corrosion current density (icorr) decreased and polarization resistance (Rp) increased, with both PVA and nAg/PVA being effective inhibitors for copper corrosion in an acid environment, forming polymer protective coatings by adsorption on the metal surface. Furthermore, scanning electron microscopy (SEM) certifies the formation of polymer coatings, revealing a specific morphology of the copper surface in the presence of PVA and nAg/PVA, very different from that of corroded copper in uninhibited solutions. Finally, the correlation of the CNN information with experimental data was reported.

coatings

Investigation of Polymer Coatings Formed by Polyvinyl Alcohol and Silver Nanoparticles on Copper Surface in Acid Medium by Means of Deep Convolutional Neural Networks