Preprint Article Version 1 This version is not peer-reviewed

Deep Convolutional Neural Network Applied on Copper Surface Coatings Based on Polyvinyl Alcohol and Silver Nanoparticles

Version 1 : Received: 20 December 2018 / Approved: 21 December 2018 / Online: 21 December 2018 (07:51:06 CET)

A peer-reviewed article of this Preprint also exists.

Samide, A.; Stoean, R.; Stoean, C.; Tutunaru, B.; Grecu, R.; Cioateră, N. Investigation of Polymer Coatings Formed by Polyvinyl Alcohol and Silver Nanoparticles on Copper Surface in Acid Medium by Means of Deep Convolutional Neural Networks. Coatings 2019, 9, 105. Samide, A.; Stoean, R.; Stoean, C.; Tutunaru, B.; Grecu, R.; Cioateră, N. Investigation of Polymer Coatings Formed by Polyvinyl Alcohol and Silver Nanoparticles on Copper Surface in Acid Medium by Means of Deep Convolutional Neural Networks. Coatings 2019, 9, 105.

Journal reference: Coatings 2019, 9, 105
DOI: 10.3390/coatings9020105

Abstract

In order to design effective protective coatings against corrosion, the polyvinyl alcohol (PVA) as compound and composite with silver nanoparticles (nAg/PVA) were electrodeposited on copper surface employing electrochemical techniques such as linear potentiometry and cyclic voltammetry. A new paradigm was used to distinguish the features of coatings, i.e., a Deep Convolutional Neural Network (CNN) was implemented to automatically and hierarchically extract the discriminative characteristics from the information given by optical microscopy images. The main arguments that invoke a CNN implementation in the surface science of materials are the following: artificial intelligence techniques can be successfully applied to learn differences between surface coatings; based on their popularity for image processing, CNN can model images related to the problem of coatings; deep learning is able to extract the features that are distinguishable between material surfaces. To provide an overview of the copper surface, CNN was applied on microscope slides ([email protected]) and inherently learnt distinctive characteristics for each class of surface morphology. The material surface morphology controlled by CNN without the interference of the human factor was successfully conducted, in our study, to extract the similarities/differences between unprotected and protected surfaces to establish the PVA and nAg/PVA performance to retard the copper corrosion.

Subject Areas

copper; polymer coatings; polyvinyl alcohol; silver nanoparticles; deep learning; CNN

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