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. Coatings2019, 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.
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. Coatings2019, 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.
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 (CNN@microscopy) 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.
Keywords
copper; polymer coatings; polyvinyl alcohol; silver nanoparticles; deep learning; CNN
Subject
Chemistry and Materials Science, Surfaces, Coatings and Films
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
