Poly(vinyl butyral-co-vinyl alcohol-co-vinyl acetate) named further PVBA was investigated as protective coating for copper corrosion in 0.9 % NaCl solution using electrochemical measurements such as, electrochemical impedance spectroscopy (EIS) and potentiodynamic polarization associated with Atomic Force Microscopy (AFM). The PVBA coating on the copper surface (Cu-PVBA) was modeled in methanol containing PVBA. Its inhibitory properties against corrosion was comparatively discussed with those of the copper sample treated in methanol without polymer (Cu-Me) and of untreated sample (standard copper). A protective performance of PVBA coating of 80 % was computed from electrochemical measurements, for copper corrosion in NaCl solution. Also, AFM images designed a specific surface morphology of coated surface with PVBA, clearly highlighting a polymer film adsorbed on the copper surface, which presents certain deterioration after corrosion, but metal surface was not significantly affected compared to those of untreated samples or treated in methanol, in the absence of PVBA.

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.

Our study aims to implement a strategy to reduce the carbon steel corrosion rate in sulfuric acid solution, using an expired drug with adsorption affinity on the metal surface. To investigate the corrosion protection efficiency of an environmental friendly inhibitor, namely neomycin sulfate (NMS), the electrochemical measurements were applied on carbon steel immersed in 1.0 M H₂SO₄ solution with and without NMS. The protective layer formed on the steel surface was studied by atomic force microscopy (AFM). The potentiodynamic polarization and electrochemical impedance spectroscopy (EIS) showed that the presence of the neomycin sulfate in acid solution leads to the decrease in corrosion current density (i_corr) and the increase of polarization resistance (R_p). The mixed mechanism between physical and chemical adsorption of NMS molecules on the steel surface was proposed according to the Langmuir adsorption isotherm. The Atomic Force Microscopy (AFM) indicated that the NMS molecules contributed to a protective layer formation by their adsorption on the steel surface. The AFM parameters such as: root-mean-square roughness (R_q); average roughness (R_a) and maximum peak to valley height (R_p-v) revealed that in the presence of NMS a smoother surface of carbon steel was obtained, compared to the steel surface corroded in sulfuric acid blank solution.