Surface study of inhibitor films formed by polyvinyl alcohol and silver nanoparticles on stainless steel in hydrochloric acid solution using convolutional neural networks

被引:28
作者
Samide, Adriana [1 ]
Stoean, Catalin [2 ,4 ]
Stoean, Ruxandra [2 ,3 ]
机构
[1] Univ Craiova, Fac Sci, Dept Chem, 107i Calea Bucuresti, Craiova, Romania
[2] Univ Craiova, Fac Sci, Dept Comp Sci, 13 AI Cuza, Craiova, Romania
[3] Univ Malaga, ETSI Telecomunicac, Grp Ingn Sistemas Integrados TIC 125, Campus Teatinos, E-29071 Malaga, Spain
[4] Univ Illinois, Beckman Inst Adv Sci & Technol, 405 N Mathews Ave, Urbana, IL 61801 USA
关键词
Convolutional neural network; Deep learning; Optical microscopy; Stainless steel; Corrosion inhibition; Polymer inhibitor; CARBON-STEEL; CORROSION INHIBITION; PARTICLES;
D O I
10.1016/j.apsusc.2018.12.255
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
The Convolutional Neural Network (CNN) approach of deep learning was successfully applied to a field of interest such as metal surface science with modified morphology via corrosion performed in the presence and absence of inhibitors. Thus, given many microscopy images, the artificial intelligence technique learns distinctive features for each class of standard/unprotected/protected surface. Specifically, the study highlights the deep learning capacity to distinguish between the surfaces of standard stainless steel and those modified by cyclic voltammetry and potentiodynamic polarization carried out in hydrochloric acid solution in the absence and presence of some corrosion inhibitors such as polyvinyl alcohol and polyvinyl alcohol with silver nanoparticles.
引用
收藏
页码:1 / 5
页数:5
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