Electrochemical and Mechanical Properties of Ni/g-C3N4 ‎Nanocomposite Coatings with Enhanced Corrosion Protective ‎Properties: A Case Study for Modeling the Corrosion Resistance ‎by ANN and ANFIS Models

Document Type : Research Paper

Authors

1 Department of Safety Engineering, Abadan Faculty of Petroleum Engineering, Petroleum University of Technology, Abadan, Iran

2 Department of Inspection Engineering, Abadan Faculty of petroleum engineering, Petroleum case study University of Technology, Abadan, Iran

3 Surface Coatings and Corrosion Department, Institute for Color Science and Technology, P.O. Box 16765-654, Tehran, Iran‎

Abstract

This work investigates the effect of electrolysis bath parameters on the corrosion, micro-hardness, and wear behavior of Ni coatings. The characterization of synthesized Graphitic carbon nitride (g-C3N4) was done by Fourier transform infrared, Raman spectroscopy, and transmission electron microscope. The surface morphology of coated samples with various amounts of current density was studied by scanning electron microscopy and energy-dispersive X-ray spectroscopy. The corrosion prevention effect of Ni/g-C3N4 nanocomposite coatings was investigated by EIS and polarization techniques. The experimental outcome demonstrates that an electrolysis bath of 0.3 g/L g-C3N4 and 0.1 A.cm-2 presents a Ni coating with the highest corrosion protection, wear resistance, and microhardness. The corrosion current densities of Ni/g-C3N4 coatings obtained by electrochemical tests were used for training two machine learning techniques (Artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS)) based on current density, g-C3N4 concentration, and plating time as an input. Various statistical criteria showed that the ANFIS model (R2= 0.99) could forecast corrosion current density more accurately than ANN with R2= 0.91. Finally, due to the robust performance of ANFIS in modeling the corrosion behavior of Ni/g-C3N4 nanocomposite coating, the effect of each parameter was studied.

Keywords

Main Subjects

Publisher’s Note Shahid Chamran University of Ahvaz remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. 

[1] Olia, H., Ghobadi, M., Danaee, I. and Onsori, S., Effect of number of layers on erosion, corrosion, and wear resistance of multilayer Cr–N/Cr–Al–N coatings on AISI 630 stainless steel, Materials and Corrosion, 71(8), 2018, 1361-1374.
[2] Kumaraguru, S., Kumar, G.G., Shanmugan, S., Mohan, S., Gnanamuthu, R.M., Enhanced texture and microhardness of the nickel surface using Bi2O3 particles via electrodeposition technique for engineering application, Journal of Alloys and Compounds, 753, 2018, 740-747.
[3] Nayana, K.O., Ranganatha, S., Shubha, H.N., Pandurangappa, M., Effect of sodium lauryl sulphate on microstructure, corrosion resistance and microhardness of electrodeposition of Ni–Co3O4 composite coatings, Transactions of Nonferrous Metals Society of China, 29(11), 2019, 2371-2383.
[4] Raghavendra, C.R., Basavarajappa, S., Sogalad, I., Saunshi, V.K., Study on surface roughness parameters of nano composite coatings prepared by electrodeposition process, Materials Today: Proceedings, 38, 2021, 3110-3115.
[5] Li, B.S., Huan, Y.X., Luo, H., Zhang, W.W., Electrodeposition and properties of Ni–B/SiC nanocomposite coatings, Surface Engineering, 35(2), 2019, 109-119.
[6] Li, B., Zhang, W., Huan, Y., Dong, J., Synthesis and characterization of Ni-B/Al2O3 nanocomposite coating by electrodeposition using trimethylamine borane as boron precursor, Surface and Coatings Technology, 337, 2018, 186-197.
[7] Baghery, P., Farzam, M., Mousavi, A.B., Hosseini, M., Ni–TiO2 nanocomposite coating with high resistance to corrosion and wear, Surface and Coatings Technology, 204(23), 2010, 3804-3810.
[8] Pingale, A.D., Belgamwar, S.U., Rathore, J.S., Effect of graphene nanoplatelets addition on the mechanical, tribological and corrosion properties of Cu–Ni/Gr nanocomposite coatings by electro-co-deposition method, Transactions of the Indian Institute of Metals, 73(1), 2020, 99-107.
[9] Shelke, A.R., Balwada, J., Sharma, S., Pingale, A.D., Belgamwar, S.U., Rathore, J.S., Development and characterization of Cu-Gr composite coatings by electro-co-deposition technique, Materials Today: Proceedings, 28, 2020, 2090-2095.
[10] Zhang, L., Ou, M., Yao, H., Li, Z., Qu, D., Liu, F., Wang, J., Li, Z., Enhanced supercapacitive performance of graphite-like C3N4 assembled with NiAl-layered double hydroxide, Electrochimica Acta, 186, 2015, 292-301.
[11] Fayyad, E.M., Abdullah, A.M., Hassan, M.K., Mohamed, A.M., Wang C, Jarjoura, G., Farhat, Z., Synthesis, characterization, and application of novel Ni-P-carbon nitride nanocomposites, Coatings, 8(1), 2018, 37.
[12] Li, C., Cao, C.B., Zhu, H.S., Graphitic carbon nitride thin films deposited by electrodeposition, Materials Letters, 58(12-13), 2004, 1903-1906.
[13] Fayyad, E.M., Abdullah, A.M., Mohamed, A.M., Jarjoura, G., Farhat, Z., Hassan, M.K., Effect of electroless bath composition on the mechanical, chemical, and electrochemical properties of new NiP–C3N4 nanocomposite coatings, Surface and Coatings Technology, 362, 2019, 239-251
[14] Yang, G., Chen, T., Feng, B., Weng, J., Duan, K., Wang, J., Lu, X., Improved corrosion resistance and biocompatibility of biodegradable magnesium alloy by coating graphite carbon nitride (g-C3N4), Journal of Alloys and Compounds, 770, 2018, 823-830.
[15] Pourhashem, S., Duan, J., Guan, F., Wang, N., Gao, Y., Hou, B., New effects of TiO2 nanotube/g- C3N4 hybrids on the corrosion protection performance of epoxy coatings, Journal of Molecular Liquids, 317, 2020, 114214.
[16] Yan, H., Li, J., Zhang, M., Zhao, Y., Feng, Y., Zhang, Y., Enhanced corrosion resistance and adhesion of epoxy coating by two-dimensional graphite-like g-C3N4 nanosheets, Journal of Colloid and Interface Science, 579, 2020, 152-161.
[17] Chen, C., He, Y., Xiao, G., Zhong, F., Xia, Y., Wu, Y., Graphic C3N4-assisted dispersion of graphene to improve the corrosion resistance of waterborne epoxy coating, Progress in Organic Coatings, 139, 2020, 105448.
[18] Wu, L., Zhang, Z., Yang, M., Yuan, J., Li, P., Guo, F., Men, X., One-step synthesis of g-C3N4 nanosheets to improve tribological properties of phenolic coating, Tribology International, 132, 2019, 221-227.
[19] Golafshani, E.M., Behnood, A., Arashpour, M., Predicting the compressive strength of normal and High-Performance Concretes using ANN and ANFIS hybridized with Grey Wolf Optimizer, Construction and Building Materials, 232, 2020, 117266.
[20] Vakili, M., Yahyaei, M., Ramsay, J., Aghajannezhad, P., Paknezhad, B., Adaptive neuro-fuzzy inference system modeling to predict the performance of graphene nanoplatelets nanofluid-based direct absorption solar collector based on experimental study, Renewable Energy, 163, 2021, 807-824.
[21] Sampath, K.H.S.M., Perera, M.S.A., Ranjith, P.G., Matthai, S.K., Tao, X., Wu, B., Application of neural networks and fuzzy systems for the intelligent prediction of CO2-induced strength alteration of coal, Measurement, 135, 2019, 47-60.
[22] Khalaj, O., Ghobadi, M., Zarezadeh, A., Saebnoori, E., Jirková, H., Chocholaty, O., Svoboda, J., Potential role of machine learning techniques for modeling the hardness of OPH steels, Materials Today Communications, 26, 2021, 101806.
[23] Nesfchi, M.M., Pirbazari, A.E., Saraei, F.E.K., Rojaee, F., Mahdavi, F., Faal Rastegar, S.A., Fabrication of plasmonic nanoparticles/cobalt doped TiO2 nanosheets for degradation of tetracycline and modeling the process by artificial intelligence techniques, Materials Science in Semiconductor Processing, 122, 2021, 105465.
[24] Hosseinzadeh, A., Zhou, J.L., Altaee, A., Baziar, M., Li, X., Modeling water flux in osmotic membrane bioreactor by adaptive network-based fuzzy inference system and artificial neural network, Bioresource Technology, 310, 2020, 123391.
[25] Hernández-Julio, Y.F., Prie6to-Guevara, M.J., Nieto-Bernal, W., Fuzzy clustering and dynamic tables for knowledge discovery and decision-making: Analysis of the reproductive performance of the marine copepod Cyclopina sp, Aquaculture, 523, 2020, 735183.
[26] Ghobadi, M., Zaarei, D., Naderi, R., Asadi, N., Seyedi, S.R., Avard, M.R., Improvement the protection performance of lanolin based temporary coating using benzotriazole and cerium (III) nitrate: Combined experimental and computational analysis, Progress in Organic Coatings, 151, 2021, 106085.
[27] Xu, Y., Zhu, Y., Xiao, G., Ma, C., Application of artificial neural networks to predict corrosion behavior of Ni–SiC composite coatings deposited by ultrasonic electrodeposition, Ceramics International, 40(4), 2014, 5425-5430.
[28] Gan, H., Liu, G., Shi, C., Tang, R., Xiong, Y., Liu, Y., Liu, H., Comparison of three artificial neural networks for predict the electrodeposition of nano-silver film, Materials Today Communications, 26, 2021, 101950.
[29] Li, X., Zhu, Y., Xiao, G., Application of artificial neural networks to predict sliding wear resistance of Ni–TiN nanocomposite coatings deposited by pulse electrodeposition, Ceramics International, 40(8), 2014, 11767-11772.
[30] Yaghoot-Nezhad, A., Moradi, M., Rostami, M., Danaee, I., Khosravi-Nikou, M.R., Dual Z-Scheme CuO-ZnO@ Graphitic Carbon Nitride Ternary Nanocomposite with Improved Visible Light-Induced Catalytic Activity for Ultrasound-Assisted Photocatalytic Desulfurization, Energy & Fuels, 34(11), 2020, 13588-13605.
[31] Moradi, M., Hasanvandian, F., Isari, A.A., Hayati, F., Kakavandi, B., Setayesh, S.R., CuO and ZnO co-anchored on g-C3N4 nanosheets as an affordable double Z-scheme nanocomposite for photocatalytic decontamination of amoxicillin, Applied Catalysis B: Environmental, 285, 2021, 119838.
[32] Franco, D.S., Duarte, F.A., Salau, N.P.G., Dotto, G.L., Analysis of indium (III) adsorption from leachates of LCD screens using artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANIFS), Journal of Hazardous Materials, 384, 2020, 121137.
[33] Zhou, Q., Wang, F., Zhu, F., Estimation of compressive strength of hollow concrete masonry prisms using artificial neural networks and adaptive neuro-fuzzy inference systems, Construction and Building Materials, 125, 2016, 417-426.
[34] Xu, J., Zhao, X., Yu, Y., Xie, T., Yang, G., Xue, J., Parametric sensitivity analysis and modelling of mechanical properties of normal-and high-strength recycled aggregate concrete using grey theory, multiple nonlinear regression and artificial neural networks, Construction and Building Materials, 211, 2019, 479-491.
[35] Reddy, N.S., Krishnaiah, J., Hong, S.G., Lee, J.S., Modeling medium carbon steels by using artificial neural networks, Materials Science and Engineering: A, 508(1-2), 2009, 93-105.
[36] Gupta, T., Patel, K.A., Siddique, S., Sharma, R.K., Chaudhary, S., Prediction of mechanical properties of rubberised concrete exposed to elevated temperature using ANN, Measurement, 147, 2019, 106870.
[37] Sugeno, M., Kang, G.T., Structure identification of fuzzy model, Fuzzy Sets and Systems, 28(1), 1998, 15-33.
[38] Jang, J.S., ANFIS: adaptive-network-based fuzzy inference system, IEEE Transactions on Systems, Man, and Cybernetics, 23(3), 1993, 665-685.
[39] Gerek, I.H., House selling price assessment using two different adaptive neuro-fuzzy techniques, Automation in Construction, 41, 2014, 33-39.
[40] Abadi, S.N.R., Mehrabi, M., Meyer, J.P., Prediction and optimization of condensation heat transfer coefficients and pressure drop of R134a inside an inclined smooth tube, International Journal of Heat and Mass Transfer, 124, 2018, 953-966.
[41] Hasanvandian, F., Moradi, M., Samani, S.A., Kakavandi, B., Setayesh, S.R., Noorisepehr, M., Effective promotion of g–C3N4 photocatalytic performance via surface oxygen vacancy and coupling with bismuth-based semiconductors towards antibiotics degradation, Chemosphere, 287, 2022, 132273.
[42] Xu, J.H., Ye, S., Di Ding, C., Tan, L.H., Fu, J.J., Autonomous self-healing supramolecular elastomer reinforced and toughened by graphitic carbon nitride nanosheets tailored for smart anticorrosion coating application, Journal of Materials Chemistry A, 6(14), 2018, 5887-5898.
[43] Li, C., Cao, C.B., Zhu, H.S., Graphitic carbon nitride thin films deposited by electrodeposition, Materials Letters, 58(12-13), 2004, 1903-1906.
[44] Bai, X., Zong, R., Li, C., Liu, D., Liu, Y., Zhu, Y., Enhancement of visible photocatalytic activity via Ag@ C3N4 core–shell plasmonic composite, Applied Catalysis B: Environmental, 147, 2014, 82-91.
[45] Benea, L., Danaila, E., Celis, J.P., Influence of electro-co-deposition parameters on nano-TiO2 inclusion into nickel matrix and properties characterization of nanocomposite coatings obtained, Materials Science and Engineering: A, 610, 2014, 106-115.
[46] Yasin, G., Khan, M.A., Arif, M., Shakeel, M., Hassan, T.M., Khan, W.Q., Zuo, Y., Synthesis of spheres-like Ni/graphene nanocomposite as an efficient anti-corrosive coating; effect of graphene content on its morphology and mechanical properties, Journal of Alloys and Compounds, 755, 2018, 79- 88.
[47] Beltowska-Lehman, E., Bigos, A., Indyka, P., Chojnacka, A., Drewienkiewicz, A., Zimowski, S., Szczerba, M.J., Optimisation of the electrodeposition process of Ni-W/ZrO2 nanocomposites, Journal of Electroanalytical Chemistry, 813, 2018, 39-51.
[48] Rasooli, A., Safavi, M.S., Babaei, F., Ansarian, A., Electrodeposited Ni–Fe–Cr2O3 nanocomposite coatings: A survey of influences of Cr2O3 nanoparticles loadings in the electrolyte, Journal of Alloys and Compounds, 822, 2020, 153725.
[49] Demir, M., Kanca, E., Karahan, I.H., Characterization of electrodeposited Ni–Cr/hBN composite coatings, Journal of Alloys and Compounds, 844, 2020, 155511.
[50] Ogihara, H., Wang, H., Saji, T., Electrodeposition of Ni–B/SiC composite films with high hardness and wear resistance, Applied Surface Science, 296, 2014, 108-113.
[51] Li, B., Zhang, W., Huan, Y., Dong, J., Synthesis and characterization of Ni-B/Al2O3 nanocomposite coating by electrodeposition using trimethylamine borane as boron precursor, Surface and Coatings Technology, 337, 2018, 186-197.
[52] Zhao, K., Shen, L., Qiu, M., Tian, Z., Jiang, W., Preparation and properties of nanocomposite coatings by pulsed current-jet electrodeposition, International Journal of Electrochemical Science, 12, 2017, 8578-8590.
[53] Maharana, H.S., Mondal, K., Manifestation of Hall–Petch breakdown in nanocrystalline electrodeposited Ni-MoS2 coating and its structure dependent wear resistance behavior, Surface and Coatings Technology, 2021, 410, 126950.
[54] Xue, Z., Lei, W., Wang, Y., Qian, H., Li, Q., Effect of pulse duty cycle on mechanical properties and microstructure of nickel-graphene composite coating produced by pulse electrodeposition under supercritical carbon dioxide, Surface and Coatings Technology, 325, 2017, 417-428.
[55] Smith, G.N., Probability and statistics in civil engineering, Collins Professional and Technical Books, 244, 1986.
[56] Gandomi, A.H., Mohammadzadeh, D., Pérez-Ordóñez, J.L., Alavi, A.H., Linear genetic programming for shear strength prediction of reinforced concrete beams without stirrups, Applied Soft Computing, 19, 2014, 112-120.
[57] Roy, P.P., Roy, K., On some aspects of variable selection for partial least squares regression models, QSAR & Combinatorial Science, 27(3), 2008, 302-313.
[58] Tavana, M., Fallahpour, A., Di Caprio, D., Santos-Arteaga, F.J., A hybrid intelligent fuzzy predictive model with simulation for supplier evaluation and selection, Expert Systems with Applications, 61, 2016, 129-144.
[59] Vellaichamy, B., Periakaruppan, P., Catalytic hydrogenation performance of an in situ assembled Au@ gC 3 N 4–PANI nanoblend: synergistic inter-constituent interactions boost the catalysis, New Journal of Chemistry, 41(15), 2017, 7123-7132.
[60] Liu, L., Qi, Y., Lu, J., Lin, S., An, W., Liang, Y., Cui, W., A stable Ag3PO4@ g-C3N4 hybrid core@ shell composite with enhanced visible light photocatalytic degradation, Applied Catalysis B: Environmental, 183, 2016, 133-141.
[61] Komatsu, T., The first synthesis and characterization of cyameluric high polymers, Macromolecular Chemistry and Physics, 202, 2001, 19–25.
[62] Giannakopoulou, T., Papailias, I., Todorova, N., Boukos, N., Liu Y., Yu, J., Trapalis C., Tailoring the energy band gap and edges’ potentials of g-C3N4/TiO2 composite photocatalysts for NOx removal, Chemical Engineering Journal, 310, 2017, 571-580.
[63] Aal, A.A., Gobran, H.A., Muecklich, F., Electrodeposition of Ni–RuAl composite coating on steel surface, Journal of Alloys and Compounds, 473(1-2), 2009, 250-254.
[64] Khalaj, O., Ghobadi, M., Saebnoori, E., Zarezadeh, A., Shishesaz, M., Mašek, B., Štadler, C., Svoboda, J., Development of Machine Learning Models to Evaluate the Toughness of OPH Alloys, Materials, 14(21), 2021, 6713.