Study of Microstructure and Corrosion Properties of Electro-Codeposited Ni-SiO<sub>2</sub> Nanocomposite Coatings from Deep Eutectic Solvent (DES) Electrolyte
Bibliographic record
Abstract
Metal matrix composites containing metal and semiconductor or insulator nanoparticles have been fabricated through different methods and mechanical and electrochemical behavior studied [1-8]. Electro-codeposition is a metal matrix nanocomposite coating fabrication method that is used widely due to its superior advantages such as homogeneous coatings, low cost, easy set-up and fast deposition rate [9,10]. Several examples of metal matrix composites are reported in the literature. In the case of nickel-based composites, many of the pure, ceramics and oxides nano/micro-sized particles such as Ti [1], TiO2 [2,3], SiC [4,5], SiO2 [6,7], and Al2O3 [8] are added as the second phase. In this paper, the fabrication of Ni-SiO2 nanocomposite coatings and the influence of the addition of SiO2 nanoparticles on composition, microstructure and corrosion properties of electrodeposited Ni-SiO2 nanocomposite coatings are discussed. A pulse electro-codeposition method was employed to prepare Ni-SiO2 nanocomposite coating on the surface of AISI 1045 medium carbon steel from a choline chloride (ChCl)/ethylene glycol (EG)-based deep eutectic solvent (DES) with a ChCl to EG molar ratio of 1/2. Nickel chloride [NiCl2.6H2O] and 15 g/L silicon dioxide (SiO2) nanopowder, 10-20 nm particle size and 99.5% purity, were then added to the electrolyte. The electro-codeposition process, for pure Ni and Ni-SiO2 nanocomposite coatings, was carried out at the same conditions of temperature, current density and frequency that were set to 70 °C, 8 mA/cm2 and 1000 Hz, respectively. Disc-shaped medium carbon steel substrates were mechanically polished and degreased followed by activation before electro-codeposition. Scanning electron microscopy (SEM), Energy-dispersive X-ray spectroscopy (EDS) and X-ray diffraction (XRD), were used to study microstructure, surface morphology, composition, and phase analysis of the Ni-SiO2 nanocomposite and pure Ni coatings. Corrosion properties of the coatings were evaluated by means of potentiodynamic polarization test in 3.5 wt.% NaCl solution at room temperature. SEM images revealed that the addition of silicon dioxide nanoparticles to the nickel film affects the grain size, microstructure and morphology of the coating surface. The microstructure of the nanocomposite coating is a combination of nano and micro-sized structures on the surface. A granular-like microstructure with a quasi-uniform dispersion of SiO2 nanoparticles in the coating was observed. Our characterization results confirm the incorporation of SiO2 nanoparticles into the Ni film. Potentiodynamic polarization results showed that the incorporation of SiO2 nanoparticles into the Ni film significantly improved the corrosion properties of the coatings compared to the pure nickel. Corrosion potential of Ni-SiO2 nanocomposite coating increased to a more positive value, and the corrosion current density decreased by a factor of 10 compared to that of pure Ni. Enhanced corrosion resistance in nanocomposite coating with respect to the pure Ni is explained by the effect of the addition of SiO2 nanoparticles on both microstructure and composition of the nanocomposite coating. Thus, the electrodeposition method using choline chloride/ethylene glycol-based DES solution can be used to produce Ni-SiO2 nanocomposite coating with uniformly dispersed SiO2 nanoparticles in the Ni film that exhibits clearly better corrosion resistance compared to pure Ni coating and suggesting its potential application in industry with the goal of surface corrosion protection. Keywords: corrosion, nanocomposite, coating, nickel, silicon dioxide, deep eutectic solvent References [1] Lekka, M.; Offoiach, R.; Revelant, F.; Fedrizzi, L. Trans. IMF 2019, 97, 73-81. [2] Mohajeri, S.; Dolati, A.; Ghorbani, M. J. Ultrafine Grained Nanostruct. Mater. 2016, 49, 51-63. [3] Danilov, F. I. et al. Surf. Eng. Appl. Electrochem. 2019, 55, 138–149. [4] Lanzutti, A. et al. Tribol. Int. 2019, 132, 50-61. [5] Zhou, Y.; Xie, F. Q.; Wu, X. Q.; Zhao, W. D.; Chen, X. J. Alloys Compd. 2017, 699, 366-377. [6] Li, R.; Hou, Y.; Liu, B.; Wang, D.; Liang, J. Electrochim. Acta 2016, 222, 1272–1280. [7] Ratajski, T. et al. Mater. Charact. 2018, 142, 478-491. [8] Jiang, S. W.et al. Surf. Coat. Technol. 2016, 286, 197-205. [9] Chen, W. W.; Gao, W.; He, Y. D. Surf. Coat. Technol. 2010, 204, 2493-2498. [10] Tu, W. Y. et al. J. Mater. Sci. 2008, 43, 1102-1108.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".