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Assessing corrosion resistance of 2D nanomaterial-based coatings on stainless steel substrates


Mujib, Shakir Bin; Mukherjee, Santanu; Ren, Zongkan; Singh, Gurpreet (2020), Assessing corrosion resistance of 2D nanomaterial-based coatings on stainless steel substrates, Dryad, Dataset,


Two dimensional (2D) materials have elicited considerable interest in the past decade due to a diverse array of novel properties ranging from high surface to mass ratios, a wide range of band gaps (insulating boron nitride to semiconducting transition metal dichalcogenides), high mechanical strength and chemical stability. Given the superior chemo-thermo-mechanical properties, 2D materials may provide transformative solution to a familiar yet persistent problem of significant socio-economic burden: the corrosion of stainless steel (SS). With this broader perspective, we investigate corrosion resistance properties of stainless steel coated with 2D nanomaterials; molybdenum disulfide (MoS2), boron nitride (BN), bulk graphite in 3.5 wt. % aqueous NaCl solution. The nanosheets were prepared by a novel liquid phase exfoliation technique and the coatings were made by a paint brush to achieve uniformity.  Open circuit potential (OCP) and potentiodynamic plots indicate the best corrosion resistance is provided by the MoS2 and the BN coatings. Superior performance of the coating is attributed low electronic conductivity, large flake size, and uniform coverage of SS substrate, which likely impeded the corrosive ions from the solution to diffuse through the coating.


Surface morphologies of the bare and coated SS samples were observed using a Carl Zeiss™ EVO MA 10 scanning electron microscope (SEM). Energy dispersive spectra (EDS) were collected using a Zeiss Gemini scanning electron microscope (SEM) at 10–30 keV. To observe layered electrode morphology, a focused Ga+ ion beam (Zeiss Auriga FIB-SEM) was used. For electrochemical/corrosion testing purposes, a 3-electrode set-up was used. This set-up was connected to a CHI 660E electrochemical workstation.



National Science Foundation, Award: 1454151