journal article·2024-09Open access

Effective multifunctional coatings with polyvinylpyrrolidone-enhanced ZIF-67 and zinc iron layered double hydroxide on microarc oxidation treated AZ31 magnesium alloy

Mohammad Aadil, Ananda Repycha Safira, Arash Fattah-alhosseini, Mohammad Alkaseem, Mosab Kaseem

Journal of Magnesium and Alloys· Volume 12· Issue 9

Abstract

Modulating metal-organic framework's (MOF) crystallinity and size using a polymer, in conjunction with a high surface area of layered double hydroxide, yields an effective strategy for concurrently enhancing the electrochemical and photocatalytic performance. In this study, we present the development of an optimized nanocomposite, denoted as 0.5PVP/ZIF-67, developed on AZ31 magnesium alloy, serving as an efficient and durable multifunctional coating. This novel strategy aims to enhance the overall performance of the porous coating through the integration of microarc oxidation (MAO), ZnFe LDH backbone, and ZIF-67 formation facilitated by the addition of polyvinylpyrrolidone (PVP), resulting in a three-dimensional, highly efficient, and multifunctional material. The incorporation of 0.5 g of PVP proved to be effective in the size modulation of ZIF-67, which formed a corrosion-resistant top layer, improving the total polarization resistance (R p = 8.20 × 10 8 ). The dual functionality exhibited by this hybrid architecture positions it as a promising candidate for mitigating environmental pollution, degrading 97.93 % of Rhodamine B dye in 45 min. Moreover, the sample displayed exceptional degradation efficiency (96.17 %) after 5 cycles. This study illuminates the potential of nanocomposites as electrochemically stable and photocatalytically active materials, laying the foundation for the advancements of next-generation multifunctional frameworks.
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Research context

OpenAlex topics: Layered Double Hydroxides Synthesis and Applications · Magnesium Oxide Properties and Applications · Magnesium Alloys: Properties and Applications

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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