Multivariate optimization methods for corrosion studies based on nanostructured materials

Corrosion studies of nanostructured materials are of increasing importance due to their unique properties compared to traditional materials. However, the corrosion behavior of nanostructured materials is influenced by numerous factors, including nanoscale dimensions, chemical composition, crystal structure, surface morphology, and environmental conditions. Optimizing the corrosion performance of these materials requires the simultaneous investigation and adjustment of these multiple parameters. Multivariate optimization methods provide powerful tools to understand the complex interaction between these factors and find the optimal conditions to achieve maximum corrosion resistance. This paper comprehensively reports the application of multivariate optimization methods in corrosion studies of nanostructured materials. In this regard, first, a brief introduction to nanostructured materials and the importance of investigating their corrosion behavior are given. Then, a variety of multivariate optimization methods, including statistical methods such as design of experiments (DoE) and response surface methodology (RSM) and metaheuristic algorithms such as particle swarm optimization algorithm, are discussed. In the following, the applications of these methods in optimizing various parameters affecting the corrosion of nanostructured materials, including synthesis and fabrication conditions, surface modification, addition of alloying elements, and design of protective coatings, are described by providing case examples from conducted studies. Also, the advantages and limitations of each of the multivariate optimization methods in the field of corrosion studies of nanostructured materials, as well as the challenges and prospects for the use of these materials, are evaluated.

کلیدواژه ها:

Corrosion ، Nanostructures ، Inhibitors ، Multivariate optimization