Sustainable selection of microwave absorbing materials: A green evaluation under interval-valued intuitionistic fuzzy environment

Abstract

Environmentally friendly technology is being used by industries all over the world, and engineers in the manufacturing and materials industry are embracing sustainable business models. In this paradigm, materials are processed using economically and environmentally sound methods. The use of microwave-absorbing materials (MAMs) in low-altitude observatory aircraft and the rise in electromagnetic pollution have brought them to light. The main aim of this study is to select an ideal MAM with excellent physical, electromagnetic, chemical, and thermal properties, which also fulfills sustainability aspects based on expert judgments. In this regard, we have proposed a new hybrid framework consisting of Modified Digital Logic (MDL), a subjective weighting method in combination with the measurement of alternatives and ranking according to compromise solution (MARCOS) under an interval-valued intuitionistic fuzzy (IVIF) environment to select an optimum MAM. Furthermore, this research work contributes to streamlining the selection process by consolidating the plethora of work available in the literature on the synthesis and characterization of MAMs. A database is created for 160 potential candidate materials in C, S, X, and Ku bands for carbon-based materials, including carbon nanotubes (CNT), graphene, reduced graphene oxide (rGO), carbon fibers, and biomass-derived materials. These materials are then passed through successive screening stages to shortlist 14 materials, which are ranked subsequently over a set of 15 crisp and ambiguous criteria. This comprehensive study simultaneously caters to quantitative and qualitative information extracted from experimental work, material resource packs, or expert evaluations. The findings highlight CNT/Fe (20 wt%, E) (Al1) as the most suitable candidate for MAM application with outstanding electromagnetic properties. Finally, the results are compared with extant approaches to check the reliability of the proposed framework. In addition, sensitivity analysis is carried out to establish the feasibility and robustness of the obtained results.