Picture fuzzy ARASsort-lp for the ideal natural waste sorting for sustainable production of nanofibers via electrospinning

Abstract

Nanofibers are engineering materials with unique architectures. From air blowing to centrifugal spinning or enzymatic treatments, several different techniques have been introduced to produce nanofibers. Among its alternatives, electrospinning is still a widely accepted and applied technique for producing nanofibers. Various raw materials from natural or synthetic sources are used in electrospinning as a polymer or an additive. Of these raw materials, biomass or organic waste-derived materials have become an area of interest due to their availability, cost-performance level, unique functionalities, and rising demand for sustainable, eco-derived, and friendly materials. Either plant-based or animal-based, a great number of materials can be used in electrospinning such as starch, lignocellulose, alginate, keratin, chitosan, etc. Each material has its typical electrospinning conditions some of which bring considerable challenges for successful nanofiber production. To overcome these challenges and find the optimum material type and process conditions, we introduced a novel Picture Fuzzy Multiple Attribute Decision Making (MCDM)-based sorting tool, namely PiF-ARASsort-lp, to classify material alternatives into three categories based on different attributes. The recommended model not only provides cost efficiency but also offers an opportunity to evaluate different materials for many aspects in a quick manner based on expert opinions. To facilitate the opinion-gathering process, a new data collection scheme is applied. With the help of an entropy-based objective attribute weighting procedure under a picture fuzzy environment, 15 waste materials were classified into three groups: higher appropriateness for sustainable electrospinning, moderate appropriateness, and lower appropriateness. The results are discussed, and some managerial and engineering implications are presented.