Cellulose nanofibrils and nano-scaled titanium dioxide-reinforced biopolymer nanocomposites: Selecting the best nanocomposites with multicriteria decision-making methods

Abstract

The aim of the paper is to determine the effects of nano fillers such as cellulose nanofibrils and nano-scaled titanium dioxide on some properties of polyhydroxybutyrate and polylactic acid biopolymers; it also determined the selection of biopolymer nanocomposites with the optimum properties by using multicriteria decision-making methods such as multi-attribute utility theory, simple additive weighting, and weighted aggregated sum product assessment. Test results showed that the mechanical properties of the biopolymer nanocomposites generally increased with the addition of the cellulose nanofibrils and nano-scaled titanium dioxide. However, the addition of nano-scaled titanium dioxide decreased the tensile modulus. The addition of the cellulose nanofibrils had a higher effect on the tensile and flexure modulus of elasticity than the addition of the nano-scaled titanium dioxide. Thermal properties were generally found to improve with the addition of the cellulose nanofibrils and nano-scaled titanium dioxide. Melting temperature (Tm) generally decreased with the addition of the nano fillers. The scanning electron microscopic images showed that the nano fillers were dispersed as white dots in the biopolymer matrix. After accelerated weathering and decay test, outdoor performance of the biopolymer nanocomposites was found to be improved with the addition of the nano fillers. Multicriteria decision-making methods were conducted to determine the biopolymer nanocomposites having the optimum properties, and all the methods showed that the best biopolymer nanocomposites was polylactic acid with 1% cellulose nanofibrils.

Taguchi-grey relational analysis in parameter optimisation of green biopolymer composites

“…As a result, the addition of the fillers generally improved the Izod IS. The CNF and n–TiO 2 nano-fillers generally increased the impact strength due to actions of the fillers, including CNF and n–TiO 2 as a reinforcing agent in the matrice as determined by Mofokeng and Luyt [44], Gumus et al [45], Silva et al [46], Aydemir and Gardner [22].

Figure 2 Mechanical properties of the BNCs.

Figure 3 SEM images of the BNCs.

…”

Investigation of the Success of Natural Regeneration Fields by Multi-Criteria Decision Making Technique

“…In particular, it is very suitable for measuring the relative intensities of different criteria in terms of representing the average inside information conveyed in decision making (Shannon, 2001;Zeleny, 2012;Huang et al 2021). The method generally consists of the following stages (Wang 2009;Özdağoğlu et al 2017;Gumus et al 2020;Zhao et al 2020;Ayçin and Güçlü 2020;Imren et al 2021;Kurt et al 2021, Kelleci et al 2022.…”

Selection of Superior Clones by the Multi-Dimensional Decision-Making Techniques in Scots Pine Seed Orchard

“…The procedures for the entropy method are specified in steps. D matrix is the decision matrix with m alternatives and n criteria [19][20][21][22][23][24];…”

“…Multi-Attribute Utility Theory (MAUT), which is a very useful method for intuitive formulating and decision making problems, is one of the multi-criteria decision making methods that was started to be used by Fishburn [25] and Keeney and Raiffa [26]. The MAUT approach can be summarized as follows [7,24].…”