绿色生物聚合物复合材料参数优化中的田口灰关联分析

IF 2.1 4区材料科学 Q3 MATERIALS SCIENCE, COMPOSITES

Plastics, Rubber and Composites Pub Date : 2023-04-19 DOI:10.1080/14658011.2023.2202865

Y. Çabuk, Havva Gumuş, D. Aydemir, R. Kurt, E. İmren, Ertuğrul Altuntaş, Zeynep Eda Ozan

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引用次数: 0

摘要

摘要研究了聚羟基丁酸酯(PHB)和聚乳酸(PLA)在不同纤维素纳米原纤维(CNF)和纳米二氧化钛(n-TiO2)添加量(0.5、1和2%)下的共混物及其纳米复合共混物(bnc)的力学性能，并利用田chi灰色关联和回归分析等统计技术对bnc的参数进行了优化。结果表明，在PHB中加入PLA提高了共混物的力学性能，且PLA的加入量越大，力学性能越好。n-TiO2的增强提高了共混物的力学性能;然而，共混物中CNF的存在普遍降低了共混物的力学性能。灰色关联分析表明，最佳的bnc为75% PHB /25% PLA + 0.5% CNF。然而，Taguchi分析发现，75% PHB / 25% PLA加2% n-TiO2具有最佳的力学性能。

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

ABSTRACT This study investigated the mechanical properties of blends and their nanocomposite blends (BNCs) of polyhydroxybutyrate (PHB) and polylactic acid (PLA) at different loadings (0.5, 1, and 2%) of cellulose nanofibrils (CNF) and nano titanium dioxide (n–TiO2), and parameter optimisation of the BNCs was conducted using a statistical technique such as Taguchi-grey relational and regressional analysis. The results showed that adding PLA to PHB increased the mechanical properties of the blends, and the mechanical properties increased more with the high loading of PLA. The reinforcement with n–TiO2 improved the mechanical properties of the blends; however, the presence of the CNF in the blends generally decreased the mechanical properties. The grey relational analysis showed that the best BNCs were 75% PHB /25% PLA with 0.5% CNF. However, Taguchi analysis found that 75% PHB / 25% PLA with 2% n–TiO2 has the best mechanical properties.

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来源期刊

Plastics, Rubber and Composites 工程技术-材料科学：复合

CiteScore

4.10

自引率

0.00%

发文量

审稿时长

4 months

期刊介绍： Plastics, Rubber and Composites: Macromolecular Engineering provides an international forum for the publication of original, peer-reviewed research on the macromolecular engineering of polymeric and related materials and polymer matrix composites. Modern polymer processing is increasingly focused on macromolecular engineering: the manipulation of structure at the molecular scale to control properties and fitness for purpose of the final component. Intimately linked to this are the objectives of predicting properties in the context of an optimised design and of establishing robust processing routes and process control systems allowing the desired properties to be achieved reliably.