Fabrication and evaluation of regenerated cellulose/nanoparticle fibers from lignocellulosic biomass

被引:9
作者
Chen, Jonathan Y. [1 ]
Sun, Liangfeng [1 ]
Negulescu, Ioan I. [2 ]
Xu, Bugao [1 ]
机构
[1] Univ Texas Austin, Austin, TX 78712 USA
[2] Louisiana State Univ, Baton Rouge, LA 70803 USA
关键词
Bagasse; Cellulose; Nanoparticle; Cellulose/nanoparticle polymer; Regenerated celloluse fiber; SEM image analysis; METHYLMORPHOLINE-N-OXIDE; PHYSICAL-PROPERTIES; CELLULOSE FIBERS; LYOCELL FIBERS; BAGASSE;
D O I
10.1016/j.biombioe.2017.03.024
中图分类号
S2 [农业工程];
学科分类号
0828 ;
摘要
This paper presents a new approach for converting lignocelluloses biomass into regenerated cellulose fibers. A major research objective is to examine the effectiveness of nanoparticles as cellulose fillers on improving thermal and mechanical properties of the regenerated cellulose fibers. Pure wood and bagasse cellulose solutions and cellulose/nanoparticle composite solutions were prepared using N-methyl-morpholine-N-oxide (NMMO) solvent, and were spun into filament fiber through an extruding device. The thermal analysis methods of TGA and DSC were used for characterizing the solution properties. The regenerated bagasse cellulose fiber was also compared with regenerated wood cellulose fiber and nanoparticle/cellulose fibers in terms of mechanical strength and elongation. Distribution of nano particles in the cellulose matrix was evaluated using a method of image analysis for fiber photomicrographs. Tested results indicated that the tensile strength of the regenerated bagasse cellulose fiber was considerably lower than that of the regenerated wood cellulose fiber but the elongation was consistent between these two fibers. The research also revealed that adding a small amount of nanoparticles to fill in the wood cellulose matrix resulted in an increase of the cellulose fiber tensile strength and modulus by 14% and 6% respectively, and a decrease of cellulose glass transition temperature. The nanoparticle distribution in the cellulose matrix was correctly detected by a computing image technique based on a mean shift method. (C) 2017 Elsevier Ltd. All rights reserved.
引用
收藏
页码:1 / 8
页数:8
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