摘要
采用2,2,6,6-四甲基哌啶-1-氧自由基(TEMPO)氧化法制备了不同羧基含量的纳米纤维素(CNF),并将其用作碳纳米管(CNTs)的分散剂,通过超声、离心处理制备出稳定均一的CNF-CNTs分散液,然后通过朗伯-比尔定律测定CNF-CNTs分散液中CNTs的浓度,研究了不同CNF羧基含量对CNTs的分散效果。此外,利用静电纺丝法制备出柔性、多孔的热塑性聚氨酯(TPU)薄膜作为基体,以CNF-CNTs分散液作为导电填料,通过真空抽滤法将CNF-CNTs负载于TPU多孔膜上,制备出CNF-CNTs/TPU复合薄膜,并探究了不同CNF羧基含量对CNF-CNTs/TPU复合薄膜应变响应性能的影响规律。结果表明,羧基含量对CNF的分散性能具有重要影响。随着CNF羧基含量的提高,CNF对CNTs分散效果越好,CNF-CNTs/TPU复合薄膜具有更大的应变响应范围。当CNF羧基含量为1.698 mmol/g时,CNF-CNTs/TPU复合薄膜的应变响应范围高达507%,灵敏度系数为335,表现出优异的应变响应性能。
The 2,2,6,6-Tetramethylpiperidine-1-oxyl radical(TEMPO)oxidation was used for preparation of cellulose nanofibers(CNF)with different carboxyl contents.Then the prepared CNF was used as the dispersing agent to disperse carbon nanotubes(CNTs)and the concentration of CNF-CNTs dispersion was measured by Lambert-Beer’s law to study the dispersion effect of CNF with different carboxyl contents.In addition,the CNF-CNTs/thermoplastic polyurethanes(TPU)composite film was prepared by pumping CNF-CNTs fillers in the prepared electrospun TPU film through vacuum filtration.The influence of carboxyl content of CNF on the strain sensitive performance of CNF-CNTs/TPU composite film was investigated.The result shows that,with the increase of the carboxyl content of CNF,the CNF has a better dispersion effect on CNTs,and the prepared CNF-CNTs/TPU composite film possesses a larger workable strain range.When the carboxyl content of CNF achieves 1.698 mmol/g,the CNF-CNTs/TPU composite film displays a large workable strain range of 507%and a high gauge factor of 335,exhibiting excellent strain sensitive performance.
作者
欧华杰
陈港
朱朋辉
魏渊
李方
OU Huajie;CHEN Gang;ZHU Penghui;WEI Yuan;LI Fang(State Key Laboratory of Pulp and Paper Engineering,South China University of Technology,Guangzhou 510640,China)
出处
《复合材料学报》
EI
CAS
CSCD
北大核心
2020年第11期2735-2742,共8页
Acta Materiae Compositae Sinica
基金
国家重点研发计划项目(2018YFC1902102)
国家工业和信息化部重点行业绿色制造系统集成项目(Z135060009002)
制浆造纸工程国家重点实验室团队项目(2017ZD01)。
关键词
纳米纤维素
碳纳米管
热塑性聚氨酯
复合薄膜
应变响应性能
cellulose nanofiber
carbon nanotubes
thermoplastic polyurethane
composite films
strain sensitive performance