• 特约专栏:纺织品可穿戴与智能化 •

石墨烯/碳纳米管功能针织面料的结构与性能

1. 浙江理工大学纺织科学与工程学院,杭州 310018
• 收稿日期:2022-03-25 出版日期:2023-01-10 网络出版日期:2023-01-17
• 通讯作者:邵怡沁,E-mail:syq@zstu.edu.cn
• 作者简介:宋倩倩(1997—),女,浙江瑞安人,硕士研究生,主要从事现代纺织技术在新产品方面的研究。
• 基金资助:
浙江省自然科学基金项目(LZJWY22B070003);浙江理工大学科研启动项目(2020YBZX10);浙江理工大学优秀博士专项(20202094-Y)

Structure and properties of GNs/CNTs functional knitted fabrics

SONG Qianqian, SHAO Yiqin, CHEN Weilai

1. College of Textiles Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China
• Received:2022-03-25 Published:2023-01-10 Online:2023-01-17

Abstract: With the rapid development of electronic intelligent devices, intelligent textiles formed by the combination of electronic device systems and textiles came into being. Electro-thermal fabrics are a kind of intelligent textiles which convert electric energy into thermal energy through electric heating elements. The common heating element materials have some disadvantages, such as poor flexibility, limited heating temperature range and high power consumption. Among electro-thermal materials, the graphene nanosheets (GNs) and carbon nanotubes (CNTs) with excellent electrical properties enhance their thermal properties. For instance, the heat dissipation speed is fast, the ambient temperature is greatly increased, and the electrothermal conversion rate is nearly 100% with no luminous loss. Besides, the probability of power decay in the long-term use process is low, and the heating power is stable. Wearable sensors need to contact the human body directly or indirectly like existing textile fabrics, and the deformation range of human joints in daily activities is usually 3%-55%, which requires satisfactory elongation and deformability of the sensor. At present, most of the research on conductive heating fabrics is based on woven fabrics. On the contrary, the research on knitted structure with good flexibility is not deep enough. Knitted fabrics have high elasticity, high flexibility and recoverability because of its unique coil structure. They can achieve both high sensitivity and large deformation.
Polyester knitted fabrics with the three different structures of weft plain weave, mesh weave and spacer weave were selected and GNs/CNTs functional knitted fabrics were prepared by the safe and simple spraying method. The surface morphology structure and mechanical properties of the fabrics were characterized by the Zeiss polarizing microscope, SEM and FTIR. The electrical properties and tensile strain sensing properties of the fabrics were compared and analyzed by using the electronic fabric strength meter and the two-probe digital multimeter, and the electro-thermal properties of the fabrics under different applied voltage modes were studied systematically by infrared thermal imager. The results show that GNs/CNTs uniformly adheres to the surface of the fiber and yarn in the state of intertwining and interlocking without forming a film on the surface of the fabric, and the fabric structure is clearly visible; GNs/CNTs functional knitted fabrics exhibit improved mechanical properties and satisfactory electrical properties and the electrical conductivity of the GNs/CNTs mesh fabric reaches 1,567 S/cm; at low voltages, GNs/CNTs compound knitted fabrics exhibit certain electro-thermal properties, which is related to the structure of fabrics. To be specific, weft plain knitted fabrics have the fastest heating rate and highest heating efficiency, and mesh fabrics can be heated to 116.3 ℃ at 10 V. Compared with the weft plain knitted fabric with a highest heating rate of 15.3 ℃/s, the temperature rising and cooling rates of the mesh and spacer fabrics are relatively low. At the same power, the average heating efficiency of the weft plain knitted fabric can reach 210.5 ℃/W, which is higher than that of the mesh fabric and spacer fabric.
The preparation method of GNs/CNTs functional knitted fabrics is simple and effective and the knitted fabrics with different structures can meet the multi-scene applications in intelligent clothing, health care and other fields. The research results can provide references and suggestions for the design and development of intelligent textiles.