丝网印刷碳/导电聚合物复合油墨，带有高性能纺织传感器智能手套

摘要：基于柔性应变传感器的智能纺织品在可穿戴设备中有着广阔的应用前景。然而，现有的大多数智能纺织品都存在制造工艺复杂、对导电材料的图案化和均匀沉积控制不足等问题，这极大地阻碍了它们的商业化。本文，武汉大学吴伟教授团队在《ACS Appl. Mater. Inter

1成果简介

基于柔性应变传感器的智能纺织品在可穿戴设备中有着广阔的应用前景。然而，现有的大多数智能纺织品都存在制造工艺复杂、对导电材料的图案化和均匀沉积控制不足等问题，这极大地阻碍了它们的商业化。本文，武汉大学吴伟教授团队在《ACS Appl. Mater. Interfaces》期刊发表名为“Screen-Printing of Carbons/Conductive Polymer Composite Inks for Smart Glove with High-Performance Textile Sensors”的论文，研究利用三种不同导电成分的协同效应，提出了一种三元复合墨水体系（石墨烯纳米颗粒/炭黑/PEDOT:PSS ，G-C-P墨水）。该体系具有优异的流变特性，可通过高分辨率丝网印刷将图案传感器均匀沉积在纺织品基底上。

三元导电材料的协同作用克服了单一/双元材料的局限性，使应变传感器具有超高的灵敏度（155-200% 应变时的测量系数 = 1628）、宽广的工作范围（0-200% 应变）和强大的耐用性（大于 5000 次循环）。此外，还集成了基于银分形树枝状突起的可拉伸互连器件，以扩展传感器阵列。传感器和互连器件都直接丝网印刷在纺织品上，实现了与工业纺织品制造工艺的无缝兼容。与印刷电路板的集成使智能纺织手套得以实现，在手势识别和物体抓取识别方面的应用前景广阔。这项工作为高性能智能纺织品建立了一种可扩展的制造模式，为智能可穿戴纺织系统的商业化提供了新的可能性。

2图文导读

图1. (a) The fabrication process of strain sensors. Schematic of the (b) composition and (c) application of the smart glove.

图2. Comparison of strain sensors prepared with different ink formulations and exploration of sensing mechanisms. (a) Relative resistance change of GSS, GCSS, and GCPSS within 200% strain range. (b) Relative resistance change curves for GSS, GCSS, and GCPSS at 5% strain gradient stretching for each 5 s. (c) Relative resistance change for GCPSS printed with 1 to 5 layers within 200% strain range. (d) SEM images of original textile at 0%, 50%, and 100% strain states, respectively. (e) SEM images of the GCPSS surface at 0%, 50%, and 100% strain states, respectively. (f) Schematic representation of the changes in the internal structure of the GSS, GCSS and GCPSS under small and large strain.

图3. Sensing performance of GCPSS. (a) Relative resistance of GCPSS over a range of 0–200% tensile strain. The inset shows the relative resistance change within a strain range of 0–60%. (b) Variation in relative resistance of GCPSS at 60% strain for different strain rates (1 mm s–1, 2 mm s–1, 3 mm s–1, 4 mm s–1, and 5 mm s–1). (c) The relative resistance changes during five continuous loading and unloading cycles at different strains (10%, 20%, 30%, 40%, and 50%). The inset shows the change in relative resistance for small strains (1%, 2%, 3%, 4%, and 5%). (d) Relative resistance change curves of GCPSS remain unchanged after stretching to 30%, 60%, and 90% strain remain unchanged. (e) Response time and recovery time of GCPSS under 1% strain at a strain rate of 60 mm s–1. (f) Relative change in resistance of GCPSS with 10 cycles of loading and unloading at 0.1% strain. (g) Relative resistance variation curves of the GCPSS under repeated stretching and releasing at 50% strain for 5000 cycles at a speed of 2 mm s–1. (h) Zoomed-in views of cycling curves at the initial, middle, and final stages of the 5,000-cycle test. (i) Comparison of the performance with the GF and the strain range of the textile-based flexible strain sensors reported in the relevant literature in recent years.

图4. GCPSS action detection in various regions of the human body. GCPSS fixed to finger (a), wrist (b), and elbow (c) collect signals of changes in relative resistance at different angles of bending. (d) Changes in the relative resistance signal of repeated shoulder lift/drop. (e) Changes in the relative resistance of the knee at different angles of bending. (f) Relative resistance change curves of GCPSS fixed to the waist during repeated stretching exercises. (g) Relative resistance change profiles during nodding and simulated drowsy head lowering (gradual neck flexion). (h) Relative resistance curve of facial muscle activity during chewing and smiling. (i) Relative resistance change curves of GCPSS fixed to the throat when the letters A and O are pronounced.

图5. Smart glove components and applications. (a) Photograph of the smart textile glove. (b) Modular architecture of the PCB. (c) Clenched fist posture with synchronized signal display on the mobile interface. (d) The schematic illustration of the overall composition of smart gloves and the transmission principle of signals. (e) Schematic of American Sign Language (ASL) gestures. (f) Photographs and signal curves of the numbers 1–4, the alphabets of C, E, and K, and the phrases of “I Love You” and “Bye”. (g) Five sign languages using fingers and wrists and corresponding signal curves. (h) Schematic of object grasp and photographs of grasping four different objects and corresponding signal curves.

3小结

综上所述，这项研究通过三种成分（石墨烯纳米片/炭黑/PEDOT:PSS）之间的协同作用，展示了一种高性能三元复合导电油墨，并通过丝网印刷在纺织品基底上制作了柔性应变传感器。研究结果表明，该传感器具有卓越的传感能力：在 0-200% 应变范围内稳定工作、超高灵敏度（155-200% 应变范围内 GF = 1628）、在 50% 应变范围内经过 5000 次加载/卸载循环后输出稳定信号以及快速响应时间（218 毫秒）。此外，应变传感器可固定在不同的身体部位，以检测在大应变和小应变下产生的运动信号，这证明了其在人体运动检测方面的潜力。通过将六个应变传感单元与印刷电路板集成，开发出了一种可穿戴智能手套系统，该系统在执行20多种手语手势和抓握各种形状的物体时都能产生可分辨的信号。这项工作为开发高性能智能纺织品提供了一种新策略，在智能医疗、人机交互和柔性机器人等领域具有广阔的应用前景。

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