1成果简介

　　随着便携式电子设备的快速发展，智能可穿戴技术在个性化运动追踪、健康监测及人机交互领域展现出巨大潜力。然而，复杂的制备工艺和严苛的性能要求阻碍了这些技术的广泛应用。为解决这一问题，本文，中国计量学院郭驾宇等研究人员在《ChemistrySelect》期刊发表名为“Conductive Thermoplastic Polyurethane Composites with Multicarbon Fillers for Flexible Resistive Strain Sensors for Handwriting Recognition”的论文，研究提出一种基于热塑性聚氨酯多孔薄膜制备柔性电阻式应变传感器的简便经济方法。通过非溶剂诱导相分离法，作者制备了含不同比例炭黑/石墨烯与炭黑/碳纳米纤维的孔隙薄膜，作为应变传感器的导电应变敏感层。
　　这种简单高效的制备方法不仅降低了生产成本，还提升了传感器的可制造性和性能稳定性，为智能技术未来发展提供了重要支撑。所得柔性电阻式应变传感器展现出卓越的传感性能：最大应变系数达9.822，拉伸范围广（ε=0%~30%），且具有优异的循环拉伸可靠性。应用测试表明，该传感器在手写识别分类任务中表现优异，为智能健康监测与虚拟现实交互等领域的多元化传感器技术应用奠定了坚实基础。
　　2图文导读 

　　图1、The schematic diagram of the preparation process for the sensitive porous conductive TPU layer for use in resistive strain sensors.

　　图2、The test procedure of real-time resistance changes of TPU films during the stretching process.

　　图3、Surface and cross-sectional SEM images of CB and CNF TPU films at different mass ratios. CB: CNF = a, b) 1: 0, c, d) 5:1, e, f) 3.5:1, and g, h) 2:1.

　　图4、The relationship between strain and change in relative resistance: a) different CB/Graphene ratios, b) different CB/CNF ratios.

　　图5、Conductive mechanism diagram of the TPU films composed of CB/graphene or CB/CNF.

　　图6、a) Changes in sensitivity of CB: Graphene = 1:1 sensor after 6–10 cycles, b) Changes in sensitivity of CB: CNF = 2:1 sensor after 6–10 cycles, c) Relative resistance change of CB: Graphene = 1:1 sensor after 1000 cycles of reciprocity.

　　图7、Application of strain sensors. a) The process of handwritten digit's acquisition using resistive strain sensor, b) change in relative resistance associated with the handwritten digit “0” over time, c) change in relative resistance associated with the handwritten digit “1” over time, d) change in relative resistance associated with handwritten digit “2” over time, e) change in relative resistance associated with handwritten digit “3” over time, f) A percentage heatmap representing the accuracy of the RF model, g) A percentage heatmap representing the accuracy of the XGB model.
　　3小结
　　应变传感器采用由碳黑（CB）、石墨烯、碳纳米纤维（CNF）和热塑性聚氨酯（TPU）组成的导电TPU薄膜构建，形成敏感电阻层，该层构成了三维导电网络。通过非溶剂诱导相分离法在TPU薄膜内部形成多孔结构，实现了敏感层的构建。该应变传感器融合高效协同导电网络与多孔微结构，具备高灵敏度特性：最大应变系数达9.822，拉伸范围广（ε=0%–30%），且循环稳定性优异。值得注意的是，实验证实该传感器在书写过程中具有优异的电阻输出响应性和稳定性，随机森林模型预测的分类准确率达98.61%，XGBoost模型预测值为98.50%。综上所述，该柔性电阻应变传感器基于三维导电网络与多孔敏感层结构，融合了协同导电网络与多孔结构的优势，兼具高灵敏度与宽拉伸范围特性。所提出的传感器在可穿戴领域具有广阔的应用前景与潜力。
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