Temperature sensing is essential for the human body’s interaction with the environment, and electronic skin mimicking human perception is crucial for developing smart wearable devices. Wearable sensors based on conductive polymer composites (CPCs) possess large sensitive, simple, and low-cost preparation characteristics. However, establishing the conductive networks necessitates sufficient filler doping, posing processability and cost control challenges. Herein, we report a susceptible thermo-sensor (TS) that utilizes the secondary polymer thermoplastic polyurethane (TPU) to connect carbon black (CB) particles, facilitating the assembly of a conductive network at low concentrations, thereby improving their electrical conductivity. The TS can defect temperatures in the range of 15 – 45 °C with a sensitivity of 1200 %, a positive temperature coefficient (PTC) intensity of approximately 5, and a response time of less than 10 s. By machine learning to identify the output signal of TS, the recognition accuracy reaches 99.8 %, then the real-time temperature display can be successfully realized. This approach provides a simple preparation method for personalized medicine and soft robotics.

中文翻译：

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基于聚合物-填料相互作用的机器学习支持实时温度响应系统，用于导电网络组装


温度传感对于人体与环境的交互至关重要，而模仿人类感知的电子皮肤对于开发智能可穿戴设备至关重要。基于导电聚合物复合材料 （CPC） 的可穿戴传感器具有制备大、灵敏度高、简单且成本低的特点。然而，建立导电网络需要足够的填料掺杂，这带来了可加工性和成本控制挑战。在此，我们报道了一种敏感的热传感器 （TS），它利用二次聚合物热塑性聚氨酯 （TPU） 连接炭黑 （CB） 颗粒，促进低浓度导电网络的组装，从而提高其导电性。TS 可以在 15 – 45 °C 的温度范围内出现缺陷，灵敏度为 1200 %，正温度系数 （PTC） 强度约为 5，响应时间小于 10 秒。通过机器学习识别 TS 的输出信号，识别准确率达到 99.8 %，然后成功实现实时温度显示。这种方法为个性化医疗和软机器人技术提供了一种简单的制备方法。