4.8 Article

Multiscale Disordered Porous Fibers for Self-Sensing and Self-Cooling Integrated Smart Sportswear

期刊

ACS NANO
卷 14, 期 1, 页码 559-567

出版社

AMER CHEMICAL SOC
DOI: 10.1021/acsnano.9b06899

关键词

multiscale disordered porous structure; smart clothing; thermal management; infrared radiation transparency; tensile strain sensor; temperature sensor

资金

  1. Natural Science Foundation of China [51672141, 51306095]
  2. Natural Science Foundation of Shandong Province of China [ZR2018QEM004]
  3. Research and Development Program of Shandong Province of China [2019GGXI02022, 2019JZZY010340, 2019JZZY010335]

向作者/读者索取更多资源

Smart clothing has demonstrated potential applications in a wide range of wearable fields for human body monitoring and self-adaption. However, current wearable sensors often suffer from not seamlessly integrating with normal clothing, restricting sensing ability, and a negative wearing experience. Here, integrated smart clothing is fabricated by employing multiscale disordered porous elastic fibers as sensing units, which show the capability of inherently autonomous self-sensing (i.e., strain and temperature sensing) and self-cooling. The multiscale disordered porous structure of the fibers contributes to the high transparency of mid-infrared human body radiation and backscatter of visible light, which allows the microenvironment temperature between the skin and clothing to drop at least similar to 2.5 degrees C compared with cotton fabrics. After the capillary-assisted adsorption of graphene inks, the modified porous fibers could also possess real-time strain and temperature-sensing capacities with a high gauge factor and thermal coefficient of resistance. As a proof of concept, the integrated smart sportswear achieved the measuring of body temperature, the tracking of large-scale limb movements, and the collection of subtle human physiological signals, along with the intrinsic self-cooling ability.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.8
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据