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- W4386241946 endingPage "18904" @default.
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- W4386241946 abstract "Personal wearable devices are considered important in advanced healthcare, military, and sports applications. Among them, e-textiles are the best candidates because of their intrinsic conformability without any additional device installation. However, e-textile manufacturing to date has a high process complexity and low design flexibility. Here, we report the direct laser writing of e-textiles by converting raw Kevlar textiles to electrically conductive laser-induced graphene (LIG) via femtosecond laser pulses in ambient air. The resulting LIG has high electrical conductivity and chemical reliability with a low sheet resistance of 2.86 Ω/□. Wearable multimodal e-textile sensors and supercapacitors are realized on different types of Kevlar textiles, including nonwoven, knit, and woven structures, by considering their structural textile characteristics. The nonwoven textile exhibits high mechanical stability, making it suitable for applications in temperature sensors and micro-supercapacitors. On the other hand, the knit textile possesses inherent spring-like stretchability, enabling its use in the fabrication of strain sensors for human motion detection. Additionally, the woven textile offers special sensitive pressure-sensing networks between the warp and weft parts, making it suitable for the fabrication of bending sensors used in detecting human voices. This direct laser synthesis of arbitrarily patterned LIGs from various textile structures could result in the facile realization of wearable electronic sensors and energy storage." @default.
- W4386241946 created "2023-08-30" @default.
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- W4386241946 date "2023-08-29" @default.
- W4386241946 modified "2023-10-18" @default.
- W4386241946 title "Multimodal E-Textile Enabled by One-Step Maskless Patterning of Femtosecond-Laser-Induced Graphene on Nonwoven, Knit, and Woven Textiles" @default.
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- W4386241946 doi "https://doi.org/10.1021/acsnano.3c04120" @default.
- W4386241946 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37643475" @default.
- W4386241946 hasPublicationYear "2023" @default.
- W4386241946 type Work @default.