We report knitted fabrics made from highly conductive stretchable fibers. The maximum initial conductivity of fibers synthesized by wet spinning was 17460 S cm(-1) with a rupture tensile strain of 50%. The maximum strain could be increased to 490% by decreasing the conductivity to 236 S cm(-1). The knitted fabric was mechanically and electrically reversible up to 100% tensile strain when coated by poly(dimethylsiloxane). The normalized resistance of the poly(dimethylsiloxane)-coated fabric decreased to 0.65 at 100% strain.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1021/nl404801t | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Metastability of a Periodic Network of Threads: Shapes of a Knitted Fabric.
Phys Rev Lett
December 2024
CNRS, ENS de Lyon, LPENSL, UMR5672, 69342, Lyon cedex 07, France.
Knitted fabrics are metamaterials with remarkable mechanical properties, such as extreme deformability and multiple history-dependent rest shapes. This Letter shows that those properties may stem from a continuous set of metastable states for a fabric free of external forces. This is evidenced through experiments, numerical simulations, and analytical developments.
View Article and Find Full Text PDFAdvanced Bioinspired Personal Thermoregulation Textiles for Outdoor Radiative Cooling.
ACS Appl Mater Interfaces
January 2025
School of Energy and Environment, City University of Hong Kong, Kowloon 999077, Hong Kong.
Article Synopsis
- Researchers developed a radiative cooling textile (PAC@T) inspired by flamingo feathers, using polyacrylonitrile and alumina particles to enhance cooling and comfort.
- PAC@T achieves high solar reflectance (95%) and mid-infrared emissivity (91.8%), resulting in effective cooling that is 6.1°C cooler than traditional textiles.
- The textile is made from common materials and offers advantages like durability and energy-free operation, posing significant potential for future industrial applications in personal thermoregulation.
Optimizing washing conditions for smart fabrics: a comprehensive study.
RSC Adv
December 2024
Department of Human Ecology, Korea National Open University Seoul 03087 Republic of Korea
This study aimed to determine optimal washing and drying methods for maintaining the functionality of silver-coated conductive knitted fabrics, commonly used in wearable smart products. By investigating changes in the physical, chemical, and electrical properties of these fabrics under various care conditions, we sought to provide recommendations for their proper maintenance. Results showed that mechanical friction during washing, combined with the chemical effect of detergent and the effects of machine drying, led to peeling and oxidation of the silver layer, resulting in changes to the fabric's appearance, color and increased surface resistance.
View Article and Find Full Text PDFHaptiknit: Distributed stiffness knitting for wearable haptics.
Sci Robot
December 2024
CHARM Laboratory, Stanford, CA, USA.
Haptic devices typically rely on rigid actuators and bulky power supply systems, limiting wearability. Soft materials improve comfort, but careful distribution of stiffness is required to ground actuation forces and enable load transfer to the skin. We present Haptiknit, an approach in which soft, wearable, knit textiles with embedded pneumatic actuators enable programmable haptic display.
View Article and Find Full Text PDFAn Improved Neural Network Model Based on DenseNet for Fabric Texture Recognition.
Sensors (Basel)
December 2024
College of Science & Technology, Ningbo University, Ningbo 315300, China.
Article Synopsis
- The accuracy of fabric texture identification is essential for automating knitted garment production and ensuring quality control, but traditional manual methods are inefficient and subjective.
- To improve this, a new model called DLW-DenseNet is introduced, enhancing the DenseNet architecture with a learnable weight mechanism and differentiated learning strategy to optimize channel selection and reduce redundancy.
- Additionally, a new dataset named KF9 for knitted fabrics has been created, and DLW-DenseNet has demonstrated a five-percentage-point increase in recognition accuracy compared to improved ResNet methods, proving its superiority in fabric texture recognition.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!