Patent classifications
D03D15/533
CONDUCTIVE FABRIC AND MANUFACTURING METHOD THEREOF
Provided are a conductive fabric and a manufacturing method thereof. The conductive fabric has a structure in which warp yarns and weft yarns are interwoven with each other, wherein at least one of the warp yarns and the weft yarns includes carbon nanotube fibers, the carbon nanotube fibers contain N-doped carbon nanotubes, the nitrogen content in each of the carbon nanotube fibers is between 1 wt% to 5 wt% based on the total weight of the carbon nanotube fiber, and the content of the N-doped carbon nanotubes in the conductive fabric is at least 0.1 wt% based on the total weight of the conductive fabric.
CONDUCTIVE FABRIC AND MANUFACTURING METHOD THEREOF
Provided are a conductive fabric and a manufacturing method thereof. The conductive fabric has a structure in which warp yarns and weft yarns are interwoven with each other, wherein at least one of the warp yarns and the weft yarns includes carbon nanotube fibers, the carbon nanotube fibers contain N-doped carbon nanotubes, the nitrogen content in each of the carbon nanotube fibers is between 1 wt% to 5 wt% based on the total weight of the carbon nanotube fiber, and the content of the N-doped carbon nanotubes in the conductive fabric is at least 0.1 wt% based on the total weight of the conductive fabric.
FABRICS INCLUDING A SINGLE-PLY YARN AND/OR HAVING LOW PICKS PER INCH OR LOW COURSES PER INCH
Fabrics including a single-ply yarn are described herein along with fabrics that have low picks per inch or low courses per inch. The fabrics may comprise modacrylic fibers, meta-aramid fibers, anti-static fibers, and optionally para-aramid fibers, and the fabrics may comprise: about 10% or 20% to about 60% or 80% modacrylic fibers by weight of the fabric; about 20% or 40% to about 80% meta-aramid fibers by weight of the fabric; about 0.1% to about 2% anti-static fibers by weight of the fabric; and about 0% to about 10% para-aramid fibers by weight of the fabric.
FABRICS INCLUDING A SINGLE-PLY YARN AND/OR HAVING LOW PICKS PER INCH OR LOW COURSES PER INCH
Fabrics including a single-ply yarn are described herein along with fabrics that have low picks per inch or low courses per inch. The fabrics may comprise modacrylic fibers, meta-aramid fibers, anti-static fibers, and optionally para-aramid fibers, and the fabrics may comprise: about 10% or 20% to about 60% or 80% modacrylic fibers by weight of the fabric; about 20% or 40% to about 80% meta-aramid fibers by weight of the fabric; about 0.1% to about 2% anti-static fibers by weight of the fabric; and about 0% to about 10% para-aramid fibers by weight of the fabric.
CUSTOM THREE DIMENSIONAL FORMING OF SURGICAL GUIDES
A kit including a medical guide template, and a sterilizable receptacle accommodating the guide template, the kit being configured to allow deformation of the template into an operational medical guide.
Anti-Static Fleece, Brushed Fabric and Composite Yarn for Their Manufacture
An anti-static fleece or brushed fabric consisting essentially of acrylic fiber, polyester fiber, cotton fiber, wool fiber, nylon fiber or combinations of 2 or more thereof, characterized in that the fleece or brushed fabric has a basis weight of from 65 gsm to 400 gsm, contains from 0.1 wt % to 2 wt % of bicomponent anti-static fiber and is further characterized in that the fleece or brushed fabric exhibits a static decay time of less than 4 seconds The woven or knit fleece or brushed fabric has permanent anti-static properties which do not wash out during laundering. A preferred yarn for making the fleece or brushed fabric is a composite anti-static filamentary yarn comprising anti-static bicomponent filament wrapped with non-conductive filament in a weight ratio of non-conductive filament:anti-static bicomponent filament of from 2:1 to 8:1.
Functional Braided Composite Yarn
Braided composite yarns including one or more functional components such as conductors and one or more structural components such as para-aramid fibers, and methods of manufacture therefor. Bundles of at least one functional component and at least one structural component undergo simultaneous parallel winding under tension onto a single bobbin prior to braiding, thus reducing the mechanical loading forces on the functional components in the final yarn. The yarns can be engineered with application-specific electrical, electronic, electromagnetic, or physical properties that enable their use as electronic components or sensors, and attached to or incorporated into active textiles and composite substrates. The yarns can be directly soldered to without prior removal of insulation or other yarn components. Some yarns, such as those for use as inductors, can include a core with desired electrical properties.
Fabric With Embedded Electrical Components
Apparatus, comprising fabric (62) formed from fibers (74); and an electrical component (20) having first and second perpendicular fiber guiding structures, wherein a first of the fibers is soldered in the first fiber guiding structure and a second of the fibers is soldered in the second fiber guiding structure.
Heat resistant outershell fabric
The present invention relates to a thermally-resistant woven fabric and/or multiple ply fabric sheet for use as single or outer layer of protective garments, of the type comprising an inside fabric layer and an outside fabric layer joined together by an array of connecting lines. The woven fabric and/or multiple ply fabric sheet comprise yarns, wherein said yarn comprises i) meta-aramid ii) from about 5 to 10 weight % of polyamide and iii) at least 2 weight % of antistatic fibers, the weight % being based on the total weight of the yarn.
Detection and Classification of Unknown Motions in Wearable Devices
Computing systems and related methods are provided for discovery of undefined user movements. Sensor data associated with one or more sensors of a wearable device can be obtained and input into one or more machine-learned models that have been trained to learn a continuous embedding space based at least in part on one or more target criteria. Data indicative of a position of the sensor data within the continuous embedding space can be obtained as an output of the one or more machine-learned models. A functionality associated with the position of the sensor data within the continuous embedding space can be determined. The functionality associated with the position of the sensor data within the continuous embedding space can be initiated.