Computing Fabrics

Loke, Gabriel; Alain, Juliette; Yan, Wei; Khudiyev, Tural; Noel, Grace H.; Yuan, Rodger; Missakian, Anais; Fink, Yoel

doi:10.1016/j.matt.2020.03.007

Cited by 47 publications

(59 citation statements)

References 7 publications

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“…When presented with an unknown temperature-time data set, the fibre infers the type of physical activity with an accuracy of 96% based on a 12-s temperature reading. The ability to measure and store digital information in a fibre which also contains inference algorithms presents intriguing opportunities in a variety of applications in which functional fibres and fabrics are in close proximity with the human body, including autonomous drug delivery 23 , neural interfaces 18 , personal thermal management 24 , and computing in fabrics 2 .…”

Section: Discussionmentioning

confidence: 99%

“…These are typically made of fabrics and have the a priori advantage of being in physical contact with large surface areas of the human body and already are a fact of life for all segments of society. As such, they present a significant opportunity 2 , 3 to harvest, store, and perhaps, even analyse relevant untapped physiologic variables. While uniquely positioned to address this challenge, the ability to impart digital attributes to fabrics has been limited.…”

Section: Introductionmentioning

confidence: 99%

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Digital electronics in fibres enable fabric-based machine-learning inference

et al. 2021

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Digital devices are the essential building blocks of any modern electronic system. Fibres containing digital devices could enable fabrics with digital system capabilities for applications in physiological monitoring, human-computer interfaces, and on-body machine-learning. Here, a scalable preform-to-fibre approach is used to produce tens of metres of flexible fibre containing hundreds of interspersed, digital temperature sensors and memory devices with a memory density of ~7.6 × 105 bits per metre. The entire ensemble of devices are individually addressable and independently operated through a single connection at the fibre edge, overcoming the perennial single-fibre single-device limitation and increasing system reliability. The digital fibre, when incorporated within a shirt, collects and stores body temperature data over multiple days, and enables real-time inference of wearer activity with an accuracy of 96% through a trained neural network with 1650 neuronal connections stored within the fibre. The ability to realise digital devices within a fibre strand which can not only measure and store physiological parameters, but also harbour the neural networks required to infer sensory data, presents intriguing opportunities for worn fabrics that sense, memorise, learn, and infer situational context.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Digital electronics in fibres enable fabric-based machine-learning inference

et al. 2021

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“…Through the combination of electrospinning and 4D printing, the bionic fiber scaffolds with different fiber sizes and orientations can be customized. Furthermore, in addition to electrospinning technology, other fiber manufacturing techniques can also be used to prepare fibers to break through the limitations of electrospinning [117,118], such as thermal drawing technique [119,120]. The diversity of manufacturing methods can produce [116] fibers composed of a variety of materials with different properties [121,122].…”

Section: Summary and Perspectivesmentioning

confidence: 99%

Shape Memory Polymer Fibers: Materials, Structures, and Applications

et al. 2021

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Shape memory polymer (SMP) is a kind of material that can sense and respond to the changes of the external environment, and its behavior is similar to the intelligent reflection of life. Electrospinning, as a versatile and feasible technique, has been used to prepare shape memory polymer fibers (SMPFs) and expand their structures. SMPFs show some advanced features and functions in many fields. In this review, we give a comprehensive overview of SMPFs, including materials, fabrication methods, structures, multifunction, and applications. Firstly, the mechanism and characteristics of SMP are introduced. We then discuss the electrospinning method to form various microstructures, like non-woven fibers, core/shell fibers, hollow fibers and oriented fibers. Afterward, the multiple functions of SMPFs are discussed, such as multi-shape memory effect, reversible shape memory effect and remote actuation of composites. We also focus on some typical applications of SMPFs, including biomedical scaffolds, drug carriers, self-healing, smart textiles and sensors, as well as energy harvesting devices. At the end, the challenges and future development directions of SMPFs are proposed.

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“…[7] During this process, the preform dimensions are scaled down by several orders of magnitude, while preserving the overall cross-sectional geometry of the final fiber. [8] This approach permits integration of multiple functionalities such as electrodes for recording of extracellular neuronal potentials, optical waveguides for optogenetic neuromodulation, and microfluidic channels for drug and gene delivery. This one-step, top-down fabrication process can yield hundreds of probes from a single preform, and is a cost-efficient and scalable alternative to lithographic approaches that require resource-intensive cleanroom microfabrication techniques.…”

Section: Introductionmentioning

confidence: 99%

Customizing Multifunctional Neural Interfaces through Thermal Drawing Process

Antonini

Sahasrabudhe

Tabet

et al. 2021

Preprint

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Fiber drawing enables scalable fabrication of multifunctional flexible fibers that integrate electrical, optical and microfluidic modalities to record and modulate neural activity. Constraints on thermomechanical properties of materials, however, have prevented integrated drawing of metal electrodes with low-loss polymer waveguides for concurrent electrical recording and optical neuromodulation. Here we introduce two fabrication approaches: (1) an iterative thermal drawing with a soft, low melting temperature (Tm) metal indium, and (2) a metal convergence drawing with traditionally non-drawable high Tm metal tungsten. Both approaches deliver multifunctional flexible neural interfaces with low-impedance metallic electrodes and low-loss waveguides, capable of recording optically-evoked and spontaneous neural activity in mice over several weeks. We couple these fibers with a light-weight mechanical microdrive (1g) that enables depth-specific interrogation of neural circuits in mice following chronic implantation. Finally, we demonstrate the compatibility of these fibers with magnetic resonance imaging (MRI) and apply them to visualize the delivery of chemical payloads through the integrated channels in real time. Together, these advances expand the domains of application of the fiber-based neural probes in neuroscience and neuroengineering.

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Computing Fabrics

Cited by 47 publications

References 7 publications

Digital electronics in fibres enable fabric-based machine-learning inference

Digital electronics in fibres enable fabric-based machine-learning inference

Shape Memory Polymer Fibers: Materials, Structures, and Applications

Customizing Multifunctional Neural Interfaces through Thermal Drawing Process

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