Bioinspired iontronic synapse fibers for ultralow-power multiplexing neuromorphic sensorimotor textiles.

Artificial neuromorphic devices can emulate dendric integration, axonal parallel transmission, along with superior energy efficiency in facilitating efficient information processing, offering enormous potential for wearable electronics. However, integrating such circuits into textiles to achieve biomimetic information perception, processing, and control motion feedback remains a formidable challenge. Here, we engineer a quasi-solid-state iontronic synapse fiber (ISF) comprising photoresponsive TiO2, ion storage Co-MoS2, and an ion transport layer. The resulting ISF achieves inherent short-term synaptic plasticity, femtojoule-range energy consumption, and the ability to transduce chemical/optical signals. Multiple ISFs are interwoven into a synthetic neural fabric, allowing the simultaneous propagation of distinct optical signals for transmitting parallel information. Importantly, IFSs with multiple input electrodes exhibit spatiotemporal information integration. As a proof of concept, a textile-based multiplexing neuromorphic sensorimotor system is constructed to connect synaptic fibers with artificial fiber muscles, enabling preneuronal sensing information integration, parallel transmission, and postneuronal information output to control the coordinated motor of fiber muscles. The proposed fiber system holds enormous promise in wearable electronics, soft robotics, and biomedical engineering.

Rational Design of a Hydrophilic Core-Hydrophobic Shell Yarn-Based Solar Evaporator with an Underwater Aerophilic Surface for Self-Floating and High-Performance Dynamic Water Purification.

Interfacial solar vapor generation holds great promise for alleviating the global freshwater crisis, but its real-world application is limited by the efficiently choppy water evaporation and industrial production capability. Herein, a self-floating solar evaporator with an underwater aerophilic surface is innovatively fabricated by weaving core-shell yarns via mature weaving techniques. The core-shell yarns possess capillary water channels in the hydrophilic cotton core and can trap air in the hydrophobic electrospinning nanofiber shell when submerged underwater, simultaneously realizing controllable water supplies, stable self-flotation, and great thermal insulation. Consequently, the self-floating solar evaporator achieves an evaporation rate of 2.26 kg m-2 h-1 under 1 sun irradiation, with a reduced heat conduction of 70.18 W m-2. Additionally, for the first time, a solar evaporator can operate continuously in water with varying waveforms and intensities over 24 h, exhibiting an outdoor cumulative evaporation rate of 14.17 kg m-2 day-1.

Nanoribbon Yarn with Versatile Inorganic Materials.

Nanomaterial-based yarns have been actively developed owing to their advantageous features, namely, high surface-area-to-volume ratios, flexibility, and unusual material characteristics such as anisotropy in electrical/thermal conductivity. The superior properties of the nanomaterials can be directly imparted and scaled-up to macro-sized structures. However, most nanomaterial-based yarns have thus far, been fabricated with only organic materials such as polymers, graphene, and carbon nanotubes. This paper presents a novel fabrication method for fully inorganic nanoribbon yarn, expanding its applicability by bundling highly aligned and suspended nanoribbons made from various inorganic materials (e.g., Au, Pd, Ni, Al, Pt, WO3, SnO2, NiO, In2O3, and CuO). The process involves depositing the target inorganic material on a nanoline mold, followed by suspension through plasma etching of the nanoline mold, and twisting using a custom-built yarning machine. Nanoribbon yarn structures of various functional inorganic materials are utilized for chemical sensors (Pd-based H2 and metal oxides (MOx)-based green gas sensors) and green energy transducers (water splitting electrodes/triboelectric nanogenerators). This method is expected to provide a comprehensive fabrication strategy for versatile inorganic nanomaterials-based yarns.

Assessing the Role of Yarn Placement in Plated Knit Strain Sensors: A Detailed Study of Their Electromechanical Properties and Applicability in Bending Cycle Monitoring.

In this study, we explore how the strategic positioning of conductive yarns influences the performance of plated knit strain sensors fabricated using commercial knitting machines with both conductive and non-conductive yarns. Our study reveals that sensors with conductive yarns located at the rear, referred to as 'purl plated sensors', exhibit superior performance in comparison to those with conductive yarns at the front, or 'knit plated sensors'. Specifically, purl plated sensors demonstrate a higher sensitivity, evidenced by a gauge factor ranging from 3 to 18, and a minimized strain delay, indicated by a 1% strain in their electromechanical response. To elucidate the mechanisms behind these observations, we developed an equivalent circuit model. This model examines the role of contact resistance within varying yarn configurations on the sensors' sensitivity, highlighting the critical influence of contact resistance in conductive yarns subjected to wale-wise stretching on sensor responsiveness. Furthermore, our findings illustrate that the purl plated sensors benefit from the vertical movement of non-conductive yarns, which promotes enhanced contact between adjacent conductive yarns, thereby improving both the stability and sensitivity of the sensors. The practicality of these sensors is confirmed through bending cycle tests with an in situ monitoring system, showcasing the purl plated sensors' exceptional reproducibility, with a standard deviation of 0.015 across 1000 cycles, and their superior sensitivity, making them ideal for wearable devices designed for real-time joint movement monitoring. This research highlights the critical importance of conductive yarn placement in sensor efficacy, providing valuable guidance for crafting advanced textile-based strain sensors.

Selective Interbundle Cross-Linking for Lightweight and Superstrong Carbon Nanotube Yarns.

In this study, a range of carbon nanotube yarn (CNTY) architectures was examined and controlled by chemical modification to gain a deeper understanding of CNTY load-bearing systems and produce lightweight and superstrong CNTYs. The architecture of CNTY, which has polymer layers surrounding a compact bundle without hampering the original state of the CNTs in the bundle, is a favorable design for further chemical cross-linking and for enhancing the load-transfer efficiency, as confirmed by in situ Raman spectroscopy under a stress load. The resulting CNTY exhibited excellent mechanical performance that exceeded the specific strength of the benchmark, high-performance fibers. This exceptional strength of the CNTY makes it a promising candidate for the cable of a space elevator traveling from the Earth to the International Space Station given its strength of 4.35 GPa/(g cm-3), which can withstand the self-weight of a 440 km cable.

Hierarchically Plied Mechano-Electrochemical Energy Harvesting Using a Scalable Kinematic Sensing Textile Woven from a Graphene-Coated Commercial Cotton Yarn.

Wearable sensing systems are suitable for monitoring human motion. To realize a cost-effective and self-powered strain-sensing fiber, we developed a mechano-electrochemical harvesting yarn and textile using hierarchically arranged plied yarns composed of meter-long graphene-coated cotton yarns. Such a fiber relies on the principle of electrochemical capacity change to convert mechanical energy to electric energy. Further, this harvester can be used as a self-powered strain sensor because its output depends on mechanical stimuli. Additionally, the yarn can be woven into a kinematic sensing textile that measures the strength and direction of the applied force. The textile-type harvester can successfully detect various human movements such as pressing, bending, and stretching. The proposed sensing fiber will pave the way for the development of advanced wearable systems for ubiquitous healthcare in the future.

Mechanical strength changes of combustible municipal solid waste components during their early pyrolysis stage and mechanism analysis.

Implementation of municipal solid waste (MSW) source segregation leads to a more convenient recycle of combustible MSW components. Textiles, plastics and papers are commonly available combustible components in MSW. Their shredding is conducive to resources recovery. But these components usually have high tensile strengths and are difficult to shred. To understand their mechanical strength changes in their early pyrolysis stage will help to address this problem. In this study, a universal electronic testing machine was used to determine the breaking strengths of the materials including cotton towel, polyethylene glycol terephthalate (PET), ivory board (IB), kraft paper (KP) and wool scarf in the temperature range of 30-250°C under N2 atmosphere, and the mechanisms of their strength changes were explored. The reaction force field molecular dynamics (ReaxFF-MD) simulation was used to explain the decomposition behaviours of different sugar groups of hemicellulose in cotton and paper and the change of van der Waals energy of wool during their early pyrolysis stages. The results showed that breaking strengths of all the combustible MSW components reduced as the temperature increased. The breaking strength of PET was found to have the highest descent rate with increasing temperature, then the descent rates of wool and cotton came as the second and third, respectively. Compared with cotton, the breaking strengths of KP and IB decreased more slowly. As the temperature increased, the breaking strength of cotton reduced mainly due to the decomposition of the glucuronic acid in hemicellulose, and the reduction was characterized by CO2 release. The breaking strength reduction of PET was caused by its molecular chain being relaxed. The breaking strength reduction of wool was firstly caused by the decrease in the van der Waals energy between its molecules, and then caused by molecular chain breaking. In addition, in order to understand the influence of material size on the breaking strength change during thermal treatment, the breaking strengths of cotton yarn bundles were correlated with their yarn number and temperature. This study lays the foundation for understanding changes in mechanical strengths of combustible MSW components during their early pyrolysis stage.

Bridging a Gap in Thermal Conductivity and Heat Transfer in Hybrid Fibers and Yarns via Polyimide and Silicon Nitride Composites.

The pressing issues of the energy crisis and rapid electronics development have sparked a growing interest in the production of highly thermally conductive polymer composites. Due to the challenges related to the poor processability of hybrid materials and filler distribution to achieve high thermal conductivity, electrospinning is employed to create composite nanofibers and yarns using polyimide (PI) and thermally conductive silicon nitride (SiN) nanoparticles. The thermal performance of the individual nanofibers is evaluated using scanning thermal microscopy (SThM), providing significant insights into their heat transfer performance. Next, the nanofibers are applied as coatings on resistance wires to assess the thermal conductivity and insulation properties. Notably, the samples containing 35 wt.% of SiN exhibit a 25% increase in surface temperature. These innovative materials hold great promise as exceptional candidates for smart textiles and thermal management applications, addressing the growing demand for effective heat dissipation and regulation.

Damage Monitoring of Braided Composites Using CNT Yarn Sensor Based on Artificial Fish Swarm Algorithm.

This study aims to enable intelligent structural health monitoring of internal damage in aerospace structural components, providing a crucial means of assuring safety and reliability in the aerospace field. To address the limitations and assumptions of traditional monitoring methods, carbon nanotube (CNT) yarn sensors are used as key elements. These sensors are woven with carbon fiber yarns using a three-dimensional six-way braiding process and cured with resin composites. To optimize the sensor configuration, an artificial fish swarm algorithm (AFSA) is introduced, simulating the foraging behavior of fish to determine the best position and number of CNT yarn sensors. Experimental simulations are conducted on 3D braided composites of varying sizes, including penetration hole damage, line damage, and folded wire-mounted damage, to analyze the changes in the resistance data of carbon nanosensors within the damaged material. The results demonstrate that the optimized configuration of CNT yarn sensors based on AFSA is suitable for damage monitoring in 3D woven composites. The experimental positioning errors range from 0.224 to 0.510 mm, with all error values being less than 1 mm, thus achieving minimum sensor coverage for a maximum area. This result not only effectively reduces the cost of the monitoring system, but also improves the accuracy and reliability of the monitoring process.

The Detection of Yarn Roll's Margin in Complex Background.

Online detection of yarn roll's margin is one of the key issues in textile automation, which is related to the speed and scheduling of bobbin (empty yarn roll) replacement. The actual industrial site is characterized by uneven lighting, restricted shooting angles, diverse yarn colors and cylinder yarn types, and complex backgrounds. Due to the above characteristics, the neural network detection error is large, and the contour detection extraction edge accuracy is low. In this paper, an improved neural network algorithm is proposed, and the improved Yolo algorithm and the contour detection algorithm are integrated. First, the image is entered in the Yolo model to detect each yarn roll and its dimensions; second, the contour and dimensions of each yarn roll are accurately detected based on Yolo; third, the diameter of the yarn rolls detected by Yolo and the contour detection algorithm are fused, and then the length of the yarn rolls and the edges of the yarn rolls are calculated as measurements; finally, in order to completely eliminate the error detection, the yarn consumption speed is used to estimate the residual yarn volume and the measured and estimated values are fused using a Kalman filter. This method overcomes the effects of complex backgrounds and illumination while being applicable to different types of yarn rolls. It is experimentally verified that the average measurement error of the cylinder yarn diameter is less than 8.6 mm, and the measurement error of the cylinder yarn length does not exceed 3 cm.

Dynamic Yarn-Tension Detection Using Machine Vision Combined with a Tension Observer.

Machine vision can prevent additional stress on yarn caused by contact measurement, as well as the risk of hairiness and breakage. However, the speed of the machine vision system is limited by image processing, and the tension detection method based on the axially moving model does not take into account the disturbance on yarn caused by motor vibrations. Thus, an embedded system combining machine vision with a tension observer is proposed. The differential equation for the transverse dynamics of the string is established using Hamilton's principle and then solved. A field-programmable gate array (FPGA) is used for image data acquisition, and the image processing algorithm is implemented using a multi-core digital signal processor (DSP). To obtain the yarn vibration frequency in the axially moving model, the brightest centreline grey value of the yarn image is put forward as a reference to determine the feature line. The calculated yarn tension value is then combined with the value obtained using the tension observer based on an adaptive weighted data fusion method in a programmable logic controller (PLC). The results show that the accuracy of the combined tension is improved compared with the original two non-contact methods of tension detection at a faster update rate. The system alleviates the problem of inadequate sampling rate using only machine vision methods and can be applied to future real-time control systems.

Movement Recognition through Inductive Wireless Links: Investigation of Different Fabrication Techniques.

In this paper, an inductive wireless link for motion recognition is investigated. In order to validate the feasibility of a wearable implementation, the use of three different materials is analyzed: a thin copper wire, a conductive yarn, and a conductive non-woven fabric. Results from the application of the developed devices on an arm are reported and discussed. It is demonstrated that the proposed textile inductive resonant wireless links are well suited for developing a compact wearable system for joint flexion recognition.

Performance Analysis of Lambda Architecture-Based Big-Data Systems on Air/Ground Surveillance Application with ADS-B Data.

This study introduces a novel methodology designed to assess the accuracy of data processing in the Lambda Architecture (LA), an advanced big-data framework qualified for processing streaming (data in motion) and batch (data at rest) data. Distinct from prior studies that have focused on hardware performance and scalability evaluations, our research uniquely targets the intricate aspects of data-processing accuracy within the various layers of LA. The salient contribution of this study lies in its empirical approach. For the first time, we provide empirical evidence that validates previously theoretical assertions about LA, which have remained largely unexamined due to LA's intricate design. Our methodology encompasses the evaluation of prospective technologies across all levels of LA, the examination of layer-specific design limitations, and the implementation of a uniform software development framework across multiple layers. Specifically, our methodology employs a unique set of metrics, including data latency and processing accuracy under various conditions, which serve as critical indicators of LA's accurate data-processing performance. Our findings compellingly illustrate LA's "eventual consistency". Despite potential transient inconsistencies during real-time processing in the Speed Layer (SL), the system ultimately converges to deliver precise and reliable results, as informed by the comprehensive computations of the Batch Layer (BL). This empirical validation not only confirms but also quantifies the claims posited by previous theoretical discourse, with our results indicating a 100% accuracy rate under various severe data-ingestion scenarios. We applied this methodology in a practical case study involving air/ground surveillance, a domain where data accuracy is paramount. This application demonstrates the effectiveness of the methodology using real-world data-intake scenarios, therefore distinguishing this study from hardware-centric evaluations. This study not only contributes to the existing body of knowledge on LA but also addresses a significant literature gap. By offering a novel, empirically supported methodology for testing LA, a methodology with potential applicability to other big-data architectures, this study sets a precedent for future research in this area, advancing beyond previous work that lacked empirical validation.

Enhanced Frequency Stability of SAW Yarn Tension Sensor by Using the Dual Differential Channel Surface Acoustic Wave Oscillator.

This paper presents a 60 MHz surface acoustic wave (SAW) yarn tension sensor incorporating a novel SAW oscillator with high-frequency stability. A SAW delay line was fabricated on ST-X quartz substrate using the unbalanced-split electrode and bi-directional engraving slots. The dual differential channel delay linear acoustic surface wave oscillator is designed and implemented to test yarn tension, which can effectively remove the interference of temperature, humidity, and other peripheral factors through differential design. The yarn tension sensor using the surface acoustic wave has high-precision characteristics, and the SAW delay line oscillator is designed to ensure the test system's stable operation. The effect of time and tension on oscillator frequency stability is studied in detail, and the single oscillator and the dual differential channel system were tested, respectively. After using the dual differential channel system, the short-term frequency stability from is reduced from 1.0163 ppm to 0.17726 ppm, the frequency accuracy of the tension sensor is improved from 134 Hz to 27 Hz, and the max frequency jump steady is reduced from 2.2395 ppm to 0.45123 ppm.

High-Power Hydro-Actuators Fabricated from Biomimetic Carbon Nanotube Coiled Yarns with Fast Electrothermal Recovery.

Bioinspired yarn/fiber structured hydro-actuators have recently attracted significant attention. However, most water-driven mechanical actuators are unsatisfactory because of the slow recovery process and low full-time power density. A rapidly recoverable high-power hydro-actuator is reported by designing biomimetic carbon nanotube (CNT) yarns. The hydrophilic CNT (HCNT) coiled yarn was prepared by storing pre-twist into CNT sheets and subsequent electrochemical oxidation (ECO) treatment. The resulting yarn demonstrated structural stability even when one end was cut off without the possible loss of pre-stored twists. The HCNT coiled yarn actuators provided maximal contractile work of 863 J/kg at 11.8 MPa stress when driven by water. Moreover, the recovery time of electrically heated yarns at a direct current voltage of 5 V was 95% shorter than that of neat yarns without electric heating. Finally, the electrothermally recoverable hydro-actuators showed a high actuation frequency (0.17 Hz) and full-time power density (143.8 W/kg).

High-Performance Biscrolled Ni-Fe Yarn Battery with Outer Buffer Layer.

The increasing demand for portable and wearable electronics has promoted the development of safe and flexible yarn-based batteries with outstanding electrochemical properties. However, achieving superior energy storage performance with a high active material (AM) load and long cycle life with this device format remains a challenge. In this study, a stable and rechargeable high-performance aqueous Ni-Fe yarn battery was constructed via biscrolling to embed AMs within helical carbon nanotube (CNT) yarn corridors. Owing to the high load of charge storage nanoparticles (NPs; above 97 wt%) and the outer neat CNT layer, the buffered biscrolled Ni-Fe yarn battery demonstrates excellent linear capacity (0.053 mAh/cm) and cycling stability (60.1% retention after 300 charge/discharge cycles) in an aqueous electrolyte. Moreover, our flexible yarn battery exhibits maximum energy/power densities of 422 mWh/cm3 and 7535 mW/cm3 based on the total volume of the cathode and anode, respectively, which exceed those reported for many flexible Ni-Fe batteries. Thus, biscrolled Ni-Fe yarn batteries are promising candidates for next-generation conformal energy solutions.

A quantitative study of the influence of cement matrix impregnation on yarn pullout strength using combined confocal microscopy and double resin impregnation.

The complex impregnation of a multifilament yarn by a cementitious matrix leads to a difficult prediction of the mechanical behaviour of textile reinforced concrete and its less spread use than steel-reinforced concrete. To solve this problem, several models were established but they are not based on direct observations of the embedded yarn or quantification of its impregnation. In order to improve those models, a double resin impregnation process followed by confocal microscopy was set up, after pullout test was performed on each sample. Several parameters were then computed from the obtained images, which enables to quantify the impregnation of the yarn for each sample and to compare it with the pullout mechanical results. The number of fully impregnated filaments is found to be the crucial point to explain the pullout maximum load. The type of failure is also defined using those same parameters, computed along the embedded length, and it was found that the shape of the extracted volume of yarn is cylindrical and so the failure of the filaments is not telescopic. A pre-existing model was then improved, considering all those conclusions obtained by microscopic observations, and a good match between the numerical and the experimental results was found. Multifilament yarns are a continuous and flexible textile structure composed of hundreds of filaments that are maintain together using a chemical product called sizing. Those multifilament yarns can be used to reinforce mortar or concrete, like steel in steel-reinforced concrete. The performances of those types of composites depend strongly on the strength of the reinforcement/mortar bond. In case of textile /mortar composite, this bond depends on the level of penetration of the mortar into the yarn, so the impregnation of the yarn by the mortar. Since they are both very heterogeneous material, this impregnation is random and incomplete. Consequently, it is very difficult to predict the strength of this bond and so, the strength of the yarn/mortar composite. As a result, this type of composite cannot be widely used. Some models were established to predict the strength of those composite; however, they are not based on direct observation of the textile/mortar bond. To improve those models, a new visualisation method of the impregnation of the yarn by the mortar was set up. A double moulding of the impregnated yarn by the mortar was manufactured, using resins and two different fluorescent dyes, after underwent a mechanical test of pulling out the yarn from the mortar (pullout test). First, a resin with a red fluorescent dye was used for the moulding of the yarn shortly after the test, and, second, after dissolution of the mortar around the yarn in acid, a resin with a green fluorescent dye. A number of cross sections of the yarn is observed using a microscope that detects those two dyes and the obtained images are analysed allowing a clear differentiation, location and counting of fully impregnated filaments of the yarn by the mortar and those partially or not impregnated. The results are compared to the mechanical parameters of the pullout test. The number of fully impregnated filaments is found to be crucial to explain the strength of the composite. The behaviour during the pullout test is also explained using parameters computed along the length of the yarn in the mortar. It is found that filaments extracted by pulling-out take a cylindrical shape and so the pullout behaviour is not conical shape as it was suggested in a number of models. Considering all those conclusions obtained by observations, a pre-existing model is improved, and a good match between the numerical and experimental results is found.

Effect of extreme mechanical densification on the electrical properties of carbon nanotube micro-yarns.

We have explored the effect of high pressure post-treatment in optimizing the properties of carbon nanotube yarns and found that the application of dry hydrostatic pressure reduces porosity and enhances electrical properties. The CNT yarns were prepared by the dry-spinning method directly from CNT arrays made by the hot filament chemical vapour deposition (HF-CVD) process. Mechanical hydrostatic pressure up to 360 MPa induces a decrease in yarn resistivity between 3% and 35%, associated with the sample's permanent densification, with CNT yarn diameter reduction of 10%-25%. However, when increasing the pressure in the 1-3 GPa domain in non-hydrostatic conditions, the recovered samples show lower electrical conductivity. This might be due to concomitant macroscopic effects such as increased twists and damage to the yarn shown by SEM imaging (caused by strong shear stresses and friction) or by the collapse of the CNTs indicated byin situhigh pressure Raman spectroscopy data.

Enhancement of the mechanical and thermal transport properties of carbon nanotube yarns by boundary structure modulation.

Carbon nanotubes (CNTs) exhibit extremely high nanoscopic thermal/electrical transport and mechanical properties. However, the macroscopic properties of assembled CNTs are significantly lower than those of CNTs because of the boundary structure between the CNTs. Therefore, it is crucial to understand the relationship between the nanoscopic boundary structure in CNTs and the macroscopic properties of the assembled CNTs. Previous studies have shown that the nanoscopic phonon transport and macroscopic thermal transport in CNTs are improved by Joule annealing because of the improved boundary Van-der-Waals interactions between CNTs via the graphitization of amorphous carbon. In this study, we investigate the mechanical strength and thermal/electrical transport properties of CNT yarns with and without Joule annealing at various temperatures, analyzing the phenomena occurring at the boundaries of CNTs. The obtained experimental and theoretical results connect the nanoscopic boundary interaction of CNTs in CNT yarns and the macroscopic mechanical and transport properties of CNT yarns.

Re-Stickable Yarn Supercapacitors with Vaper Phase Polymerized Multi-Layered Polypyrrole Electrodes for Smart Garments.

Yarn supercapacitors have attracted significant attention for wearable energy storage due to their ability to be directly integrated with garments. Conducting polymer polypyrrole (PPy) based yarn supercapacitors show limited cycling stability because of the huge volume changes during the charge-discharge processes. In addition, laundering may cause damage to such yarn supercapacitors. Here, the fabrication of PPy-based re-stickable yarn supercapacitors is reported with good cycling stability by employing vapor phase polymerization (VPP) and water-soluble polyethylene oxide (PEO) film as the adhesive layer. VPP duration and cycle are controlled to achieve multi-layered PPy electrodes. The assembled yarn supercapacitors show a good cycling stability with capacitance retention of 79.1% after 5000 charge-discharge cycles. The energy stored in the yarn supercapacitor is sufficient to power a photodetector. After gluing the yarn supercapacitors onto a PEO film, the devices can be stunk on and peeled off the garment to avoid the mechanical stresses during the washing process. Three yarn supercapacitors connected in parallel on PEO film show negative changes in electrochemical performance after 5 sticking-peeling cycles. This work provides a facile way to fabricate PPy-based re-stickable energy storage devices with high cycling stability for smart garments.