Promoting smart cities into the 5G era with multi-field Internet of Things (IoT) applications powered with advanced mechanical energy harvesters

With the development of 5G and Internet of Things (IoT), the era of big data‐driven product design is booming. In addition, artificial intelligence (AI) is also emerging and evolving by recent breakthroughs in computing power and software architectures. In this regard, the digital twin, analyzing various sensor data with the help of AI algorithms, has become a cutting‐edge technology that connects the physical and virtual worlds, in which the various sensors are highly desirable to collect environmental information. However, although existing sensor technologies, including cameras, microphones, inertial measurement units, etc., are widely used as sensing elements for various applications, high‐power consumption and battery replacement of them is still a problem. Triboelectric nanogenerators (TENGs) as self‐powered sensors supply a feasible platform for realizing self‐sustainable and low‐power systems. Herein, the recent progress on TENG‐based intelligent systems, that is, wearable electronics, robot‐related systems, and smart homes, followed by prospective future development enabled by sensor fusion technology, is focused on. Finally, how to apply artificial intelligence to the design of intelligent sensor systems for the 5G and IoT era is discussed.

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/aisy.202100228

Artificial Intelligence‐Enabled Sensing Technologies in the 5G/Internet of Things Era: From Virtual Reality/Augmented Reality to the Digital Twin

The seamless integration of emerging triboelectric nanogenerator (TENG) technology with traditional wearable textile materials has given birth to the next‐generation smart textiles, i.e., textile TENGs, which will play a vital role in the era of Internet of Things and artificial intelligences. However, low output power and inferior sensing ability have largely limited the development of textile TENGs. Among various approaches to improve the output and sensing performance, such as material modification, structural design, and environmental management, a 3D fabric structural scheme is a facile, efficient, controllable, and scalable strategy to increase the effective contact area for contact electrification of textile TENGs without cumbersome material processing and service area restrictions. Herein, the recent advances of the current reported textile TENGs with 3D fabric structures are comprehensively summarized and systematically analyzed in order to clarify their superiorities over 1D fiber and 2D fabric structures in terms of power output and pressure sensing. The forward‐looking integration abilities of the 3D fabrics are also discussed at the end. It is believed that the overview and analysis of textile TENGs with distinctive 3D fabric structures will contribute to the development and realization of high‐power output micro/nanowearable power sources and high‐quality self‐powered wearable sensors.

Advances in High‐Performance Autonomous Energy and Self‐Powered Sensing Textiles with Novel 3D Fabric Structures

Firefighting protective clothing is an essential equipment that can protect firefighters from burn injuries during the firefighting process. However, it is still a challenge to detect the damage of firefighting protective clothing at an early stage when firefighters are exposed to excessively high temperature in fire cases. Herein, an ultralight self-powered fire alarm electronic textile (SFA e-textile) based on conductive aerogel fiber that comprises calcium alginate (CA), Fe3O4 nanoparticles (Fe3O4 NPs), and silver nanowires (Ag NWs) was developed, which achieved ultrasensitive temperature monitoring and energy harvesting in firefighting clothing. The resulting SFA e-textile was integrated into firefighting protective clothing to realize wide-range temperature sensing at 100-400 °C and repeatable fire warning capability, which could timely transmit an alarm signal to the wearer before the firefighting protective clothing malfunctioned in extreme fire environments. In addition, a self-powered fire self-rescue location system was further established based on the SFA e-textile that can help rescuers search and rescue trapped firefighters in fire cases. The power in the self-powered fire location system was offered by an SFA e-textile-based triboelectric nanogenerator (TENG). This work provided a useful design strategy for the preparation of ultralight wearable temperature-monitoring SFA e-textile used in firefighting protective clothing.

An Ultralight Self-Powered Fire Alarm e-Textile Based on Conductive Aerogel Fiber with Repeatable Temperature Monitoring Performance Used in Firefighting Clothing.

Elastic Multifunctional Liquid–Metal Fibers for Harvesting Mechanical and Electromagnetic Energy and as Self-Powered Sensors

Fire disaster is one of the most common hazards that threaten public safety and social development: how to improve the fire escape and rescue capacity remains a huge challenge. Here, a 3D honeycomb-structured woven fabric triboelectric nanogenerator (F-TENG) based on a flame-retardant wrapping yarn is developed. The wrapping yarn is fabricated through a continuous hollow spindle fancy twister technology, which is compatible with traditional textile production processes. The resulting 3D F-TENG can be used in smart carpets as a self-powered escape and rescue system that can precisely locate the survivor position and point out the escape route to timely assist victim search and rescuing. As interior decoration, the unique design of the honeycomb weaving structure endows the F-TENG fabric with an excellent noise-reduction ability. In addition, combining with its good machine washability, air permeability, flame-retardency, durability, and repeatability features, the 3D F-TENG may have great potential applications in fire rescue and wearable sensors as well as smart home decoration.

A Machine-Fabricated 3D Honeycomb-Structured Flame-Retardant Triboelectric Fabric for Fire Escape and Rescue.

The industrial production of solar‐steaming materials is an essential part of the wide promotion of solar‐driven desalination technology. However, balancing the cost and freshwater yield constitutes a constant challenge. To solve these problems, discrete waste carbon fibers are assembled into an extensible and compressible yarn and further prepared for a fabric evaporator to achieve solar steam generation by two main textile technologies: blended yarn spinning and multibeam weaving. To improve the evaporation efficiency of the photothermal device, a bionic fabric structure resembling the aquatic plant Pistia is designed to reduce heat loss and increase material utilization. Assisted by the porous structure and gradient capillary effect of the evaporator, the performance is maximized by setting the photothermal part 3.2 cm above the water, improving evaporation efficiency from 52.70% in the original state to 88.70% under one sun (1 kW m−2). Combined with the low‐cost of waste carbon fiber and mature textile technologies, the overall raw material cost of the device is reduced to USD 3.54 per m2, which is anticipated that this study offers new possibilities for a cost‐effective, industrialized solar‐driven steam generation route.

Pistia‐Inspired Photothermal Fabric based on Waste Carbon Fiber for Low‐Cost Vapor Generation: An Industrialization Route

CTFusion: Convolutions Integrate with Transformers for Multi-modal Image Fusion

Airflow-assisted manufacturing holds significant promise across various applications, including rotor spinning, where airflow is crucial for fiber transfer and ultimately affects yarn performance. This study introduces a three-dimensional model of flexible fiber motion within the flow field of a rotor spinning unit. The fiber is modeled as cylindrical segments connected by rigid joints, with the segments experiencing fluid torques and the joints subjected to fluid forces. The model accounts for the fiber bending and torsional deformation. Fiber motion is determined by solving the translational and rotational equations, alongside the fiber–wall interactions. Emphasis is placed on the fiber stripping process from the opening roller. Supported by the numerical methods validated through visualization experiments and further corroborated by spinning examinations, we advance the understanding of the motion behavior of fibers released from different initial positions in the rotor spinning unit, and specifically, delve into the impact of airflow patterns on fiber movement. The developed numerical methodology and findings are vital for optimizing rotor spinning unit design and effectively harnessing airflow technology in processing.

Fiber motion in rotor spinning unit airflow: Numerical simulation and experimental validation

This paper established a dynamic drafting model with the frictional force between fibers to simulate the drafting process of the uneven input sliver. The simulated results indicated that the number of back, slow-floating, fast-floating, and front fibers would change sequentially when the input sliver linear density changed. Due to the different timing of quantity changes in the four types of fibers, the uneven input sliver led to a repeated increase and decrease in the proportion of fast and slow fibers in the drafting zone. When the input sliver linear density decreased, the floating fibers first continuously accelerated earlier, then accelerated earlier later, and then continuously accelerated earlier again. The effect of increasing the input sliver linear density on the floating fiber motion was opposite to that of decreasing the input sliver linear density. This paper offers a theoretical foundation for further improving the auto-leveling approach. 

The influence rule of uneven input sliver on floating fiber motion during the dynamic drafting process

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