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Triboelectric nanogenerators (TENGs) and TENG sensors have received great attention in cutting-edged electrical device applications, including energy collectors, wearable electronics, health monitoring, and human-machine interfaces. However, film TENG still has the problem of high cost, complicated production process, low integration and slow production speed. Furthermore, there are problems such as uneven and difficult to cut after processing. Here, laser-induced graphene based TENG (L-TENG) assembled with polyimide (PI) paper was developed for environmental energy harvesting and tactile perception. Laser programmable processing has the advantages of patterning, customization and efficiency, thereby producing diversified TENG in one step. L-TENG can be captured not only for mechanical energy harvesting but also for environmental sensing. By means of laser parameter optimization, L-TENG can produce voltage as high as 245 V. Through special shape design, the performance of L-TENG can be increased from 48 to 94 V. The L-TENG was first applied to wearable devices that collect energy from human movement to drive LED lights. In addition, the smart mask is synchronously made to capture human health information, such as breathing, speaking, detection. The wind energy collection device was constructed to realize the collection of environmental energy. To further illustrate L-TENG multifunctionally applied in the field of sensing, matrix sensor can also be applied by voltage signals to detect gesture touch recognition and writing movement detection. This laser-manufactured TENG using PI paper provides a promising platform for the development of environmental energy capture, flexible tactile sensing, human-computer interaction and intelligent wearable devices. [ABSTRACT FROM AUTHOR]

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Unidirectional two-lane freeway is a typical and the simplest form of freeway. The traffic flow characteristics including safety condition on two-lane freeway is of great significance in planning, design, and management of a freeway. Many previous traffic flow models are able to figure out flow characteristics such as speed, density, delay, and so forth. These models, however, have great difficulty in reflecting safety condition of vehicles. Besides, for the cellular automation, one of the most widely used microscopic traffic simulation models, its discreteness in both time and space can possibly cause inaccuracy or big errors in simulation results. In this paper, a micro-simulation model of two-lane freeway vehicles is proposed to evaluate characteristics of traffic flow, including safety condition. The model is also discrete in time but continuous in space, and it divides drivers into several groups on the basis of their preferences for overtaking, which makes the simulation more aligned with real situations. Partial test is conducted in this study and results of delay, speed, volume, and density indicate the preliminary validity of our model, based on which the proposed safety coefficient evaluates safety condition under different flow levels. It is found that the results of this evaluation coincide with daily experience of drivers, providing ground for effectiveness of the safety coefficient. [ABSTRACT FROM AUTHOR]

A process for continuously monitoring the ultrasonic dispersion process of carbon nanotubes (CNTs) was discovered by integrating dynamic light scattering (DLS) and UV-vis spectroscopy. Through a continuous flow approach adapted to the dispersion system, the CNT suspension could be quantitatively characterized in real time. By continuously monitoring and identifying the evolution of mean particle size distribution and transparency of CNT suspension, the effective dispersion parameters and dispersion quality was quantified. Two types of MWNTs of different diameters and lengths were studied. For longer MWNTs with smaller diameters, the dispersion process achieved at a steady 100 nm of agglomerate size with 40 min of sonication. For shorter and large diameter and less entangled MWNTs, after 60 min of sonication, an agglomerate size of 100 nm was achieved, yet the stability decrease. The results show the potential ability of the system to optimize nanoparticle dispersion process for in-line dispersion quality control. [ABSTRACT FROM AUTHOR]

1D graphene based flexible sensors as wearable electronics have recently attracted considerable attentions because of lightweight, high extensibility, easy to wind and weave, and superior sensitivity. In this research, we established a facile and low-cost strategy to construct graphene thin film enabled yarn sensors (GYS) by combining the process of graphene oxide (GO) coating and reducing on polyester (PE) wound spandex yarns. According to systematic processing-property relationship study, a key finding of this work discovers that the degree of resistance recovery as well as gauge sensitivity of GYS can be well controlled and modulated by a pre-stretch treatment. Specifically, as the level of pre-stretch increases from 0 to 60%, the deformable range of sensor that guarantees full resistance recovery prolongs evidently from 0% to ~50%. Meanwhile, the gauge factor of GYS is tunable in the range from 6.40 to 12.06. To understand the pre-stretch process dependent sensing performance, SEM analysis was assisted to evidence the growing size of micro-cracks determining dominantly the behavior of electron transport. Lastly, to take better advantage of GYS, a new wearing mode was demonstrated by direct winding the yarn sensor on varied portions of human body for monitoring different body movements and muscle contracting & relaxing. [ABSTRACT FROM AUTHOR]

Piezoresistive properties play a vital role in the development of sensor for structural health monitoring (SHM) applications. Novel stable crack initiation method (SCIM) is established to improve the gauge factor (GF) with maximum achievable working strain region for PI tape enabled buckypaper hybrid sensors. Cracks are generated by applying strain rate-controlled tension force using dynamic mechanical analyzer (DMA). The sensor has been cycled in tension to characterize GF with crack opening. It is determined experimentally that GF increases with increasing crack opening and crack becomes unstable when opening increases above 8 µm. Tremendous improvement in GF has been observed which improved from single-digit to several hundreds. The highest GF obtained so far is ~255, showing 75 times improvement compared with the ones without the SCIM implementation. The crack initiation strain (CIS) is characterized by sonication and centrifugation time. It is determined experimentally that the maximum CIS of 3.5% can be achieved with sonication time of 40 min and increasing centrifuge time has an in-significantly dropping effect on CIS. Excellent stability/reproducibility has been proved/demonstrated on SCIM implemented sensors through a rigorous 12,500 tensile cycle test on DMA. The performance of sensor is practically demonstrated in tension and bending on glass fiber reinforced polymer (GFRP) structures. [ABSTRACT FROM AUTHOR]

The next-generation of hierarchical composites needs to have built-in functionality to continually monitor and diagnose their own health states. This paper includes a novel strategy for in-situ monitoring the processing stages of composites by co-braiding CNT-enabled fiber sensors into the reinforcing fiber fabrics. This would present a tremendous improvement over the present methods that excessively focus on detecting mechanical deformations and cracks. The CNT enabled smart fabrics, fabricated by a cost-effective and scalable method, are highly sensitive to monitor and quantify various events of composite processing including resin infusion, onset of crosslinking, gel time, degree and rate of curing. By varying curing temperature and resin formulation, the clear trends derived from the systematic study confirm the reliability and accuracy of the method, which is further verified by rheological and DSC tests. More importantly, upon wisely configuring the smart fabrics with a scalable sensor network, localized processing information of composites can be achieved in real time. In addition, the smart fabrics that are readily and non-invasively integrated into composites can provide life-long structural health monitoring of the composites, including detection of deformations and cracks. [ABSTRACT FROM AUTHOR]