ABSTRACT
Triboelectric nanogenerators (TENGs) have garnered substantial attention in breeze wind energy harvesting. However, how to improve the output performance and reduce friction and wear remain challenging. To this end, a blade-type triboelectric-electromagnetic hybrid generator (BT-TEHG) with a double frequency up-conversion (DFUC) mechanism is proposed. The DFUC mechanism enables the TENG to output a high-frequency response that is 15.9 to 300 times higher than the excitation frequency of 10 to 200 rpm. Coupled with the collisions between tribomaterials, a higher surface charge density and better generating performance are achieved. The magnetization direction and dimensional parameters of the BT-TEHG were optimized, and its generating characteristics under varying rotational speeds and electrical boundary conditions were studied. At wind speeds of 2.2 and 10 m/s, the BT-TEHG can generate, respectively, power of 1.30 and 19.01 mW. Further experimentation demonstrates its capacity to charge capacitors, light up light emitting diodes (LEDs), and power wireless temperature and humidity sensors. The demonstrations show that the BT-TEHG has great potential applications in self-powered wireless sensor networks (WSNs) for environmental monitoring of intelligent agriculture.
ABSTRACT
This paper presents an efficient underwater image enhancement method, named ECO-GAN, to address the challenges of color distortion, low contrast, and motion blur in underwater robot photography. The proposed method is built upon a preprocessing framework using a generative adversarial network. ECO-GAN incorporates a convolutional neural network that specifically targets three underwater issues: motion blur, low brightness, and color deviation. To optimize computation and inference speed, an encoder is employed to extract features, whereas different enhancement tasks are handled by dedicated decoders. Moreover, ECO-GAN employs cross-stage fusion modules between the decoders to strengthen the connection and enhance the quality of output images. The model is trained using supervised learning with paired datasets, enabling blind image enhancement without additional physical knowledge or prior information. Experimental results demonstrate that ECO-GAN effectively achieves denoising, deblurring, and color deviation removal simultaneously. Compared with methods relying on individual modules or simple combinations of multiple modules, our proposed method achieves superior underwater image enhancement and offers the flexibility for expansion into multiple underwater image enhancement functions.
Subject(s)
Image Processing, Computer-Assisted , Neural Networks, Computer , Image Processing, Computer-Assisted/methods , Image Enhancement , Tomography, X-Ray Computed , MotionABSTRACT
Filler/matrix interfacial cohesion exerts a straightforward effect on stress transfer at the interface in composite structures, thereby significantly affecting their integrated mechanical properties. Thus, controlling the interface interaction of polymers/fillers is essential for the fabrication of high-performance polymer composites. In this work, a functionalized core-shell structured hybrid was prepared via charge attraction and applied as a novel filler in the trans-1,4-polyisoprene matrix to improve the interfacial interaction of the filler/matrix. A series of tests on the micro- and macroscale was performed to investigate its thermal, mechanical and shape memory performances. The obtained results show that while guaranteeing the shape memory properties of the composites, the utilization of the core-shell structured hybrid not only improved the heat resistant performance, but also contributed to better mechanical properties. This provides solid evidence for the potential of the innovative method presented herein, which may shed some light on the improvement of the interface design strategy and the development of composites with high performances.
ABSTRACT
Regulating the interfacial interaction between fillers and matrices is crucial for fabricating high-performance polymer composites. In this research, a functionalized core-shell hybrid silica@graphene oxide was produced by the charge attraction method, and then added to a trans-1,4-polyisoprene matrix as a neoteric filler to obtain a brand-new silica@graphene oxide/trans-1,4-polyisoprene polymer nanocomposite. The hybrid incorporation simultaneously improved the fracture toughness, mechanical strength and heat resistance of the nanocomposites. We examined the thermal, mechanical and shape memory properties of the nanocomposites via methodical measurements from the microscale to the macroscale. The experimental results demonstrated that, compared with other samples, the nanocomposite sample with 1.0 wt% silica@graphene oxide exhibited the best mechanical and thermal performance, and the fabricated nanocomposites showed good shape memory properties. This new and feasible approach is likely to enable a new strategy for the design of interfaces for developing nanocomposites with high performance.
ABSTRACT
As important observational platforms for the Smart Ocean concept, autonomous underwater vehicles (AUVs) that perform long-term observation in fleets are beneficial because they provide large-scale sampling data with a sufficient spatiotemporal resolution. Therefore, a large number of low-cost micro AUVs with docking capability for power recharge and data transmission are essential. This study designed a low-cost electromagnetic docking guidance (EMDG) system for micro AUVs. The EMDG system is composed of a transmitter coil located on the dock and a three-axial search coil magnetometer acting as a receiver. The search coil magnetometer was optimized for small sizes while maintaining sufficient sensitivity. The signal conditioning and processing subsystem was designed to calculate the deflection angle (ß) for docking guidance. Underwater docking tests showed that the system can detect the electromagnetic signal and successfully guide AUV docking. The AUV can still perform docking in extreme positions, which cannot be realized through normal optical or acoustic guidance. This study is the first to focus on the EM guidance system for low-cost micro AUVs. The search coil sensor in the AUV is inexpensive and compact so that the system can be equipped on a wide range of AUVs.
ABSTRACT
In this work, we have synthesized a novel graphene oxide/trans-1,4-polyisoprene (GO/TPI) shape memory polymer (SMP) nanocomposite by adding GO (0.0-1.5 phr) to bulk TPI polymer to enhance its mechanical properties. We have studied and examined its mechanical, thermal and thermomechanical properties through systematic tests from microscale to macroscale. The shape memory properties and the effect of the GO content on the thermal and mechanical properties of the manufactured SMP nanocomposites were studied by differential scanning calorimetry, dynamic mechanical analysis, thermogravimetric analysis, thermal conductivity, and static tensile and mechanical as well as thermomechanical cyclic loading tests. The experimental results indicate that the SMP nanocomposite with 0.9 phr GO has superior thermal and mechanical properties compared to all other cases, and good shape memory was obtained in the GO/TPI nanocomposites.
ABSTRACT
Polymer composites are sensitive to impact loading due to their low impact resistance. Shape memory alloy (SMA) wires have been used to improve the impact resistance of the polymer composite materials because of their unique superelasticity performance. In this study, a new SMA hybrid basalt fiber-reinforced polymer composite embedded with two perpendicular layers of superelastic SMA wires is designed and the low-velocity impact behavior is experimental investigated. For contrast, the conventional polymer composite without SMA wires is also tested as the reference laminate. The tests are carried out at three different impact energy levels (30, 60 and 90 J). Moreover, to find out indications for manufacturing of SMA hybrid composites with high impact resistance, four different SMA wires embedded modes are investigated. Visual inspection and scanning electron microscope methods are adopted to identify the damage modes of the impacted samples. Results show that the impact resistance of the hybrid laminates is improved due to the hybridization of SMA wires. The most effective impact resistance of the SMA hybrid composites can be obtained by incorporating the SMA wires with one layer between the front two plies and another layer between the bottom two plies into the composite structure.
ABSTRACT
Trans-l,4-polyisoprene (TPI) shape memory polymer composites with different chopped carbon fiber mass fractions were prepared to study the effects of different chopped carbon fiber mass fractions and temperatures on the TPI shape memory polymer composites in this paper. While guaranteeing the shape memory effect of TPI shape memory polymers, the carbon fiber fillers also significantly enhanced the mechanical properties of the polymers. The thermodynamic properties and shape memory properties of TPI shape memory polymers were studied by a differential scanning calorimeter (DSC) test, dynamic mechanical analysis (DMA) test, thermal conductivity test, static tensile test, mechanical cycle test, thermodynamic cycling test and shape memory test. Furthermore, the tensile fracture interface of TPI shape memory polymer composites was analyzed by scanning electron microscopy. The experimental results show that when the chopped carbon mass fraction fiber is 8%, TPI shape memory polymers have good shape memory properties and the best mechanical properties.