The design and utilization of polymers with healing capability have drawn increasing attention owing to their enhanced chain mobility and opportunity to heal minor cracks in composites. Rehealable thermoset polymers promise reduction in the maintenance cost and thus prolonged lifetime, reshaping, and recyclability. Introducing reversible covalent bonds is the mainstay strategy to achieve such plasticity in crosslinked polymers. Herein, we report a dynamic epoxy, which includes associative covalent adaptive networks (CANs) based on disulfide exchange bonds. Epoxy resin is chosen to study rehealing, as it is one of the most critical thermosetting polymers for various industries from aerospace to soft robotics. This study enlightens us about not only the consequences of CANs in the epoxy but also various factors such as soft segments and carbon nanotubes (CNTs). Epoxy dynamic networks are investigated in an attempt to explore the synergistic effect of the soft-segmented resins and CNTs on the healing and reshaping characteristics of epoxy networks along with varying stiffness. This research discusses epoxy dynamic networks in three main aspects: crosslink density, CAN density, and CNTs. Introducing soft segments into the epoxy network enhances the healing efficiency due to the increased chain mobility. A higher CAN density accelerates network rearrangement, improving the healing efficiency. It should also be noted that even with a low weight fraction of nanotubes, CNT-reinforced samples restored their initial strength more than neat samples after healing. The tensile strength of dynamic networks is at least 50 MPa, which is significant for their utility in primary or secondary structural components.
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http://dx.doi.org/10.1021/acsomega.2c03910 | DOI Listing |
Polymers (Basel)
January 2025
Departamento de Química Física, Facultad de Ciencias Químicas, Universidad Complutense de Madrid, Ciudad Universitaria, Plaza de la Ciencias s/n, 28040 Madrid, Spain.
This study examines the adsorption and bulk assembly behaviour of quaternized hydroxyethylcellulose ethoxylate (QHECE)-sodium dodecyl sulphate (SDS) complexes on negatively charged substrates. Due to its quaternized structure, QHECE, which is used in several industries, including cosmetics, exhibits enhanced electrostatic interactions. The phase behaviour and adsorption mechanisms of QHECE-SDS complexes are investigated using model substrates that mimic the wettability and surface charge of damaged hair fibres.
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January 2025
School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China.
With the rapid development of AI algorithms and computational power, object recognition based on deep learning frameworks has become a major research direction in computer vision. UAVs equipped with object detection systems are increasingly used in fields like smart transportation, disaster warning, and emergency rescue. However, due to factors such as the environment, lighting, altitude, and angle, UAV images face challenges like small object sizes, high object density, and significant background interference, making object detection tasks difficult.
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January 2025
State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China.
As advancements in autonomous underwater vehicle (AUV) technology unfold, the role of underwater wireless sensor networks (UWSNs) is becoming increasingly pivotal. However, the high energy consumption in these networks can significantly reduce their operational lifespan, while latency issues can impair overall network performance. To address these challenges, a novel mixed packet forwarding strategy is developed, which incorporates a wakeup threshold and a dynamically adjusted access probability for the cluster head (CH).
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January 2025
Shanghai Film Academy, Shanghai University, Shanghai 200072, China.
The advancement of neural radiance fields (NeRFs) has facilitated the high-quality 3D reconstruction of complex scenes. However, for most NeRFs, reconstructing 3D tissues from endoscopy images poses significant challenges due to the occlusion of soft tissue regions by invalid pixels, deformations in soft tissue, and poor image quality, which severely limits their application in endoscopic scenarios. To address the above issues, we propose a novel framework to reconstruct high-fidelity soft tissue scenes from low-quality endoscopic images.
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January 2025
State Key Laboratory of Satellite Navigation System and Equipment Technology, The 54th Research Institute, China Electronics Technology Group Corporation (CETC), Shijiazhuang 050081, China.
Intelligent unmanned clusters have played a crucial role in military reconnaissance, disaster rescue, border patrol, and other domains. Nevertheless, due to factors such as multipath propagation, electromagnetic interference, and frequency band congestion in high dynamic scenarios, unmanned cluster networks experience frequent topology changes and severe spectrum limitations, which hinder the provision of connected, elastic and autonomous network support for data interaction among unmanned aerial vehicle (UAV) nodes. To address the conflict between the demand for reliable data transmission and the limited network resources, this paper proposes an AODV routing protocol based on node energy consumption and mobility optimization (AODV-EM) from the perspective of network routing protocols.
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