Graphene paper (GP) has attracted great attention as a heat dissipation material due to its unique thermal transfer property exceeding the limit of graphite. However, the relatively poor thermal transfer properties in the normal direction of GP restricts its wider applications in thermal management. In this work, a 3D bridged carbon nanoring (CNR)/graphene hybrid paper is constructed by the intercalation of polymer carbon source and metal catalyst particles, and the subsequent in situ growth of CNRs in the confined intergallery spaces between graphene sheets through thermal annealing. Further investigation demonstrates that the CNRs are covalently bonded to the graphene sheets and highly improve the thermal transport in the normal direction of the CNR/graphene hybrid paper. This full-carbon architecture shows excellent heat dissipation ability and is much more efficient in removing hot spots than the reduced GP without CNR bridges. This highly thermally conductive CNR/graphene hybrid paper can be easily integrated into next generation commercial high-power electronics and stretchable/foldable devices as high-performance lateral heat spreader materials. This full-carbon architecture also has a great potential in acting as electrodes in supercapacitors or hydrogen storage devices due to the high surface area.
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http://dx.doi.org/10.1002/smll.201501878 | DOI Listing |
Sci Rep
December 2024
Department of Information Security, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, 632014, Tamil Nadu, India.
In Internet of Things (IoT) networks, identifying the primary Medium Access Control (MAC) layer protocol which is suited for a service characteristic is necessary based on the requirements of the application. In this paper, we propose Energy Efficient and Group Priority MAC (EEGP-MAC) protocol using Hybrid Q-Learning Honey Badger Algorithm (QL-HBA) for IoT Networks. This algorithm employs reinforcement agents to select an environment based on predefined actions and tasks.
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December 2024
Department of Electrical Engineering, College of Engineering, King Saud University, Riyadh, 11421, Saudi Arabia.
The world is moving towards the utilization of hydrogen vehicle technology because its advantages are uniformity in power production, more efficiency, and high durability when compared to fossil fuels. So, in this work, the Proton Exchange Membrane Fuel Stack (PEMFS) device is selected for producing the energy for the hydrogen vehicle. The merits of this fuel technology are the possibility of operating less source temperature, and more suitability for stationery and transportation applications.
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December 2024
College of Electric Engineering, Naval University of Engineering, Wuhan, 430033, China.
To address the challenges related to active power dissipation and node voltage fluctuation in the practical transformation of power grids in the field of new energy such as wind and photovoltaic power generation, an improved Dung Beetle Optimization Algorithm Based on a Hybrid Strategy of Levy Flight and Differential Evolution (LDEDBO) is proposed. This paper systematically addresses this issue from three aspects: firstly, optimizing the DBO algorithm using Chebyshev chaotic mapping, Levy flight strategy, and differential evolution algorithm; secondly, validating the algorithm's feasibility through real-time network reconfiguration at random time points within a 24-h period; and finally, applying the LDEDBO to address the dynamic reconfiguration problems of the IEEE-33 and IEEE-69 node bus. The simulation indicates that the power dissipation of the IEEE-33 node bus is decreased by 28.
View Article and Find Full Text PDFLithofacies classification and identification are of great significance in the exploration and evaluation of tight sandstone reservoirs. Existing methods of lithofacies identification in tight sandstone reservoirs face issues such as lengthy manual classification, strong subjectivity of identification, and insufficient sample datasets, which make it challenging to analyze the lithofacies characteristics of these reservoirs during oil and gas exploration. In this paper, the Fuyu oil formation in the Songliao Basin is selected as the target area, and an intelligent method for recognizing the lithophysics reservoirs in tight sandstone based on hybrid multilayer perceptron (MLP) and multivariate time series (MTS-Mixers) is proposed.
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December 2024
Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran.
Surface electromyography (sEMG) data has been extensively utilized in deep learning algorithms for hand movement classification. This paper aims to introduce a novel method for hand gesture classification using sEMG data, addressing accuracy challenges seen in previous studies. We propose a U-Net architecture incorporating a MobileNetV2 encoder, enhanced by a novel Bidirectional Long Short-Term Memory (BiLSTM) and metaheuristic optimization for spatial feature extraction in hand gesture and motion recognition.
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