226 research outputs found
Interfacial Thermal Resistance Between Carbon Nanotubes: Molecular Dynamics Simulations and Analytical Thermal Modeling
Interfacial thermal transport between offset parallel (10,10) single-wall carbon nanotubes is investigated by molecular dynamics simulation and analytical thermal modeling as a function of nanotube spacing, overlap, and length. A four order of magnitude reduction in interfacial thermal resistance is found as the nanotubes are brought into intimate contact. A reduction is also found for longer nanotubes and for nanotubes with increased overlap area. Thermal resistance between a nanotube and a reservoir at its boundary increases with decreasing reservoir temperature. Additionally, length-dependent Young\u27s moduli and damping coefficients are calculated based on observed nanotube deflections
VQ-NeRF: Neural Reflectance Decomposition and Editing with Vector Quantization
We propose VQ-NeRF, a two-branch neural network model that incorporates
Vector Quantization (VQ) to decompose and edit reflectance fields in 3D scenes.
Conventional neural reflectance fields use only continuous representations to
model 3D scenes, despite the fact that objects are typically composed of
discrete materials in reality. This lack of discretization can result in noisy
material decomposition and complicated material editing. To address these
limitations, our model consists of a continuous branch and a discrete branch.
The continuous branch follows the conventional pipeline to predict decomposed
materials, while the discrete branch uses the VQ mechanism to quantize
continuous materials into individual ones. By discretizing the materials, our
model can reduce noise in the decomposition process and generate a segmentation
map of discrete materials. Specific materials can be easily selected for
further editing by clicking on the corresponding area of the segmentation
outcomes. Additionally, we propose a dropout-based VQ codeword ranking strategy
to predict the number of materials in a scene, which reduces redundancy in the
material segmentation process. To improve usability, we also develop an
interactive interface to further assist material editing. We evaluate our model
on both computer-generated and real-world scenes, demonstrating its superior
performance. To the best of our knowledge, our model is the first to enable
discrete material editing in 3D scenes.Comment: Accepted by TVCG. Project Page:
https://jtbzhl.github.io/VQ-NeRF.github.io
Swing surfaces and holographic entanglement beyond AdS/CFT
We propose a holographic entanglement entropy prescription for general states
and regions in two models of holography beyond AdS/CFT known as flat/BMSFT
and (W)AdS/WCFT. Flat/BMSFT is a candidate of holography for
asymptotically flat three-dimensional spacetimes, while (W)AdS/WCFT is
relevant in the study of black holes in the real world. In particular, the
boundary theories are examples of quantum field theories that feature an
infinite dimensional symmetry group but break Lorentz invariance. Our
holographic entanglement entropy proposal is given by the area of a swing
surface that consists of ropes, which are null geodesics emanating from the
entangling surface at the boundary, and a bench, which is a spacelike geodesic
connecting the ropes. The proposal is supported by an extension of the
Lewkowycz-Maldacena argument, reproduces previous results based on the Rindler
method, and satisfies the first law of entanglement entropy.Comment: 45 pages, 4 figures; v2: corrected typos and added comments on strong
subadditivity, matches published versio
Modular Hamiltonians in flat holography and (W)AdS/WCFT
We study several aspects of holographic entanglement in two models known as
flat/BMSFT and (W)AdS/WCFT. These are two examples of holography beyond
AdS/CFT where the the boundary field theories are not Lorentz invariant but
still feature an infinite set of local symmetries. In the first example,
BMS-invariant field theories (BMSFTs) are conjectured to provide a holographic
description of quantum gravity in asymptotically flat three-dimensional
spacetimes; while in the second example, warped conformal field theories
(WCFTs) are proposed to describe quantum gravity in warped AdS or AdS
backgrounds with Dirichlet-Neumann boundary conditions. In particular, we
derive the modular Hamiltonian for single intervals in both BMSFTs and WCFTs
and find the holographic duals in the bulk using the covariant formulation of
gravitational charges. We also extend the first law of entanglement entropy to
these models of non-AdS holography and discuss the bound on "modular chaos"
introduced recently in the context of the AdS/CFT correspondence.Comment: 46 pages, 3 figures; v2: corrected typos, matches published versio
FollowNet: A Comprehensive Benchmark for Car-Following Behavior Modeling
Car-following is a control process in which a following vehicle (FV) adjusts
its acceleration to keep a safe distance from the lead vehicle (LV). Recently,
there has been a booming of data-driven models that enable more accurate
modeling of car-following through real-world driving datasets. Although there
are several public datasets available, their formats are not always consistent,
making it challenging to determine the state-of-the-art models and how well a
new model performs compared to existing ones. In contrast, research fields such
as image recognition and object detection have benchmark datasets like
ImageNet, Microsoft COCO, and KITTI. To address this gap and promote the
development of microscopic traffic flow modeling, we establish a public
benchmark dataset for car-following behavior modeling. The benchmark consists
of more than 80K car-following events extracted from five public driving
datasets using the same criteria. These events cover diverse situations
including different road types, various weather conditions, and mixed traffic
flows with autonomous vehicles. Moreover, to give an overview of current
progress in car-following modeling, we implemented and tested representative
baseline models with the benchmark. Results show that the deep deterministic
policy gradient (DDPG) based model performs competitively with a lower MSE for
spacing compared to traditional intelligent driver model (IDM) and
Gazis-Herman-Rothery (GHR) models, and a smaller collision rate compared to
fully connected neural network (NN) and long short-term memory (LSTM) models in
most datasets. The established benchmark will provide researchers with
consistent data formats and metrics for cross-comparing different car-following
models, promoting the development of more accurate models. We open-source our
dataset and implementation code in
https://github.com/HKUST-DRIVE-AI-LAB/FollowNet
High-performance supercapacitors based on hierarchically porous carbons with a three-dimensional conductive network structure
Clews of polymer nanobelts (CsPNBs) have the advantages of inexpensive raw materials, simple synthesis and large output. Novel clews of carbon nanobelts (CsCNBs) have been successfully prepared by carbonizing CsPNBs and by KOH activation subsequently. From the optimized process, CsCNBs*4, with a specific surface area of 2291 m2 g−1 and a pore volume of up to 1.29 cm3 g−1, has been obtained. Fundamentally, the CsCNBs possess a three-dimensional conductive network structure, a hierarchically porous framework, and excellent hydrophilicity, which enable fast ion diffusion through channels and a large enough ion adsorption/desorption surface to improve electrochemical performance of supercapacitors. The product exhibits a high specific capacitance of 327.5 F g−1 at a current density of 0.5 A g−1 in a three-electrode system. The results also reveal a high-rate capacitance (72.2% capacitance retention at 500 mV s−1) and stable cycling lifetime (95% of initial capacitance after 15 000 cycles). Moreover, CsCNBs*4 provides a high energy density of 29.8 W h kg−1 at a power density of 345.4 W kg−1 in 1 M tetraethylammonium tetrafluoroborate/acetonitrile (TEABF4/AN) electrolyte. These inspiring results imply that this carbon material with a three-dimensional conductive network structure possesses excellent potential for energy storage
Optimized synthesis of ultrahigh-surface-area and oxygen-doped carbon nanobelts for high cycle-stability lithium-sulfur batteries
Hierarchical clews of carbon nanobelts (CsCNBs) with ultrahigh specific surface area (2300 m2 g−1) and large pore volume (up to 1.29 cm3 g−1) has been successfully fabricated through carbonization and KOH activation of phenolic resin based nanobelts. The product possesses hierarchically porous structure, three-dimensional conductive network framework, and polar oxygen-rich groups, which are very befitting to load sulfur leading to excellent cycling stability of lithium-sulfur batteries. The composites of CsCNBs/sulfur exhibit an ultrahigh initial discharge capacity of 1245 mA h g−1 and ultralow capacity decay rate as low as 0.162% per cycle after 200 cycles at 0.1 C. Even at high current rate of 4 C, the cells still display a high initial discharge capacity (621 mA h g−1) and ultralow capacity decay rate (only 0.039% per cycle) after 1000 cycles. These encouraging results indicate that polar oxygen-containing functional groups are important for improving the electrochemical performance of carbons. The oxygen-doped carbon nanobelts have excellent energy storage potential in the field of energy storage
Ultrahigh-content nitrogen-decorated nanoporous carbon derived from metal organic frameworks and its application in supercapacitors
Single electric double-layer capacitors cannot meet the growing demand for energy due to their insufficient energy density. Generally speaking, the supercapacitors introduced with pseudo-capacitance by doping heteroatoms (N, O) in porous carbon materials can obtain much higher capacitance than electric double-layer capacitors. In view of above merits, in this study, nanoporous carbon materials with ultrahigh N enrichment (14.23 wt%) and high specific surface area (942 m2 g−1) by in situ introduction of N-doped MOF (ZTIF-1, Organic ligands 5-methyltetrazole/C2H4N4) were produced. It was found that as supercapacitors' electrode materials, these nanoporous carbons exhibit a capacitance as high as 272 F g-1 at 0.1 A g−1, and an excellent cycle life (almost no attenuation after 10,000 cycles.). Moreover, the symmetric supercapacitors were assembled to further investigate the actual capacitive performance, and the capacitance shows up to 154 F g-1 at 0.1 A g−1. Such excellent properties may be attributed to a combination of a high specific surface area, ultrahigh nitrogen content and hierarchically porous structure. The results shown in this study fully demonstrate that the nanoporous carbon materials containing ultrahigh nitrogen content can be used as a potential electrode material in supercapacitors
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