The rapid development of integrated electronics and the boom in miniaturized and portable devices have increased the demand for miniaturized and on‐chip energy storage units. Currently thin‐film ...batteries or microsized batteries are commercially available for miniaturized devices. However, they still suffer from several limitations, such as short lifetime, low power density, and complex architecture, which limit their integration. Supercapacitors can surmount all these limitations. Particularly for micro‐supercapacitors with planar architectures, due to their unique design of the in‐plane electrode finger arrays, they possess the merits of easy fabrication and integration into on‐chip miniaturized electronics. Here, the focus is on the different strategies to design electrode finger arrays and the material engineering of in‐plane micro‐supercapacitors. It is expected that the advances in micro‐supercapacitors with in‐plane architectures will offer new opportunities for the miniaturization and integration of energy‐storage units for portable devices and on‐chip electronics.
In‐plane micro‐supercapacitors possess the merits of easy fabrication and integration into on‐chip electronics, and offer new opportunities for the miniaturization and integration of energy‐storage units for portable devices. Strategies to fabricate electrode finger arrays and the material engineering of in‐plane micro‐supercapacitors are discussed.
•Review recent studies on reliability, vulnerability, and resilience of transportation network.•Definitions, quantitative indices, and analyses of the three concepts are overviewed.•The ...characteristics of, and the relationship among the three concepts are investigated.•We provide remarks on the foci, measurements, and applications of the three concepts.
We review recent studies on transportation network performance under perturbations. Three representative concepts relating to network performance are covered: reliability, vulnerability, and resilience. With an overview of the definitions and the quantitative indices of these three concepts, we analyse and compare their similarities and differences in the context of transportation. These concepts differ from each other in terms of focus, measurement, and application scenario. Numerical examples are conducted to assess these concepts under different perturbation scenarios. The results indicate their rationale in reflecting network performance under perturbations, yet their outputs differ. Moreover, the relationship among the three concepts is intuitively illustrated by the analysis results.
To study the seepage and deformation characteristics of coal at high temperatures, coal samples from six different regions were selected and subjected to computed tomography (CT) scanning studies. In ...conjunction with ANSYS software, 3D reconstruction of CT images was used for the establishment of fluid-solid conjugate heat transfer model and coal thermal deformation model based on the microstructures of coal. In addition, the structure of coal was studied in 2D and 3D perspectives, followed by the analysis of seepage and deformation characteristics of coal at high temperatures. The results of this study indicated that porosity positively correlated with the fractal dimension, and the connectivity and seepage performances were roughly identical from 2D and 3D perspectives. As the porosity increased, the fractal dimension of coal samples became larger and the pore-fracture structures became more complex. As a result, the permeability of coal samples decreased. In the meantime, fluid was fully heated, generating high-temperature water at outlet. However, when the porosity was low, the outlet temperature was very high. The average deformation of coal skeleton with different pore-fracture structures at high temperatures showed a trend of initial increase and subsequent decrease with the increase of porosity and fractal dimension. The maximum deformation of coal skeleton positively correlated with connectivity but negatively correlated with the fractal dimension.
By simultaneously utilizing preview and global road information, a comfort optimization strategy which combines vehicle speed planning and preview semi-active suspension control is designed for ...autonomous vehicles. Considering that the impact of vehicle speed at the suspension vibration source is always a barrier for preview suspension control, a processing method for the road data is novelly proposed. Then, to utilize the processed data and to handle the nonlinearity of semi-active actuators, a hybrid horizon-varying (HV) model predictive control (MPC) method is given. The method can adapt to speed variation and meanwhile take the most of the road data within a fixed preview length. Further, based on the global information and considering multiple road irregularities in a driving path, a speed planning problem is established in the spatial domain and a dynamic programming based solution is provided. The final speed trajectory can compromise the driving time, vertical vibration and longitudinal acceleration. Various simulation results have been employed to verify the superiority of the hybrid HV-MPC method and the significance of speed and suspension coordination for comfort improvement.
Pre-trained language representation models (PLMs) cannot well capture factual knowledge from text. In contrast, knowledge embedding (KE) methods can effectively represent the relational facts in ...knowledge graphs (KGs) with informative entity embeddings, but conventional KE models cannot take full advantage of the abundant textual information. In this paper, we propose a unified model for
nowledge
mbedding and
re-trained
anguag
epresentation (
), which can not only better integrate factual knowledge into PLMs but also produce effective text-enhanced KE with the strong PLMs. In KEPLER, we encode textual entity descriptions with a PLM as their embeddings, and then jointly optimize the KE and language modeling objectives. Experimental results show that KEPLER achieves state-of-the-art performances on various NLP tasks, and also works remarkably well as an inductive KE model on KG link prediction. Furthermore, for pre-training and evaluating KEPLER, we construct Wikidata5M
, a large-scale KG dataset with aligned entity descriptions, and benchmark state-of-the-art KE methods on it. It shall serve as a new KE benchmark and facilitate the research on large KG, inductive KE, and KG with text. The source code can be obtained from
.
The nanosheets of Bi2MoO6 were loaded to surface of TiO2 NTs (grown on Ti mesh) via simple solvothermal method and the amount of Bi2MoO6 nanosheets was adjusted by changing reaction intervals. The ...PEC removal efficiency of RhB, MO, MB and Cr (VI) achieved 75%, 100%, 100% and 100%. The type-II heterojunction mechanism was tentatively proposed.
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The fabrication of TiO2 NTs/Bi2MoO6 type-II heterojunction photocatalyst was carried out by a simple solvothermal method. Bi2MoO6 nanoparticles with nanosheet microstructures were successfully loaded on TiO2 NTs surface through the adjustment of reaction intervals. The heterojunction photocatalyst showed excellent organic dye and heavy metal ion removal performances, and nearly 100%, 75%, 100% and 100% of MO, RhB, MB and Cr (VI) were removed by simulative sunlight irradiation for 3 or 2 h, respectively. The outstanding photocatalyic performance was mainly due to the formation of type-II heterojunction between TiO2 and Bi2MoO6. The type-II heterojunction not only enhanced visible light response but also accelerated photogenerated charge carrier transfer and restrained the recombination of photogenerated electron-hole pairs with the assistance of internal electric field.
For a phase-change material (PCM) confined in a porous structure, the interfacial interactions between the PCM and the porous skeleton are the decisive factors in latent heat storage performance. In ...this work, a novel composite PCM based on hierarchically porous TiO2 and n-octadecane was successfully synthesized. The porous TiO2 was prepared by a soft-template method, and the composite PCM was fabricated by introducing n-octadecane under vacuum. Transmission electron microscope and X-ray diffraction (XRD) results revealed that the as-prepared supporting matrix was crystalline TiO2, and N2 adsorption/desorption isotherms indicated that TiO2 has a hierarchically porous structure. For composite PCMs, Fourier transform infrared spectroscopy and XRD spectra revealed that no chemical bonds were formed between n-octadecane and TiO2. Scanning electron microscopy results showed abundant n-octadecane enclosed within the nanopores and closely bound on the surfaces of the hierarchically porous TiO2, as a result of capillary forces and interfacial tension. Porous TiO2 exhibited high adsorption for n-octadecane (50 wt%), and the relative enthalpy of the composite PCM was as high as 85.8 J/g. Compared with pure n-octadecane, the thermal conductivity of the as-prepared composite PCMs (e.g., 50 wt% n-octadecane sample) was improved by 138% with the addition of porous TiO2. After 800 melting/solidifying cycles, the composite PCMs exhibited excellent thermal reliability and high enthalpy. The influence of the TiO2 pore structure on n-octadecane crystallization behavior is shown in the results.
•Hierarchically porous TiO2 nanomaterial provided an ideal skeleton.•A novel n-octadecane/hierarchically porous TiO2 composite PCMs was synthesized.•The effect of the nanoporous structure on crystallization is demonstrated.
Underwater vital signs monitoring of respiratory rate, blood pressure, and the heart's status is essential for healthcare and sports management. Real‐time electrocardiography (ECG) monitoring ...underwater can be one solution for this. However, the current electrodes used for ECGs are not suitable for aquatic applications since they may lose their adhesiveness to skin, stable conductivity, or/and structural stability when immersed into water. Here, the design and fabrication of water‐resistant electrodes to repurpose stretchable electrodes for applications in an aquatic environment are reported. The electrodes are composed of stretchable metal–polymer composite film as the substrate and dopamine‐containing polymer as a coating. The polymer is designed to possess underwater adhesiveness from the dopamine motif, water stability from the main scaffold, and ionic conductivity from the carboxyl groups for signal transmission. Stable underwater conductivity and firm adhesion to skin allow the electrodes to collect reliable ECG signals under various conditions in water. It is shown that wearable devices incorporated with the water‐resistant electrodes can acquire real‐time ECG signals during swimming, which can be used for revealing the heart condition. These water‐resistant electrodes realize underwater detection of ECG signals and can be used for health monitoring and sports management during aquatic activities.
Water‐resistant stretchable electrodes are fabricated with a specially designed polymer. The polymer is adhesive underwater to bridge the electrode and skin, and ionic‐conductive to transmit electrophysiological signals. The conformal electrodes realize reliable electrocardiography (ECG) detection when moving the body or being impacted with water flow, which enables stable wireless real‐time ECG collection during swimming with a wearable device.
Stretchable strain sensors play a pivotal role in wearable devices, soft robotics, and Internet‐of‐Things, yet these viable applications, which require subtle strain detection under various strain, ...are often limited by low sensitivity. This inadequate sensitivity stems from the Poisson effect in conventional strain sensors, where stretched elastomer substrates expand in the longitudinal direction but compress transversely. In stretchable strain sensors, expansion separates the active materials and contributes to the sensitivity, while Poisson compression squeezes active materials together, and thus intrinsically limits the sensitivity. Alternatively, auxetic mechanical metamaterials undergo 2D expansion in both directions, due to their negative structural Poisson's ratio. Herein, it is demonstrated that such auxetic metamaterials can be incorporated into stretchable strain sensors to significantly enhance the sensitivity. Compared to conventional sensors, the sensitivity is greatly elevated with a 24‐fold improvement. This sensitivity enhancement is due to the synergistic effect of reduced structural Poisson's ratio and strain concentration. Furthermore, microcracks are elongated as an underlying mechanism, verified by both experiments and numerical simulations. This strategy of employing auxetic metamaterials can be further applied to other stretchable strain sensors with different constituent materials. Moreover, it paves the way for utilizing mechanical metamaterials into a broader library of stretchable electronics.
Auxetic mechanical metamaterials are employed to significantly enhance the sensitivity of stretchable strain sensors, by regulating the transverse Poisson effect due to auxetic expansion. High sensitivity with almost 24‐fold improvement is achieved, together with high maximum stretchability and cyclic durability. Additionally, the underlying mechanism, elongated microcracks, is proven by both experiments and numerical simulations.
Lots of learning tasks require dealing with graph data which contains rich relation information among elements. Modeling physics systems, learning molecular fingerprints, predicting protein ...interface, and classifying diseases demand a model to learn from graph inputs. In other domains such as learning from non-structural data like texts and images, reasoning on extracted structures (like the dependency trees of sentences and the scene graphs of images) is an important research topic which also needs graph reasoning models. Graph neural networks (GNNs) are neural models that capture the dependence of graphs via message passing between the nodes of graphs. In recent years, variants of GNNs such as graph convolutional network (GCN), graph attention network (GAT), graph recurrent network (GRN) have demonstrated ground-breaking performances on many deep learning tasks. In this survey, we propose a general design pipeline for GNN models and discuss the variants of each component, systematically categorize the applications, and propose four open problems for future research.
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