•Enable CNN-based physics-informed deep learning for PDEs on irregular domain.•The proposed network can be trained without any labeled data.•Boundary conditions are strictly encoded in a hard ...manner.•Investigated complex parametric PDEs, e.g., Naiver-Stokes with varying geometries.•Shows improvements of efficiency and accuracy over FC-NN formulations.
Recently, the advent of deep learning has spurred interest in the development of physics-informed neural networks (PINN) for efficiently solving partial differential equations (PDEs), particularly in a parametric setting. Among all different classes of deep neural networks, the convolutional neural network (CNN) has attracted increasing attention in the scientific machine learning community, since the parameter-sharing feature in CNN enables efficient learning for problems with large-scale spatiotemporal fields. However, one of the biggest challenges is that CNN only can handle regular geometries with image-like format (i.e., rectangular domains with uniform grids). In this paper, we propose a novel physics-constrained CNN learning architecture, aiming to learn solutions of parametric PDEs on irregular domains without any labeled data. In order to leverage powerful classic CNN backbones, elliptic coordinate mapping is introduced to enable coordinate transforms between the irregular physical domain and regular reference domain. The proposed method has been assessed by solving a number of steady-state PDEs on irregular domains, including heat equations, Navier-Stokes equations, and Poisson equations with parameterized boundary conditions, varying geometries, and spatially-varying source fields. Moreover, the proposed method has also been compared against the state-of-the-art PINN with fully-connected neural network (FC-NN) formulation. The numerical results demonstrate the effectiveness of the proposed approach and exhibit notable superiority over the FC-NN based PINN in terms of efficiency and accuracy.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Their highly functional nature has endowed metal–organic frameworks (MOFs) with diverse applications. On this basis, a higher demand has been proposed for the preparation of novel‐structured MOFs. ...Hollow MOFs have been intensively studied and exhibited versatile properties, and among the various methods, secondary‐component incorporation has been proved promising in the design and preparation of complex structures with requisite properties. Herein, the synthesis and applications of secondary component incorporated MOFs and their derivatives are systematically reviewed. Two main methodologies, preincorporation and postmodification, are discussed in detail, and the role of the secondary component is demonstrated. Based on these introductions, the applications of those materials, including chemical catalysis, electrocatalysis, and energy storage applications, are summarized. Finally, a personal outlook for the future opportunities and challenges in this field is given.
The synthetic procedures toward novel secondary‐component‐incorporated metal–organic frameworks (MOFs) and their derivatives are systematically reviewed in terms of synthetic routes. The manipulation of chemical components, electronic structures, and morphologies is demonstrated in detail. Novel‐structured MOFs and MOF‐derived nanomaterials with improved functionalities are desirable for diverse catalytic reactions, energy conversion, and storage reactions.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Numerical simulations on fluid dynamics problems primarily rely on spatially or/and temporally discretization of the governing equation using polynomials into a finite-dimensional algebraic system. ...Due to the multi-scale nature of the physics and sensitivity from meshing a complicated geometry, such process can be computational prohibitive for most real-time applications (e.g., clinical diagnosis and surgery planning) and many-query analyses (e.g., optimization design and uncertainty quantification). Therefore, developing a cost-effective surrogate model is of great practical significance. Deep learning (DL) has shown new promises for surrogate modeling due to its capability of handling strong nonlinearity and high dimensionality. However, the off-the-shelf DL architectures, success of which heavily relies on the large amount of training data and interpolatory nature of the problem, fail to operate when the data becomes sparse. Unfortunately, data is often insufficient in most parametric fluid dynamics problems since each data point in the parameter space requires an expensive numerical simulation based on the first principle, e.g., Navier–Stokes equations. In this paper, we provide a physics-constrained DL approach for surrogate modeling of fluid flows without relying on any simulation data. Specifically, a structured deep neural network (DNN) architecture is devised to enforce the initial and boundary conditions, and the governing partial differential equations (i.e., Navier–Stokes equations) are incorporated into the loss of the DNN to drive the training. Numerical experiments are conducted on a number of internal flows relevant to hemodynamics applications, and the forward propagation of uncertainties in fluid properties and domain geometry is studied as well. The results show excellent agreement on the flow field and forward-propagated uncertainties between the DL surrogate approximations and the first-principle numerical simulations.
•Proposed a simulation-free, physics-constrained deep learning for surrogate CFD model.•Boundary-encoded neural network outperforms the one with soft boundary constraints.•Demonstrated effectiveness of the label-free learning on a few vascular flows.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Developing advanced high‐rate electrode materials has been a crucial aspect for next‐generation lithium ion batteries (LIBs). A conventional nanoarchitecturing strategy is suggested to improve the ...rate performance of materials but inevitably brings about compromise in volumetric energy density, cost, safety, and so on. Here, micro‐size Nb14W3O44 is synthesized as a durable high‐rate anode material based on a facile and scalable solution combustion method. Aberration‐corrected scanning transmission electron microscopy reveals the existence of open and interconnected tunnels in the highly crystalline Nb14W3O44, which ensures facile Li+ diffusion even within micro‐size particles. In situ high‐energy synchrotron XRD and XANES combined with Raman spectroscopy and computational simulations clearly reveal a single‐phase solid‐solution reaction with reversible cationic redox process occurring in the NWO framework due to the low‐barrier Li+ intercalation. Therefore, the micro‐size Nb14W3O44 exhibits durable and ultrahigh rate capability, i.e., ≈130 mAh g−1 at 10 C, after 4000 cycles. Most importantly, the micro‐size Nb14W3O44 anode proves its highest practical applicability by the fabrication of a full cell incorporating with a high‐safety LiFePO4 cathode. Such a battery shows a long calendar life of over 1000 cycles and an enhanced thermal stability, which is superior than the current commercial anodes such as Li4Ti5O12.
Micro‐size Nb14W3O44 with interconnected tunnel structure is synthesized by a facile solution combustion method. Li+ insertion/extraction in Nb14W3O44 is a single‐phase solid‐solution electrochemical mechanism, leading to high Li+ diffusion coefficient and excellent structural stability during cycling. The as‐prepared Nb14W3O44 exhibits ultrahigh‐rate and high‐safety Li+ storage performance.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Macrophages play an important role in a wide variety of physiologic and pathologic processes. Plasticity and functional polarization are hallmarks of macrophages. Macrophages commonly exist in two ...distinct subsets: classically activated macrophages (M1) and alternatively activated macrophages (M2). M2b, a subtype of M2 macrophages, has attracted increasing attention over the past decade due to its strong immune‐regulated and anti‐inflammatory effects. A wide variety of stimuli and multiple factors modulate M2b macrophage polarization in vitro and in vivo. M2b macrophages possess both protective and pathogenic roles in various diseases. Understanding the mechanisms of M2b macrophage activation and the modulation of their polarization might provide a great perspective for the design of novel therapeutic strategies. The purpose of this review is to discuss current knowledge of M2b macrophage polarization, the roles of M2b macrophages in a variety of diseases and the stimuli to modulate M2b macrophage polarization.
Review outlines the current knowledge of the stimuli of M2b macrophage polarization and the roles of these cells in diseases.
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The fabrication strategies, characterizations, photocatalytic performances and the corresponding mechanism of some typical TiO2/MOF composites were highlighted and reviewed. Also, the prospective and ...challenges of TiO2/MOF composites as photocatalysts were declared.
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•The facile fabrication strategies of TiO2/MOF composites were highlighted.•The enhanced photocatalytic activities of TiO2/MOF composites were reviewed.•The possible photocatalysis mechanisms over TiO2/MOF composites were summed.•The composite fabrication is a good approach to combine the merits of TiO2 and MOF.•The prospective of TiO2/MOF composites as photocatalysts is declared.
Up to now, titanium dioxide (TiO2) is the most established semiconductor photocatalyst, which is used to achieve photocatalytic H2 evolution, pollutants degradation, CO2 reduction, and N2 reduction under UV light irradiation. TiO2 as photocatalyst is always under the spotlight due to its unique properties like outstanding thermal/chemical stability, wide bandgap with suitable band edge, low cost, non-toxicity, and corrosion resistance. To further improve the photocatalytic activity of TiO2, the versatile and porous metal-organic frameworks (MOFs) can be introduced to constructionTiO2/MOF composites, which can accomplish the enhanced light absorption performance and improved electron-hole pair separation. With this review, the fabrication strategies, characterizations techniques, photocatalytic activities and the mechanisms of some selected TiO2/MOF composites were reviewed and highlighted. The last but not the least, the outlooks and challenges of TiO2/MOF composites as photocatalysts for energy conversion and environment remediation are proposed.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The China Spallation Neutron Source is expected to produce its first beam in 2017. Hesheng Chen and Xun-Li Wang provide an overview of this user facility and what it means for science in China and ...elsewhere. Neutron scattering is a powerful tool for materials researchers and industries. Over the years, it has made substantial contributions to many areas of physics, chemistry, biology, materials science and materials engineering. For example, neutron scattering played a crucial role in elucidating the interplay between spin uctuations and superconductivity in high-temperature superconductors1. In addition, because of their characteristic energy scales, cold neutrons are uniquely positioned to probe dynamic processes in so matter2, where applications range.
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The development of bifunctional electrocatalysts with high performance for both hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) with earth-abundant elements is still a challenge ...in electrochemical water splitting technology. Herein, we fabricated a free-standing electrocatalyst in the form of vertically oriented Fe-doped Ni3S2 nanosheet array grown on three-dimensional (3D) Ni foam (Fe-Ni3S2/NF), which presented a high activity and durability for both HER and OER in alkaline media. On the basis of systematic experiments and calculation, the Fe-doping was evidenced to increase the electrochemical surface area, improve the water adsorption ability, and optimize the hydrogen adsorption energy of Ni3S2, which resulted in the enhancement of HER activity on Fe-Ni3S2/NF. Moreover, metal sites of Fe-Ni3S2/NF were proved to play a significant role in the HER process. During the catalysis of OER, the formation of Ni–Fe (oxy)hydroxide was observed on the near-surface section of Fe-Ni3S2/NF, and the introduction of the Fe element dramatically enhanced the OER activity of Ni3S2. The overall water splitting electrolyzer assembled by Fe-Ni3S2/NF exhibited a low cell voltage (1.54 V @ 10 mA cm–2) and a high durability in 1 M KOH. This work demonstrated a promising bifunctional electrocatalyst for water electrolysis in alkaline media with potential application in the future.
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