Owing to their unique chemical and physical properties, hydrogels are attracting increasing attention in both basic and translational biomedical studies. Although the classical hydrogels with static ...networks have been widely reported for decades, a growing number of recent studies have shown that structurally dynamic hydrogels can better mimic the dynamics and functions of natural extracellular matrix (ECM) in soft tissues. These synthetic materials with defined compositions can recapitulate key chemical and biophysical properties of living tissues, providing an important means to understanding the mechanisms by which cells sense and remodel their surrounding microenvironments. This review begins with the overall expectation and design principles of dynamic hydrogels. We then highlight recent progress in the fabrication strategies of dynamic hydrogels including both degradation-dependent and degradation-independent approaches, followed by their unique properties and use in biomedical applications such as regenerative medicine, drug delivery, and 3D culture. Finally, challenges and emerging trends in the development and application of dynamic hydrogels are discussed.
To achieve the electrochemical nitrogen reduction reaction (NRR) for efficient and sustainable NH3 production, catalysts should exhibit high selectivity and activity with optimal adsorption energy. ...Herein we developed a three‐dimensional (3D) amorphous BiNi alloy toward a significantly enhanced NRR compared with its crystalline and metal counterparts. Ni alloying enables the chemisorption of nitrogen and the lower free‐energy change for the *NNH formation, and the 3D alloy electrocatalyst exhibits high catalytic activity for NH3 production with a yield rate of 17.5 μg h−1 mgcat−1 and Faradaic efficiency of 13.8 %. The enhanced electron transfer and increased electrochemical surface area were revealed in the interconnected porous scaffold, affording it sufficiently efficient and stable activity for potential practical applications. This work offers new insights into optimizing the adsorption energy of reactants and intermediates combined with tuning the crystallinity of NRR electrocatalysts.
A 3D amorphous BiNi alloy gives a significantly enhanced electrocatalytic nitrogen fixation. Ni substitution in Bi enables the chemisorption of nitrogen and a lower ΔG for *NNH formation. Enhanced electron transfer and increased electrochemical surface area in the interconnected porous scaffold allow its efficient and stable activity for potential practical applications.
2D nanomaterials provide numerous fascinating properties, such as abundant active surfaces and open ion diffusion channels, which enable fast transport and storage of lithium ions and beyond. ...However, decreased active surfaces, prolonged ion transport pathway, and sluggish ion transport kinetics caused by self‐restacking of 2D nanomaterials during electrode assembly remain a major challenge to build high‐performance energy storage devices with simultaneously maximized energy and power density as well as long cycle life. To address the above challenge, porosity (or hole) engineering in 2D nanomaterials has become a promising strategy to enable porous 2D nanomaterials with synergetic features combining both 2D nanomaterials and porous architectures. Herein, recent important progress on porous/holey 2D nanomaterials for electrochemical energy storage is reviewed, starting with the introduction of synthetic strategies of porous/holey 2D nanomaterials, followed by critical discussion of design rule and their advantageous features. Thereafter, representative work on porous/holey 2D nanomaterials for electrochemical capacitors, lithium‐ion and sodium‐ion batteries, and other emerging battery technologies (lithium‐sulfur and metal‐air batteries) are presented. The article concludes with perspectives on the future directions for porous/holey 2D nanomaterial in energy storage and conversion applications.
Holey 2D nanomaterials have shown great promise to address several key challenges of decreased active surfaces, prolonged ion transport pathway, and sluggish ion transport kinetics caused by self‐restacking of 2D materials. Recent progress of porous 2D nanomaterials, in terms of the synthetic strategies and their applications in electrochemical energy storage, as well as the future development are summarized.
In this paper, we propose a physical informed neural network approach for designing the electromagnetic metamaterial. The approach can be used to deal with various practical problems such as ...cloaking, rotators, concentrators, etc. The advantage of this approach is the flexibility that we can deal with not only the continuous parameters but also the piecewise constants. As our best knowledge, there is no other faster and much efficient method to deal with these problems. As a byproduct, we propose a method to solve high frequency Helmholtz equation, which is widely used in physics and engineering. Some benchmark problems have been solved in numerical tests to verify our method.
Seeking sustainable and cost‐effective energy sources is one of the significant challenges for the sustainable development of modern society. To date, considerable expectations have been held for ...technologies, such as fuel cells and electrolyzers, where the performance strongly depends on electrochemical conversion processes that can generate and store chemical energy through the breaking or formation of chemical bonds. However, those advanced technologies are severely limited by the efficiency, selectivity, and durability of electrocatalysis. Thanks to their hierarchically porous architecture, compositional and structural tunability, and ease of functionalization, the family of gel materials opens exciting opportunities for advanced energy technologies. Unique advances in gel materials based on controllable compositions and functions enable gel electrocatalysts to potentially break the limitations of current materials, enhancing the device performance of electrochemical energy. Here, recent developments and challenges for nanostructured gel‐based materials for electrocatalysis applications are summarized. Future possibilities and challenges for gel electrocatalysts in terms of synthesis and applications are also discussed.
Gel electrocatalysts represent an emerging class of materials for sustainable energy technologies. The advantageous features of gel frameworks based on hierarchically porous architecture, high tenability, and functionalization enable them to potentially break the limitations of current materials. An overview of rational synthesis is provided and advanced applications of gel electrocatalysts are highlighted. Challenges and outlooks for gel electrocatalysts are also discussed.
Research on 2D nanomaterials is rising to an unprecedented height and will continue to remain a very important topic in materials science. In parallel with the discovery of new candidate materials ...and exploration of their unique characteristics, there are intensive interests to rationally control and tune the properties of 2D nanomaterials in a predictable manner. Considerable attention is focused on modifying these materials structurally or engineering them into designed architectures to meet requirements for specific applications. Recent advances in such structural engineering strategies have demonstrated their ability to overcome current material limitations, showing great promise for promoting device performance to a new level in many energy‐related applications. Existing in many forms, these strategies can be categorized based on how they intrinsically or extrinsically alter the pristine structure. Achieved through various synthetic routes and practiced in a range of different material systems, they usually share common descriptors that predestine them to be effective in certain circumstances. Therefore, understanding the underlying mechanism of these strategies to provide fundamental insights into structural design and property tailoring is of critical importance. Here, the most recent development of structural engineering of 2D nanomaterials and their significant effects in energy storage and catalysis technologies are addressed.
Structural engineering serves as an important material design concept and offers new inroads into the field of 2D nanomaterials, including both conventional material engineering methods and novel nanotechnology approaches. Recent progress of structural engineering strategies for 2D nanomaterials and their implications for advanced energy storage and catalysis technologies are presented.
Dual Attention Network for Scene Segmentation Fu, Jun; Liu, Jing; Tian, Haijie ...
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR),
06/2019
Conference Proceeding
In this paper, we address the scene segmentation task by capturing rich contextual dependencies based on the self-attention mechanism. Unlike previous works that capture contexts by multi-scale ...features fusion, we propose a Dual Attention Networks (DANet) to adaptively integrate local features with their global dependencies. Specifically, we append two types of attention modules on top of traditional dilated FCN, which model the semantic interdependencies in spatial and channel dimensions respectively. The position attention module selectively aggregates the features at each position by a weighted sum of the features at all positions. Similar features would be related to each other regardless of their distances. Meanwhile, the channel attention module selectively emphasizes interdependent channel maps by integrating associated features among all channel maps. We sum the outputs of the two attention modules to further improve feature representation which contributes to more precise segmentation results. We achieve new state-of-the-art segmentation performance on three challenging scene segmentation datasets, i.e., Cityscapes, PASCAL Context and COCO Stuff dataset. In particular, a Mean IoU score of 81.5% on Cityscapes test set is achieved without using coarse data.
The objective of this study was to explore microbial community structures, functional profiles, and metabolic pathways in a lab-scale and a full-scale wastewater treatment bioreactors. In order to do ...this, over 12 gigabases of metagenomic sequence data and 600,000 paired-end sequences of bacterial 16S rRNA gene were generated with the Illumina HiSeq 2000 platform, using DNA extracted from activated sludge in the two bioreactors. Three kinds of sequences (16S rRNA gene amplicons, 16S rRNA gene sequences obtained from metagenomic sequencing, and predicted proteins) were used to conduct taxonomic assignments. Specially, relative abundances of ammonia-oxidizing archaea (AOA) and ammonia-oxidizing bacteria (AOB) were analyzed. Compared with quantitative real-time PCR (qPCR), metagenomic sequencing was demonstrated to be a better approach to quantify AOA and AOB in activated sludge samples. It was found that AOB were more abundant than AOA in both reactors. Furthermore, the analysis of the metabolic profiles indicated that the overall patterns of metabolic pathways in the two reactors were quite similar (73.3% of functions shared). However, for some pathways (such as carbohydrate metabolism and membrane transport), the two reactors differed in the number of pathway-specific genes.
Pickering emulsion, a kind of emulsion stabilized only by solid particles locating at oil-water interface, has been discovered a century ago, while being extensively studied in recent decades. ...Substituting solid particles for traditional surfactants, Pickering emulsions are more stable against coalescence and can obtain many useful properties. Besides, they are more biocompatible when solid particles employed are relatively safe
. Pickering emulsions can be applied in a wide range of fields, such as biomedicine, food, fine chemical synthesis, cosmetics, and so on, by properly tuning types and properties of solid emulsifiers. In this article, we give an overview of Pickering emulsions, focusing on some kinds of solid particles commonly serving as emulsifiers, three main types of products from Pickering emulsions, morphology of solid particles and as-prepared materials, as well as applications in different fields.
In this article, we develop a hybrid physics-informed neural network (hybrid PINN) for partial differential equations (PDEs). We borrow the idea from the convolutional neural network (CNN) and finite ...volume methods. Unlike the physics-informed neural network (PINN) and its variations, the method proposed in this article uses an approximation of the differential operator to solve the PDEs instead of automatic differentiation (AD). The approximation is given by a local fitting method, which is the main contribution of this article. As a result, our method has been proved to have a convergent rate. This will also avoid the issue that the neural network gives a bad prediction, which sometimes happened in PINN. To the author's best knowledge, this is the first work that the machine learning PDE's solver has a convergent rate, such as in numerical methods. The numerical experiments verify the correctness and efficiency of our algorithm. We also show that our method can be applied in inverse problems and surface PDEs, although without proof.
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