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Improving thermal transport between substrate and transistors has become a vital solution to the thermal challenge in nanoelectronics. Recently 2D WTe2 has sparked extensive interest because of heavy atomic mass and low Debye temperature. Here, the thermal transport of supported WTe2 was studied via Raman thermometry with electrical heating. The supported 30 nm WTe2 encased with 70 nm Al2O3 delivered 4.8 W·m−1·K−1 in-plane thermal conductivity along zigzag direction at room temperature, which was almost 1.6 times larger than that along armchair direction (3.0 W·m−1·K−1). Interestingly, the superior and inferior directions for thermal transport are just opposite of those for electrical transport. Hence, a heat manipulation model in WTe2 FET device was proposed. Within the designed configuration, waste heat in WTe2 would be mostly dissipated to metal contacts located along zigzag, relieving the local temperature discrepancy in the channel effectively and preventing degradation or breakdown. Our study provides new insight into thermal transport of anisotropic 2D materials, which might inspire energy-efficient nanodevices in the future.

This paper revisits the classical problem of multiple query optimization in federated RDF systems. We propose a heuristic query rewriting-based approach to optimize the evaluation of multiple queries. This approach can take advantage of SPARQL 1.1 to share the common computation of multiple queries while considering the cost of both query evaluation and data shipment. Although we prove that finding the optimal rewriting for multiple queries is NP-complete, we propose a heuristic rewriting algorithm with a bounded approximation ratio. Furthermore, we propose an efficient method to use the interconnection topology between RDF sources to filter out irrelevant sources, and utilize some characteristics of SPARQL 1.1 to optimize multiple joins of intermediate matches. The extensive experimental studies show that the proposed techniques are effective, efficient and scalable.

Thermally responsive shape memory polymers (SMPs) used in 4D printing are often reported to be activated by external heat sources or embedded stiff heaters. However, such heating strategies impede the practical application of 4D printing due to the lack of precise control over heating or the limited ability to accommodate the stretching during shape programming. Herein, we propose a novel 4D printing paradigm by fabricating stretchable heating circuits with fractal motifs via electric-field-driven microscale 3D printing of conductive paste for seamless integration into 3D printed structures with SMP components. By regulating the fractal order and printing/processing parameters, the overall electrical resistance and areal coverage of the circuits can be tuned to produce an efficient and uniform heating performance. Compared with serpentine structures, the resistance of fractal-based circuits remains relatively stable under both uniaxial and biaxial stretching. In practice, steady-state and transient heating modes can be respectively used during the shape programming and actuation phases. We demonstrate that this approach is suitable for 4D printed structures with shape programming by either uniaxial or biaxial stretching. Notably, the biaxial stretchability of fractal-based heating circuits enables the shape change between a planar structure and a 3D one with double curvature. The proposed strategy would offer more freedom in designing 4D printed structures and enable the manipulation of the latter in a controlled and selective manner.

Quantifying the uncertainties in the prediction of landslide displacement is important for making reliable predictions and for managing landslide risk. This study develops a novel approach for the ...

In the case of the rockfall hazards, multiple rockfall impacts usually occur. To improve the ductility and the multi-impact resistance ability of the rock-shed, geofoam is proposed to replace part of sand, forming a composite cushion. Two types of geofoam are selected in the paper, namely, expandable polyethylene (EPE) and expanded polystyrene (EPS). A systematic experimental study was conducted to compare the properties of EPE and EPS. The compression tests showed that EPE geofoam had better resilience than EPS geofoam. Under the laboratory multi-impact tests, the buffer performance of the sand-EPE composite cushion performed well under the multi-impacts. However, for the sand-EPS composite cushion, the buffer performance became poor with increased impact number. For the thicker geofoam, the buffer performance of the composite cushion changed slightly from the second impact to the fifth impact, especially for the EPE geofoam. Finally, large-scale rockfall experiments were carried out to further study the buffer performance of different cushions. Compared with EPS geofoam, EPE geofoam was more suitable for improving the capability of the composite cushion to resist multi-impacts, as well as to protect the rock-shed under multiple rockfall impacts.

In view of the axial force produced in the working process of double arc helical gear hydraulic pump, the theory of differential equation of curve and curved surface was utilized so that the calculation formula of axial force was obtained and the relationship between the axial force and structure parameters of gears was clarified. In order to balance the axial force, the pressure oil in the high pressure area was introduced into the end face of the plunger to press the plunger against the gear shaft, and the hydrostatic bearing whose type is plunger at the end of the shaft was designed. In order to verify the balance effect of axial force, the leakage owing to end clearance and volume efficiency of gear hydraulic pump before and after the balancing process was analyzed. This paper provides a new analysis idea and balance scheme for the axial force produced in the working process of the double arc helical gear hydraulic pump, which can reduce the leakage owing to end clearance caused by the axial force and improve the volume efficiency of the gear hydraulic pump.

This article explores the relationships between image element pairs and label pairs and extends label diffusion to label pair diffusion for the interactive image segmentation task. Compared with label diffusion, more accurate relationships between unlabeled and labeled data can be captured on a tensor product graph (TPG) by using higher order information, and more complex interactions among image elements and finer relationships between image element pairs and label pairs are explored in label pair diffusion (LPD) process. We first establish a prior label estimation framework to measure the label pair prior probability. Then, a probability learning process on TPG is designed to smooth the label prior. The learning process is equivalent to an iterative LPD process on the original graph, which makes the proposed algorithm maintain computational efficiency. Finally, the unary label probabilities can be obtained by a total-probability-theorem-based conversion from the binary relationships. Experiments on popular segmentation data sets demonstrate the superior performance of the proposed method.

In this paper, the liquid-free battery Na-ISE | NaBr (m1), Na2SO4 (m2) |Br-ISE was used to measure the average activity coefficient of the ternary system NaBr–Na2SO4–H2O at 308.15 K by using the ce...

Blind image deconvolution is a fundamental task in image processing, computational imaging, and computer vision. It has earned intensive attention in the past decade since the seminal work of Fergus et al. [1] for camera shake removal. In spite of the recent great progress in this field, this paper aims to formulate the blind problem with a simpler modeling perspective. What is more important, the newly proposed approach is expected to achieve comparable or even better performance towards the real blurred images. Specifically, the core critical idea is the proposal of a pure gradient-based discriminative prior for accurate and robust blur kernel estimation. Numerous experimental results on both the benchmark datasets and real-world blurred images in various imaging scenarios, e.g., natural, manmade, low-illumination, text, or people, demonstrate well the effectiveness and robustness of the proposed approach.

User interactions are generally utilized to impose the hard constraint and estimate the label prior probability in the interactive image segmentation methods. The conventional interactive approaches cannot work well when the user inputs contain incorrect marks. The existing error tolerance methods mainly focus on the interaction itself and try to eliminate the influence of incorrect hard constraint, which however ignore the label prior estimation. This paper mainly focuses on solving the label prior estimation problem when error marks appear. The prior probability is generally defined as the matching degree between pixels and the cluster centers of marked pixels. Incorrect interaction leads to the formation of incorrect clusters. Therefore, a reliability learning model is constructed in this paper by 1) assigning smaller weighting factors to incorrect clusters, 2) assigning larger weighting factors to correct clusters with higher matching degree. Accurate label prior probability can be obtained by the weighted averaging. Then referring to the existing methods, an error-tolerant segmentation model is designed as a ratio energy function, which can both overcome the hard constraint and the label prior limitation with error marks. Experiments on the challenging Berkeley segmentation data set and Microsoft GrabCut database demonstrate the effectiveness of the proposed method.

Shape reconfiguration is ubiquitous in nature and widely used in many applications such as soft robotics, metamaterials, energy absorption and tissue engineering. Shape reconfigurable soft actuators, due to their ability to adapt and adjust in complex and unpredictable working environment, have been designed by the use of various delicate structures and active materials. However, soft actuators that exhibit reconfigurable helical deformation have not been proposed; they have the advantage of integrating both bending and twisting actuations in one deformation mode. In this work, we present a thermal-induced shape reconfigurable soft actuator that shows reversible actuations with vastly shape differences under thermal stimulus. It exhibits helical deformation at lower temperature and mainly in-plane bending at relatively higher temperature. The reversible shape transition is controlled by a thermal stimulus that changes the anisotropy of the structure, which consists of shape memory polymer fibers embedded in a homogeneous elastic matrix. A theoretical model is proposed based on the minimum potential energy that incorporates the thermomechanical behavior of the shape memory polymer fibers. Experiments are conducted and the results agree well with the theoretical modeling. Using the theoretical model, we establish design principles for reconfigurable soft actuators whose functional response is programmable given the architecture and external stimulus. A six-handed helical soft actuator, constructed to demonstrate its programmable deformation, is utilized to catch a living fish in water.

Drainage is a cost-effective way to stabilize slopes. However, clogging has always been a factor that decreases the sustainability and efficiency of the drainage method in slope stabilization. A new drainage method utilizing the hydraulic principle is presented in this paper. A deterministic method, which is performed by integrated software, for evaluating the factor of safety (FOS) under the action of a given self-starting drainage design is proposed correspondingly. A framework for the optimized design of self-starting drainage is established, and the following steps are mainly included: The optimal dip angles of the inclined boreholes are determined by a plotting method; the optimal lengths of the permeable section of the inclined boreholes are determined based on uniform design and support vector machine; the concept of Pareto front is introduced to determine the most preferred design in design space considering multiple objectives including safety, cost, and drain effectiveness. The entire process is illustrated with a case study, the Qili landslide in Zhejiang Province, China.

The design of a compressible battery with stable electrochemical performance is extremely important in compression-tolerant and flexible electronics. While this remains challenging with the current battery manufacturing method, the field of 3D printing offers the possibility of producing free-standing 3D-printed electrodes with various structural configurations. Through the simple and scalable strategy, various structural configurations can be produced. Herein, we demonstrate a 3D-printed quasi-solid-state Ni-Fe battery (QSS-NFB) that shows excellent compressibility, ultrahigh energy density, and superior long-term cycling durability. Through a rational design and adjustment of chemical components, two electrodes consisting of ultrathin Ni(OH)2 nanosheet array cathode and holey α-Fe2O3 nanorod array anode are achieved with a ultrahigh active material loading over 130 mg cm-3 and excellent compressibility up to 60%. It is noteworthy that the compressible QSS-NFB demonstrated an excellent cycling stability (∼91.3% capacity retentions after 10000 cycles) and ultrahigh energy density (28.1 mWh cm-3 at a power of 10.6 mW cm-3). This work provides a simple method for producing compression-tolerant energy-storage devices, which are expected to have promising applications in next generation stretchable/wearable electronics.

Conventional landslide drainage methods always seem to lack a long-term and efficient drainage capacity. Thus, it is necessary to develop a new drainage method that does not have these disadvantage...

Semantic segmentation networks usually utilize special pyramid structure after encoder or combine low-level and high-level feature maps in decoder to capture multi-scale context information, which we term them feature combination and feature connection, respectively. However, both such frameworks are less valid with the fixed geometric structure or unsuitable interim. In this paper, we advocate Dynamic Attention Network (DAN) to solve these problems. First, we design a Deformable Attention Pyramid (DAP) module to perform a self-adjustable descriptor of high-level output, which utilizes deformable function to model geometric transformation. With DAP, semantic information can be captured effectively. Second, we propose a Fusing Attention Interim (FAI) module to guide the back-propagation of long-short range information in each level of decoder. We evaluate DAN on the challenging PASCAL VOC 2012 and Cityscapes segmentation benchmarks and find that it achieves state-of-the-art results without post-processing. Our observation can be concluded that the flexible structure that possesses dynamic attention mechanism is beneficial to learn multi-scale context information.

Reverse cyclic lateral testing was undertaken to investigate the seismic behavior of 1/4 scale steel-plate concrete (SC) composite walls. The experimental program involved seven SC wall pier specimens. A new chamber structure is proposed, using steel diaphragms to connect the two steel faceplates to each other and to divide the SC wall pier into two parts. Conventional wall specimens failed mainly by tensile fracture of the concrete at the junction of the wall side and wall base, crushing of the concrete at the toe of the wall, or buckling of the steel faceplate. Tearing of the welded joints at the steel faceplates and steel diaphragm, buckling of steel, steel diaphragms being pulled out, tensile fracture and crushing of the concrete were the main failure modes of the chamber structure walls. A parametric numerical analysis in ABAQUS was developed to investigate the effects of the stiffening rib, steel web amount, material strength, shear-span ratio, and axial compression ratio on the seismic response of SC walls. The chamber structure of the SC wall piers can improve the peak load, ductility, and energy-dissipating capacity. The steel faceplate thickness and stiffening ribs can improve the behavior of SC wall piers.

Electrocardiography is a useful diagnostic tool for various cardiovascular diseases, such as myocardial infarction (MI). An electrocardiograph (ECG) records the electrical activity of the heart, which can reflect any abnormal activity. MI recognition by visual examination of an ECG requires an expert’s interpretation and is difficult because of the short duration and small amplitude of the changes in ECG signals associated with MI. Therefore, we propose a new method for the automatic detection of MI using ECG signals. In this study, we used maximal overlap discrete wavelet transform to decompose the data, extracted the variance, inter-quartile range, Pearson correlation coefficient, Hoeffding’s D correlation coefficient and Shannon entropy of the wavelet coefficients and used the k-nearest neighbor model to detect MI. The accuracy, sensitivity and specificity of the model were 99.57%, 99.82% and 98.79%, respectively. Therefore, the system can be used in clinics to help diagnose MI.

Super-resolution of facial images, a.k.a. face hallucination, has been intensively studied in the past decades due to the increasingly emerging analysis demands in video surveillance, e.g., face detection, verification, identification. However, the actual performance of most previous hallucination approaches will drop dramatically when a very low-res tiny face is provided, due to the challenging multimodality of the problem as well as lack of an informative prior as a strong semantic guidance. Inspired by the latest progress in deep unsupervised learning, this paper focuses on tiny faces of size 16 × 16 pixels, hallucinating them to their 8 × upsampling versions by exploring the potentials of Wasserstein generative adversarial networks (WGAN). Besides a pixel-wise L2 regularization term imposed to the generative model, it is found that our advocated autoencoding generator with both residual and skip connections is a critical component for WGAN representing the facial contour and semantic content to a reasonable precision. With the additional Lipschitz penalty and architectural considerations for the critic in WGAN, the proposed approach finally achieves state-of-the-art hallucination performance in terms of both visual perception and objective assessment. The cropped CelebA face dataset is primarily used to aid the tuning and analysis of the new method, termed as tfh-WGAN. Experimental results demonstrate that the proposed approach not only achieves realistic hallucination of tiny faces, but also adapts to pose, expression, illuminance and occluded variations to a great degree.

Four-dimensional (4D) printing, which enables 3D printed structures to alter shapes over time, is attracting increasing attention because of its exciting potential in various applications. Among all the 4D printing materials, shape memory polymers (SMPs) have a higher stiffness and faster response rate and therefore are considered as one of the most promising 4D printing materials. However, the current studies of SMP-based 4D printing mainly focused on the deformation behavior and structural design of 4D printed structures. An additional function such as color change is desired for 4D printed structure, which would be potentially beneficial to the applications such as anti-counterfeiting, encryption, and bioinspired camouflage. In this paper, we report an ultraviolet (UV)-curable and thermochromic (UVT) SMP system that enables color-changeable 4D printing. The UVT SMP system is acrylate-based, thus highly UV-curable and compatible with PμSL-based high-resolution 3D printing technique. Thermochromism is imparted by adding the thermochromic microcapsules to the UVT SMP system, which allows the printed structures to reversibly change colors upon heating and cooling. To demonstrate its extraordinary thermochromic and mechanical performance, we use UVT SMP to print QR codes and multilevel anti-counterfeiting patterns which can hide the visible information at room temperature and visualize the information by encrypting, decrypting, and encrypting again steps with the shape-color recovery process. The development of UVT SMP will significantly advance current applications of SMP-based 4D printing, especially for anti-counterfeiting and safe data recording.

With the rapid progress of urbanization, more and more structure clusters are being built in major cities and small towns, where the spacing of buildings is becoming smaller and smaller. The cluster effect among buildings in the system of soil and structure cluster interaction (SSCI) emerges during an earthquake. The concept of cluster effect of SSCI is presented in this paper. This work investigates and fully presents the cluster interaction in the context of SSCI using a shaking table test based on the finite element method (FEM). Several cases are studied: a single building and a structure cluster, with various loading conditions, such as the El Centro ground record and Shanghai artificial wave. The Davidenkov foundation model is used as the constitutive soil model, and this model describes the dynamic nonlinearity of soil and is conducted using an ANSYS parameter design language and a restarting command. The numerical simulations of the shaking table test are in good agreement with the experimental records. Extensive analyses on parametric analyses of SSCI are conducted to determine the influence of some parameters of SSCI, including structure spacing, soil property and structure number. Considering the cluster effect under the similar conditions with the experiment, the laws and the influences of soil and structure cluster interaction are fully summarized as follows: the natural frequency of the system of SSCI is less than that of soil and structure interaction (SSI). Under earthquake excitation, the structures in different locations have different responses, and this is the cluster effect of SSCI. Cluster interaction reduces the acceleration responses of structures, while velocity may be larger or less than that of the single building (SSI), and this is related with the earthquake input and the structure location. The displacement of the central building reduces, but these of the surrounding structures along the vibration increase, and the buildings farthest from the center have the largest displacements. The maximum absolute value of the influence coefficients of the cluster effect for displacement reaches 770.94%. The building number is the most significant factor that affects the cluster effect in SSCI system. The cluster interaction constantly reduce when the structure spacing increase and disappears when the clear spacing is larger than 1.32 times as the size of the building foundation, and the cluster effect is weak. Last, the mechanism of SSCI is studied by the laws of the displacement.

Rhenium disulfide (ReS2) has recently amassed increasing interests as lithium-ion batteries (LIBs) anode material due to its extremely weak interlayer coupling and high Li storage capacity. However, its potential as high-performance sodium-ion batteries (SIBs) has rarely been explored comprehensively. In this work, we demonstrate a novel construct of ReS2 anode consisting of vertical ReS2 nanosheets uniformly grown on metal-organic framework (MOF) derived porous carbonaceous nanocubes (RESNC), is able to achieve high-performance SIBs. Exhibiting high specific surface area (117.40 m2 g−1) and near-microporous (∼2.20 nm) pore size distribution, this unique and conductive nanostructure provides abundant reactive sites which enable rapid and efficient ions/electrons transportation. For the first time, the sodiation/desodiation redox mechanisms of the ReS2-based nanostructure are revealed via ex situ investigations. Our results demonstrated that RESCN nanocomposite is endued with high capacity (365 mA h g−1, 100 mA g−1), high rate capability (up to 2 A g−1) and stable cycling performance (>600 cycles, 2 A g−1). To elaborate on the rate and cycling capabilities, various diffusion pathways of sodium-ions on the ReS2 lattice were unravelled via first-principles/ab initio techniques, with the results indicating favourable diffusion mechanics. Through the joint approach of comprehensive experimental and theoretical investigations, our results suggest that the ReS2/C composites are highly promising as high-performance SIBs anode materials, as well as broadening the current perspectives of materials choices as far as high capacity transition metal disulfides (TMDs) is concerned.

Shape memory alloys (SMAs) have been widely used to fabricate soft actuators by embedding SMA wires into various soft matrices manufactured by conventional moulding methods or novel three-dimensional (3D) printing techniques. However, soft matrices of SMA based actuators are typically fabricated from only one or two different materials. Here, we exploit the great manufacturing flexibility of multimaterial 3D printing to fabricate various bending, twisting and extensional actuators by precisely controlling the spatial arrangements of different printing materials with different stiffnesses. In order to achieve a broad range of deformations, ten different printing materials were characterized and used in the actuators design. In addition, we developed a finite element model to simulate complex deformations of the printed actuators and facilitate the design process. The model incorporates a user defined material subroutine that describes the nonlinear temperature dependant behavior of SMAs. The results show the efficiency and flexibility of multimaterial 3D printing in tailoring the deformed shape of the SMA based soft actuators, which cannot be accomplished using conventional manufacturing methods such as moulding.

The recently developed dual-stage 3D printing reprocessable thermosets (3DPRTs) utilize acrylate functional groups to enable the ultra-violet based 3D printing (Stage I), and employ bond exchange reaction (BERs) to tailor the network structure for mechanical properties enhancement and impart the reshapeability, reparability, and recyclability into traditionally unprocessable thermosets (Stage II). 3DPRT provides a practical solution to address environmental challenges associated with the rapid increase in the consumption of 3D printing materials. However, due to the nascent state of development, fundamental understanding of the chemomechanics during the processing of 3DPRTs is still lacking. In this paper, we present detailed experimental and theoretical studies to understand the effect of thermal treatment condition at Stage II on the evolution of network structure and thermomechanical properties of 3DPRTs. A chemomechanics theory is defined to link the molecular-level BER kinetics to the macroscale thermomechanical properties of 3DPRTs during the thermal treatment. A thermo-viscoelastic multi-branched constitutive model is then established to capture the elastic and glass transition behaviors during the Stage II processing. The developed theory is able to capture the experimental observations on the mechanical properties enhancement and provide theoretical guidance for the network design and the selection of thermal treatment conditions to tailor the final mechanical properties of 3DPRTs.

Conventional 4D printing technologies are realized by combining 3D printing with soft active materials such as shape memory polymers (SMPs) and hydrogels. However, the intrinsic material property limitations make the SMP or hydrogel-based 4D printing unsuitable to fabricate the actuators that need to exhibit fast-response, reversible actuations. Instead, pneumatic actuations have been widely adopted by the soft robotics community to achieve fast-response, reversible actuations, and many efforts have been made to apply the pneumatic actuation to 3D printed structures to realize passive 4D printing with fast-response, reversible actuation. However, the 3D printing of soft actuators/robots heavily relies on the commercially available UV curable elastomers the break strains of which are not sufficient for certain applications which require larger elastic deformation. In this paper, we present two simple approaches to tune the mechanical properties such as stretchability, stiffness, and durability of the commercially available UV curable elastomers by adding: (i) mono-acrylate based linear chain builder; (ii) urethane diacrylate-based crosslinker. Material property characterizations have been performed to investigate the effects of adding the two additives on the stretchability, stiffness, mechanical repeatability as well as viscosity. Demonstrations of fully printed robotic finger, grippers, and highly deformable 3D lattice structure are also presented.

Single-image nonparametric blind super-resolution is a fundamental image restoration problem yet largely ignored in the past decades among the computational photography and computer vision communities. An interesting phenomenon is observed that learning-based single-image super-resolution (SR) has been experiencing a rapid development since the boom of the sparse representation in 2005s and especially the representation learning in 2010s, wherein the high-res image is generally blurred by a supposed bicubic or Gaussian blur kernel. However, the parametric assumption on the form of blur kernels does not hold in most practical applications because in real low-res imaging a high-res image can undergo complex blur processes, e.g., Gaussian-shaped kernels of varying sizes, ellipse-shaped kernels of varying orientations, curvilinear kernels of varying trajectories. The paper is mainly motivated by one of our previous works: Shao and Elad (in: Zhang (ed) ICIG 2015, Part III, Lecture notes in computer science, Springer, Cham, 2015). Specifically, we take one step further in this paper and present a type of adaptive heavy-tailed image priors, which result in a new regularized formulation for nonparametric blind super-resolution. The new image priors can be expressed and understood as a generalized integration of the normalized sparsity measure and relative total variation. Although it seems that the proposed priors are simple, the core merit of the priors is their practical capability for the challenging task of nonparametric blur kernel estimation for both super-resolution and deblurring. Harnessing the priors, a higher-quality intermediate high-res image becomes possible and therefore more accurate blur kernel estimation can be accomplished. A great many experiments are performed on both synthetic and real-world blurred low-res images, demonstrating the comparative or even superior performance of the proposed algorithm convincingly. Meanwhile, the proposed priors are demonstrated quite applicable to blind image deblurring which is a degenerated problem of nonparametric blind SR.

Vitrimers are a new class of thermosetting polymers that can be thermally processed through bond exchange reactions (BERs) without losing network integrity. In engineering applications, the tunability of their thermomechanical properties is highly desirable to meet the requirements of different working conditions. Here, we report a simple composite-based strategy that avoids complex chemistry to prepare vitrimer blends with tunable thermomechanical properties by virtue of the good weldability of base vitrimers. Effects of processing parameters (such as temperature and time) on the properties of recycled vitrimer blends are experimentally investigated. A computational model that accounts for the random distribution of component vitrimer particles is developed to predict the thermomechanical properties of the recycled vitrimer blends with various compositions. Good agreement is achieved between theoretical prediction and experiment. Parametric studies are further conducted by employing the computational model to explore the designability and provide some basic principles to guide the design of recycled vitrimer blends. Reasonable recyclability of the vitrimer blends is verified by multiple generations of recycling experiments.

As a promising energy storage system, lithium–sulfur (Li–S) batteries are attracting increasing attention but still limited by the sluggish reaction kinetics and shuttle effect caused by the dissolution of lithium polysulfides. Herein, a significant improvement of conversion kinetics and areal sulfur loading is achieved using an ordered microchannel graphene scaffold with incorporated catalytic Fe3O4 nanocrystals and porous carbon as a multifunctional sulfur host. The synergy between the polar catalytic Fe3O4 nanocrystals and porous carbon frameworks enables a strong polysulfide anchoring effect and a fast polysulfide conversion rate. Thus, the 3D ternary Fe3O4/porous carbon/graphene aerogel demonstrates an ultrahigh rate performance of 755 mA h g−1 at 3C and a high areal capacity of 6.24 mA h cm−2 at a sulfur loading of 7.7 mg cm−2. Moreover, the promoted reaction kinetics and reliable cyclability are revealed by the visible evolution of polysulfides using in situ X-ray diffraction (XRD), and the enhanced chemical anchoring of polysulfides is disclosed by density functional theory (DFT) calculations. This work provides a promising approach to develop multifunctional ordered porous aerogels with metal oxide nanocrystals for high-performance Li–S batteries, especially those which suffer from low sulfur loading and inferior rate performance.

Graphene aerogels are emerging low density and superelasticity macroscopic porous materials with various applications. However, it still remains a challenge to develop a versatile strategy under ambient conditions for fabricating large-area, high-performance graphene aerogels, which is crucial for their practical applications. Here, we report a novel room-temperature reduction self-assembly (RTRS) strategy to fabricate large-area graphene aerogels under ambient conditions. The strategy is based on using unique hydrazine hydrates as reducing agents to generate stable microbubbles beneficial for the formation of macroporous graphene hydrogels. Interestingly, the resultant hydrogel followed by a simple pre-freeze treatment can be naturally dried into graphene aerogels without noticeable volume shrinkage or structure cracking. Benefiting from the mild conditions, a large-area graphene aerogel with a diameter of up to 27 cm was prepared as an example. The as-formed aerogels exhibit a stable honeycomb-like coarse-pores structure, a low density of 3.6 mg cm-3 and superelasticity (rapidly recoverable from 95% compression) which are suitable for pressure/strain sensors. Moreover, the aerogel exhibits superior particulate matter adsorption efficiency (PM2.5: 93.7%, PM10: 96.2%) and good recycling ability. Importantly, the preparation process is cost-effective and easily scalable without the need for any special drying techniques and heating processes, which provides an ideal platform for mass production of graphene aerogels toward practical applications.

Achievement of optimized lateral and vertical resolution is a key factor to obtaining three-dimensional (3D) structural details fabricated through digital light processing (DLP)-based 3D printing technologies which exploit digitalized ultraviolet (UV) or near-UV light to trigger localized photopolymerization forming solid patterns from liquid polymer resins. Many efforts have been made to optimize printing resolution through improving the optical systems. However, researchers have paid comparatively little attention to understand the influences of polymer formulation on the printing resolution and surface quality. Here, we report an investigation on the effects of in-house formulated (meth)acrylate-based photopolymer constituent types and concentrations on the resolution and quality of structures printed on a bottom-exposure DLP-based 3D printing system. We examined a wide variety of resin formulations to determine optimal formulations that yield best printing resolution and surface quality over a reasonably broad range of mechanical properties. We demonstrated the controlled fabrication of sub-pixel conical and aspherical smooth features, whereby the shape and dimensions could be prescribed with the resin formulation and process parameters. Such features hold promising implications in micro-optic and microfluidic fabrication using the DLP-based 3D printing technique. Additionally, we devised di(meth)acrylate-based resin formulations which could exclusively produce optically-clear layers in contrast to opaque, rough surfaces resulting from commonly-used diacrylate-based resins including those available commercially. Use of this solution minimized the ‘stair-stepping’ effect in components printed in a layer-by-layer manner. We also showed that the maximum lateral and vertical resolution attainable using our present system were 7 μm and 4 μm, respectively, while maintaining uniform width and height. Taken together, the present findings provide a basis for optimized photopolymer resin formulations that retain maximum vertical and lateral resolutions and minimal surface roughness and layering artifacts for a versatile range of mechanical and rheological properties suited to novel applications in 3D printing of smooth free-form solids, micro-optics, and direct fabrication of microfluidic platforms with functional surfaces.

Novel glass-ceramic electrolytes of (100-x) (70Li2S-30P2S5)-xNi2P (x = 0, 1, 2, 3, 4) with high ionic conductivity were proposed with the addition of Ni2P. The 2 mol% Ni2P containing electrolyte (Li7Ni0.2P3.1S11) displays the highest lithium-ion conductivity of 2.22 mS cm−1 at room temperature, which is 1.6 times higher than pristine Li7P3S11. By direct combination with Li2S, the fabricated Li7Ni0.2P3.1S11-Li2S all-solid-state battery exhibits an initial discharge capacity of 429 mAh g−1 at 0.064 mA cm−2 and 454 mAh g−1 at 20th charging/discharging cycle with a high Coulombic efficiency of 99.7%. Keywords: Ni2P doped Li7P3S11, Li2S utilization, Lithium-ion conductivity, All-solid-state lithium‑sulfur batteries

Many interactive image segmentation methods design energy functions based on the local relationship of neighboring pixels, which is insufficient at capturing the abundant information of an image, and these methods are susceptible to many problems, such as sensitivity to seeds and under-segmentation of boundaries. To solve these problems, this paper explores utilizing the global relationship for interactive image segmentation. To effectively obtain the global information of the image, we first propose a robust affinity diffusion (RAD) method to propagate the local affinity graph. Compared with the existing diffusion-based approaches, the advantage of RAD is that it can converge to an effective limit value, which makes the diffusion process more computationally efficient and easier to control. The segmentation model is then constructed based on this convergent global graph. To efficiently utilize the global information, the energy function is designed by the multiplication of the global affinity matrix and a prior probability vector. The use of global information can significantly improve the segmentation performance. Experiments on challenging data sets demonstrate that RAD can obtain better results than state-of-the-art methods.

Stretchable and flexible electronics conformal to human skin or implanted into biological tissues has attracted considerable interest for emerging applications in health monitoring and medical treatment. Although various stretchable materials and structures have been designed and manufactured, most are limited to two-dimensional (2D) layouts for interconnects and active components. Here, by using projection microstereolithography (PμSL)-based three-dimensional (3D) printing, we introduce a versatile microfabrication process to push the manufacturing limit and achieve previously inaccessible 3D geometries at a high resolution of 2 μm. After coating the printed microstructures with thin Au films, the 3D conductive structures offer exceptional stretchability (∼130%), conformability, and stable electrical conductivity (5% resistance change at 100% tensile strain). This fabrication process can be further applied to directly create complicated 3D interconnect networks of sophisticated active components, as demonstrated with a stretchable capacitive pressure sensor array here. The proposed scheme allows a simple, facile, and scalable manufacturing route for complex, integrated 3D flexible electronic systems.

Soft composite actuators can be fabricated by embedding shape memory alloy (SMA) wires into soft polymer matrices. Shape retention and recovery of these actuators are typically achieved by incorporating shape memory polymer segments into the actuator structure. However, this requires complex manufacturing processes. This work uses multimaterial 3D printing to fabricate composite actuators with variable stiffness capable of shape retention and recovery. The hinges of the bending actuators presented here are printed from a soft elastomeric layer as well as a rigid shape memory polymer (SMP) layer. The SMA wires are embedded eccentrically over the entire length of the printed structure to provide the actuation bending force, while the resistive wires are embedded into the SMP layer of the hinges to change the temperature and the bending stiffness of the actuator hinges via Joule heating. The temperature of the embedded SMA wire and the printed SMP segments is changed sequentially to accomplish a large bending deformation, retention of the deformed shape, and recovery of the original shape, without applying any external mechanical force. The SMP layer thickness was varied to investigate its effect on shape retention and recovery. A nonlinear finite element model was used to predict the deformation of the actuators.

Vitrimers with bond exchange reactions (BERs) are a class of covalent adaptable network (CAN) polymers at the forefront of recent polymer research. They exhibit malleable and self-healable behaviors and combine the advantages of easy processability of thermoplastics and excellent mechanical properties of thermosets. For thermally sensitive vitrimers, a molecular topology melting/frozen transition is triggered when the BERs are activated to rearrange the network architecture. Notable volume expansion and stress relaxation are accompanied, which can be used to identify the BER activation temperature and rate as well as to determine the malleability and interfacial welding kinetics of vitrimers. Existing works on vitrimers reveal the rate-dependent behaviors of the nonequilibrium network during the topology transition. However, it remains unclear what the quantitative relationship with heating rate is, and how it will affect the macroscopic stress relaxation. In this paper, we study the responses of an epoxy-based vitrimer subjected to a change in temperature and mechanical loading during the topology transition. Using thermal expansion tests, the thermal strain evolution is shown to depend on the temperature-changing rate, which reveals the nonequilibrium states with rate-dependent structural relaxation. The influences of structural relaxation on the stress relaxation behaviors are examined in both uniaxial tension and compression modes. Assisted by a theoretical model, the study reveals how to tune the material and thermal-temporal conditions to promote the contribution of BERs during the reprocessing of vitrimers.

Shape-shifting structures have gained growing interest recently and found wide applications in areas such as soft robotics, biomedical devices and self-folding origami, attributed to their ability to construct complicated shapes directly from simple structures. However, an efficient method to design and fabricate programmable 3D shape-shifting structures from 2D polymer films still lacks. In this work, we design programmable shape-shifting 3D structures via the release of internal gradient stress using the frontal photopolymerization (FPP) method. First, the relation between the non-uniformly distributed material and loading parameters, and the geometric and fabrication parameters are established theoretically. The finite element (FE) model is then developed based on the theoretically obtained material and loading parameters. Next, the elastic instability in the shape-shifting behaviors of a cured film is captured through an elastic energy minimization. Furthermore, by using grayscale light patterns, it is shown that we can selectively manipulate the geometric and fabrication parameters to improve the design freedom of various complex 3D structures.

Spatial control of material properties is highly desirable in additive manufacturing of functional structures with complex geometries. Whereas most previous efforts focused on developing new printing or material systems, we propose a new voxel design strategy of constructing digital materials to provide the additively manufactured polymeric structures with spatially customized thermomechanical properties. In our approach, a matrix-inclusion composite layout is adopted in the linearly patterned voxels that perform as building blocks to construct bulk material. Through rational design of voxel size and inclusion content, the printed polymeric digital material displays a tunable storage modulus up to three orders of magnitude and glass transition temperature ranging from 0 °C to 60 °C. By taking advantage of the design freedom, we demonstrate a sequential folding structure with spatially tunable actuation speed, and multi-stable configurations that trap elastic energy in deterministic collapse sequences. Overall, our approach provides an effective and convenient way of spatially customizing material properties for additive manufacturing and offers instructive inspirations to the realm of digital fabrication.

Some cancers such as human breast cancer, prostate cancer, and lung cancer easily metastasize to bone, leading to osteolysis and bone destruction accompanied by a complicated microenvironment. Systemic administration of bisphosphonates (BP) or denosumab is the routine therapy for osteolysis but with non-negligible side effects such as mandibular osteonecrosis and hypocalcemia. Thus, it is imperative to exploit optimized drug delivery systems, and some novel nanotechnology and nanomaterials have opened new horizons for scientists. Targeted and local drug delivery systems can optimize biodistribution depending on nanoparticles (NPs) or microspheres (MS) and implantable biomaterials with the controllable property. Drug delivery kinetics can be optimized by smart and sustained/local drug delivery systems for responsive delivery and sustained delivery. These delicately fabricated drug delivery systems with special matrix, structure, morphology, and modification can minimize unexpected toxicity caused by systemic delivery and achieve desired effects through integrating multiple drugs or multiple functions. This review summarized recent studies about optimized drug delivery systems for the treatment of cancer metastatic osteolysis, aimed at giving some inspiration in designing efficient multifunctional drug delivery systems.

In this work, we propose a new method termed Semantic Frustum Based VoxelNet (SFB-VoxelNet) for 3D object detection. Since previous frustum-based methods are greatly affected by the accuracy of 2D image proposal, we directly alleviate this influence by reducing the probability of omissions and reinforcing the discriminating ability of the 3D detector. Specifically, first, we use semantic map, generating by the method of semantic segmentation, to keep up semantic information and stuff of interest. Second, by projecting the semantic map to LIDAR space, the frustum is generated and then sent to our detector to estimate 3D object classification and bounding box location. Our method not only cuts down most of search space but also assigns a reasonable arrangement for the two steps of frustum-based methods. In addition, our method is evaluated on KITTI 3D object detection benchmark to verify its superiority while maintaining a real-time inference speed, comparing to other state-of-the-art literatures.

Extraordinary optical, electrical and chemical properties of 2D materials have potential to be useful for quick and sensitive detection of pathological diseases. One important example is malaria disease that can progress rapidly and cause death within days. Therefore, fast, accurate and cost-effective malaria diagnosis available at the point of care is urgently needed to facilitate precise treatment. Here we report rapid and highly sensitive malaria detection with an inexpensive graphene-protected copper surface plasmon resonance biosensor. Using phase sensitive surface plasmon resonance technique and a graphene functionalization protocol for attaching end-tethered DNA probes that were complementary to a malaria specific DNA target, we were able to significantly improve the detection limit of the malarial plasmodium parasite. The phase sensitivity of our graphene-enhanced sensors exceeds by two orders of magnitude the sensitivity of analogous optical biosensors. This enhanced sensitivity could provide means to detect low copy number bacterial infectious agents and to associate dormant bacterial populations with chronic inflammatory diseases using simple label-free optical detection.

Four-dimensional (4D) printing of shape memory polymer (SMP) imparts time responsive properties to 3D structures. Here, we explore 4D printing of a SMP in the submicron length scale, extending its applications to nanophononics. We report a new SMP photoresist based on Vero Clear achieving print features at a resolution of ~300 nm half pitch using two-photon polymerization lithography (TPL). Prints consisting of grids with size-tunable multi-colours enabled the study of shape memory effects to achieve large visual shifts through nanoscale structure deformation. As the nanostructures are flattened, the colours and printed information become invisible. Remarkably, the shape memory effect recovers the original surface morphology of the nanostructures along with its structural colour within seconds of heating above its glass transition temperature. The high-resolution printing and excellent reversibility in both microtopography and optical properties promises a platform for temperature-sensitive labels, information hiding for anti-counterfeiting, and tunable photonic devices., Four-dimensional (4D) printing of shape memory polymer (SMP) imparts time responsive properties to 3D structures. Here, the authors explore 4D printing of a SMP in the submicron length scale, extending its applications to nanophononics.

By 3D printing lattice structure with active materials, the structures can exhibit shape and functional changes under external stimulus. However, the programmable shape changes of the 3D printed lattice structures are limited due to the complex geometries, nonlinear behaviors of the active materials, and the diverse external stimuli. In this work, we propose a design framework combining experiments, theoretical modeling, and finite element simulations for the controllable shape changes of the 3D printed horseshoe under thermal stimulus. The theoretical model is based on a phase evolution model that combines the geometrical nonlinearity and the material nonlinearity. Results show that the shapes with positive or negative Poisson's ratio and bending intermediate shapes can be programmed by tuning the geometrical parameters and the temperature distribution. This work provides a method to aid the design of 3D printed functional lattice structures and have potential applications in soft robotics, biomedicine, and energy absorbing fields.

We report a method to prepare highly stretchable and UV curable hydrogels for high resolution DLP based 3D printing. Hydrogel solutions were prepared by mixing self-developed high-efficiency water-soluble TPO nanoparticles as the photoinitiator with an acrylamide-PEGDA (AP) based hydrogel precursor. The TPO nanoparticles make AP hydrogels UV curable, and thus compatible with the DLP based 3D printing technology for the fabrication of complex hydrogel 3D structures with high-resolution and high-fidelity (up to 7 μm). The AP hydrogel system ensures high stretchability, and the printed hydrogel sample can be stretched by more than 1300%, which is the most stretchable 3D printed hydrogel. The printed stretchable hydrogels show an excellent biocompatibility, which allows us to directly 3D print biostructures and tissues. The great optical clarity of the AP hydrogels offers the possibility of 3D printing contact lenses. More importantly, the AP hydrogels are capable of forming strong interfacial bonding with commercial 3D printing elastomers, which allows us to directly 3D print hydrogel–elastomer hybrid structures such as a flexible electronic board with a conductive hydrogel circuit printed on an elastomer matrix.

Normally-off Gallium Nitride (GaN) transistors with p-GaN gated technology for power applications are studied for the optimization of off-state breakdown performance. The gate-drain distance, gate length, field plate length and its configuration have been studied. Increase gate-drain distance will not only enhance the breakdown performance but also increase the $\mathrm{R}_{\mathrm{on}},\ \mathrm{L}_{\mathrm{GD}}$ between $10 -20\ \mu\mathrm{m}$ is recommended. Extra gate length will lead to the rising of the gate leakage current under high voltage. The overhang length of the field plate is a critical factor for breakdown performance, over placing the length of overhang may result in an additional current leakage path.

The energy dissipation issue has become one of the greatest challenges of the modern electronic industry. Incorporating graphene into the electronic devices has been widely accepted as a promising approach to solve this issue, due to its superior carrier mobility and thermal conductivity. Here, using Raman spectroscopy and infrared thermal microscopy, we identify the energy dissipation behavior of graphene device with different thicknesses. Surprisingly, the monolayer graphene device is demonstrated to have a comparable energy dissipation efficiency per unit volume with that of a few-layer graphene device. This has overturned the traditional understanding that the energy dissipation efficiency will reduce with the decrease of functional materials dimensions. Additionally, the energy dissipation speed of the monolayer graphene device is very fast, promising for devices with high operating frequency. Our finding provides a new insight into the energy dissipation issue of two-dimensional materials devices, which will have a global effect on the development of the electronic industry.

Digital Light Processing (DLP) 3D printing enables the creation of hierarchical complex structures with specific micro- and macroscopic architectures that are impossible to achieve through traditional manufacturing methods. Here, this hierarchy is extended to the mesoscopic length scale for optimized devices that dissipate mechanical energy. A photocurable, thus DLP-printable main-chain liquid crystal elastomer (LCE) resin is reported and used to print a variety of complex, high-resolution energy-dissipative devices. Using compressive mechanical testing, the stress-strain responses of 3D-printed LCE lattice structures are shown to have 12 times greater rate-dependence and up to 27 times greater strain-energy dissipation compared to those printed from a commercially available photocurable elastomer resin. The reported behaviors of these structures provide further insight into the much-overlooked energy-dissipation properties of LCEs and can inspire the development of high-energy-absorbing device applications.

Soft biomaterials have a wide range of applications in many areas. However, one material can only cover a specific range of mechanical performance such as the elastic modulus and stretchability. In order to improve the mechanical performance of soft biomaterials, lattice structures are embedded to reinforce the biomaterials. In this paper, rectangular and triangular lattice structures formed by modified horseshoe microstructures are used because their mechanical properties are tunable and can be tailored precisely to match the desired properties by adjusting four geometrical parameters, the length L, radius R, width w and arc angle θ0. A theoretical design framework for the modified horseshoe lattice structures is developed to predict the dependence of the mechanical behaviors on geometrical parameters. Both experiments and finite element simulations on lattice structures are conducted to validate the theoretical models. Results show that a wide range of design space for the elastic modulus (a few kPa to hundreds of MPa), stretchability (strain up to 180%) and Poisson ratio (ranging from -0.5 to 1.2) can be achieved. Experiments on lattice-hydrogel composites are also conducted to verify the reinforcement effect of lattice structures on the hydrogel. This work provides a theoretical method to predict the mechanical behaviors of the lattice structures and aid the rational design of reinforced biomaterials, which has applications in tissue engineering, drug delivery and intraocular lenses.

Vitrimers have the characteristics of shape-reforming and surface-welding, and have the same excellent mechanical properties as thermosets; so vitrimers hold the promise of a broad alternative to traditional plastics. Since their initial introduction in 2011, vitrimers have been applied to many unique applications such as reworkable composites and liquid crystal elastomer actuators. A series of experiments have investigated the effects of reprocessing conditions (such as temperature, time, and pressure) on recycled materials. However, the effect of particle size on the mechanical properties of recycled materials has not been reported. In this paper, we conducted an experimental study on the recovery of epoxy-acid vitrimers of different particle sizes. Epoxy-acid vitrimer powders with different particle size distributions were prepared and characterized. The effects of particle size on the mechanical properties of regenerated epoxy-acid vitrimers were investigated by dynamic mechanical analysis and uniaxial tensile tests. In addition, other processing parameters such as temperature, time, and pressure are discussed, as well as their interaction with particle size. This study helped to refine the vitrimer reprocessing condition parameter toolbox, providing experimental support for the easy and reliable control of the kinetics of the bond exchange reaction.

The performance of conventional interactive image segmentation methods is strongly affected by seed quantity and position, and it is difficult for them to maintain global data coherence due to the bias that is caused by limited interactions. Furthermore, the pixel-level relationships in these methods are too local to capture long-range connectivity cues, which often causes them to obtain under-segmented results. To solve these problems, this paper proposes an interactive segmentation method that is based on likelihood diffusion and perceptual learning. The diffusive likelihood strategy is proposed for accurately estimating the prior label probability from limited user inputs. Superpixel-level grouping cues are utilized to enforce continuity during the segmentation process. The geometrical adjacency and long-range grouping cues are fused in the proposed framework to ensure that the segmentation results maintain proximity and continuity. The final results can be obtained by applying a joint optimization technique to solve a pair of sub-module functions. Experiments on the Berkeley segmentation data set and the Microsoft GrabCut database demonstrate that the proposed method outperforms state-of-the-art methods.

A microlens array has become an important micro-optics device in various applications. Compared with traditional manufacturing approaches, digital light processing (DLP)-based printing enables fabrication of complex three-dimensional (3D) geometries and is a possible manufacturing approach for microlens arrays. However, the nature of 3D printing objects by stacking successive 2D patterns formed by discrete pixels leads to coarse surface roughness and makes DLP-based printing unsuccessful in fabricating optical components. Here, we report an oscillation-assisted DLP-based printing approach for fabrication of microlens arrays. An optically smooth surface (about 1 nm surface roughness) is achieved by mechanical oscillation that eliminates the jagged surface formed by discrete pixels, and a 1-3 s single grayscale ultraviolet (UV) exposure that removes the staircase effect. Moreover, computationally designed grayscale UV patterns allow us to fabricate microlenses with various profiles. The proposed approach paves a way to 3D print optical components with high quality, fast speed, and vast flexibility.

The seismic problem of concrete dams has long been a difficult issue facing academic and engineering researchers. Traditional anti-seismic and isolation measures produce unfavorable results in hydraulic structures. However, the air-cushion seismic isolation technique represents a new development orientation for the anti-seismic method of concrete dams. To study the isolation and anti-cracking effects of the air-cushion, the gas-liquid-solid tri-phase coupling numerical model of the air-cushion isolation control of high arch dams is presented in this paper, in which the cracking behavior of concrete is considered. A 300 m level dam was simulated numerically under three different seismic intensities. The results show that the air-cushion reduces the hydrodynamic pressure significantly. The maximum hydrodynamic pressure is reduced by more than 70 %, and the acceleration of the dam crest is reduced by more than 50 % with a 1 m air-cushion. The reduction in hydrodynamic pressure and dam acceleration increases with increasing seismic intensity. In addition, the air-cushion decreases the cracking range of the dam body effectively. Thus, the isolation effects of the air-cushion are remarkable.