•A holistic review of current research on the mechanical properties of AAC is provided.•The AACs reviewed include slag-based, fly ash-based, and fly ash/slag-based types.•Both static and dynamic ...mechanical properties of AAC are discussed.•The fracture, bond and high-temperature properties of AAC are also addressed.•The slag/fly ash ratio is a very influential factor to the mechanical properties of AAC.
Alkali-activated concretes (AACs) are attracting increasing attention due to their potential as alternatives to ordinary Portland cement concrete (OPCC). This paper is a holistic review of current research on the mechanical properties of AAC including research on its compressive strength, tensile strength, elastic modulus, Poisson’s ratio, stress–strain relationship under uniaxial compression, fracture properties, bond mechanism with steel reinforcement, dynamic mechanical properties, and high-temperature performance. Three types of AAC are reviewed: alkali-activated slag, alkali-activated fly ash, and alkali-activated slag-fly ash concretes. The applicability to AAC of design formulas found in codes of practice that were developed to estimate the basic mechanical performances of OPCC is also discussed. It is shown that, in general, AAC exhibits better bond performance with steel reinforcement and better strength performance after exposure to elevated temperatures than OPCC. For the other reviewed mechanical properties, the differences between AAC and OPCC largely depend on the proportions of raw materials in the concrete; specifically, the slag to fly ash ratio may be a very influential factor. As there is a trend to combine slag and fly ash in the production of AAC to achieve normal temperature curing and environmental friendliness, further research is deemed necessary to determine how the slag to fly ash ratio influences the fundamental mechanical properties of AAC and how this affects practical designs.
We developed a method to engineer well-distributed dicobalt phosphide (Co2P) nanoparticles encapsulated in N,P-doped graphene (Co2P@NPG) as electrocatalysts for hydrogen evolution reaction (HER). We ...fabricated such nanostructure by the absorption of initiator and functional monomers, including acrylamide and phytic acid on graphene oxides, followed by UV-initiated polymerization, then by adsorption of cobalt ions and finally calcination to form N,P-doped graphene structures. Our experimental results show significantly enhanced performance for such engineered nanostructures due to the synergistic effect from nanoparticles encapsulation and nitrogen and phosphorus doping on graphene structures. The obtained Co2P@NPG modified cathode exhibits small overpotentials of only −45 mV at 1 mA cm–2, respectively, with a low Tafel slope of 58 mV dec–1 and high exchange current density of 0.21 mA cm–2 in 0.5 M H2SO4. In addition, encapsulation by N,P-doped graphene effectively prevent nanoparticle from corrosion, exhibiting nearly unfading catalytic performance after 30 h testing. This versatile method also opens a door for unprecedented design and fabrication of novel low-cost metal phosphide electrocatalysts encapsulated by graphene.
Due to its impressive representation power, the graph convolutional network (GCN) has attracted increasing attention in the hyperspectral image (HSI) classification. However, the most of available ...GCN-based methods for HSI classification utilize superpixels as graph nodes, which ignore the pixel-wise spectral-spatial features. To overcome the issues, we propose a novel multi-feature fusion network (MFGCN), where two different convolutional networks, i.e., multi-scale GCN and multi-scale convolutional neural network (CNN), are utilized in two branches, separately. The multi-scale superpixel-based GCN can reduce the computing power requirements, deal with the problem of labeled deficiency, and refine the multi-scale spatial features from HSI. The multi-scale CNN can extract the multi-scale pixel-wise local features for HSI classification. Furthermore, we introduced a 1D CNN to extract the spectral features for superpixels (nodes), which is different from most existing methods. Finally, a concatenate operation is employed to fuse the complementary multi-scale features. In comparison with the state-of-the-art models on three datasets, the proposed method achieves superior experimental results and outperforms competitive methods.
Recently, graph convolutional network (GCN) has achieved promising results in hyperspectral image (HSI) classification. However, GCN is a transductive learning method, which is difficult to aggregate ...the new node. Besides, the existing GCN-based methods divide graph construction and graph classification into two stages ignoring the influence of constructed graph error on classification results. Moreover, the available GCN-based methods fail to understand the global and contextual information of the graph. In this article, we propose a novel multiscale graph sample and aggregate network with a context-aware learning method for HSI classification. The proposed network adopts a multiscale graph sample and aggregate network (graphSAGE) to learn the multiscale features from the local regions graph, which improves the diversity of network input information and effectively solves the impact of original input graph errors on classification. By employing a context-aware mechanism to characterize the importance among spatially neighboring regions, deep contextual and global information of the graph can be learned automatically by focusing on important spatial targets. Meanwhile, the graph structure is reconstructed automatically based on the classified objects as network training, which is able to effectively reduce the influence of the initial graph error on the classification result. Extensive experiments are conducted on three real HSI datasets, which are demonstrated to outperform the compared state-of-the-art methods.
The recycled fine-powder (RFP), produced during the recycling process, will induce a serious impact on the environment with improper disposition. A potential green way to reuse RFP is to add it as ...supplementary cementitious material in concrete. The effects of RFP on the hydration, microstructure, shrinkage and mechanical properties of ultra-high performance engineered cementitious composites (UHP-ECC) with different replacement ratios up to 50% were investigated. The hydration kinetics were compared among the different replacement ratios using the isothermal calorimetry, which demonstrated an accelerating effect of RFP to the hydration of UHP-ECC matrix. The phase development was quantified by the thermal gravimetric analysis and proved the pozzolanic effect of RFP. The compressive and tensile properties of UHP-ECCs were obtained at 3, 7 and 28 days, respectively, to trace their development along the curing ages. The addition of RFP significantly reduced the autogenous shrinkage of UHP-ECC. Besides, the single fiber pullout test was investigated to quantify the influence of RFP at the fiber level. The environmental scanned electron microscope analysis was conducted to study the morphology of PE fiber at the fracture surface.
•Up to 50% of cement was replaced by recycled fine powder (RFP) without significant loss in mechanical properties•Effects of RFP on the hydration, microstructure, shrinkage and mechanical properties of UHP-ECC were investigated•A linkage of micro-mechanical scale at the fiber level and to macro-mechanical scale at the composite level was established.
Adding short steel fibers into slag-based geopolymer mortar and concrete is an effective method to enhance their mechanical properties. The fracture properties of steel fiber-reinforced slag-based ...geopolymer concrete/mortar (SGC/SGM) and unreinforced control samples were compared through three-point bending (TPB) tests. The influences of steel fiber volume contents (1.0%, 1.5% and 2.0%) on the fracture properties of SGC and SGM were studied. Load-midspan deflection (
) curves and load-crack mouth opening displacement (
-CMOD) curves of the tested beams were recorded. The compressive and splitting tensile strengths were also tested. The fracture energy, flexural strength parameters, and fracture toughness of steel fiber-reinforced SGC and SGM were calculated and analyzed. The softening curves of steel fiber-reinforced SGC and SGM were determined using inverse analysis. The experimental results show that the splitting tensile strength, fracture energy, and fracture toughness are significantly enhanced with fiber incorporation. A strong correlation between the equivalent and residual flexural strengths is also observed. In addition, the trilinear strain-softening curves obtained by inverse analysis predict well of the load-displacement curves recorded from TPB tests.
To examine the effect of tanshinone IIA on Angiotensin II (Ang II)-induced proliferation and autophagy in vascular smooth muscle cells (VSMCs) and the related mechanism. VSMCs were treated with Ang ...II with or without tanshinone IIA (1, 5 and 10 µg/mL), and the proliferation, apoptosis in cells with different treatment were examined by methylthiazolyl tetrazolium (MTT) and flow cytometry methods. Moreover, the expression of autophagy related proteins and mitogen-activated protein kinase (MAPK) signaling molecules were examined by RT-quantitative (q)PCR and Western blot methods. Ang II induced significantly increase in the proliferation and autophagy of VSMCs, and the MAPK signaling was activated. Tanshinone IIA can attenuate Ang II-induced effects via down-regulating the MAPK signaling pathway. Tanshinone IIA can inhibit Ang II-induced proliferation and autophagy of VSMCs via regulating the MAPK signaling pathway.
The regional policy in China is shifting from solely gross domestic product (GDP) orientation to development that is more balanced between economic growth and ecological protection, as well as ...achieving equality among regions. Using land use maps and the adjusted value coefficients to assess ecosystem service values (ESV) for the 1980s, 1995, 2000, and 2010, we estimated the ESV in Shaanxi Province for different years, and characterized the spatial and temporal distribution of ESV and GDP. The results demonstrated that the total value of ecosystem services in Shaanxi Province increased from 208.95 billion Yuan in the 1980s to 309.76 billion Yuan in 2010. Variation Coefficient (Cv) and Theil index (T) were used to reflect the disparities of GDP or ESV within the study area. The values of Cv in descending order are GDP, ESV per capita, ESV, and GDP per capita. The Theil indexes of GDP were much greater than the ones of ESV. Variations of Cv and T showed that disparity in GDP kept increasing from the 1980s to 2000, then decreased; while no significant change in regional disparity of ESV were detected in parallel. The cities with higher GDP usually contributed little to ESV, and vice versa. The variation in GDP and ESV, in terms of the prefectural totals and per capita values, increased from the 1980s to 2010. This study provides an accessible way for local decision makers to evaluate the regional balance between economic growth and ecosystem services.
Luminescent metal clusters have attracted great interest in current research; however, the design synthesis of Al clusters with color‐tunable luminescence remains challenging. Herein, an ...Al8(OH)8(NA)16 (Al8, HNA = nicotinic acid) molecular cluster with dual luminescence properties of fluorescence and room‐temperature phosphorescence (RTP) is synthesized by choosing HNA ligand as phosphor. Its prompt photoluminescence (PL) spectrum exhibits approximately white light emission at room temperature. Considering that halogen atoms can be used to regulate the RTP property by balancing the singlet and triplet excitons, different CdX2 (X− = Cl−, Br−, I−) are introduced into the reactive system of the Al8 cluster, and three new Al8 cluster‐based metal‐organic frameworks, {Al8Cd3Cl5(OH)8(NA)17H2O·2HNA}n (CdCl2‐Al8), {Al8Cd4Br7(OH)8(NA)16CH3CN·NA·HNA}n (CdBr2‐Al8) and {Al8Cd8I16(OH)8(NA)16}n (CdI2‐Al8) are successfully obtained. They realize the color tunability from blue to yellow at room temperature. The origination of fluorescence and phosphorescence has also been illustrated by structure‐property analysis and theoretical calculation. This work provides new insights into the design of multicolor luminescent metal cluster‐based materials and develops advanced photo‐functional materials for multicolor display, anti‐counterfeiting, and encryption applications.
By fixing a phosphor of nicotinic acid into crystalline state, an Al8 cluster with dual luminescent properties of fluorescence and room‐temperature phosphorescence (RTP) is obtained. Introducing CdX2 into the reactive system of the Al8 cluster to regulate RTP by balancing the singlet and triplet excitons, three heterometallic Al8‐based metal‐organic frameworks are obtained. They realize the luminescent color tunability from blue to yellow.
With limited number of labeled samples, hyperspectral image (HSI) classification is a difficult Problem in current research. The graph neural network (GNN) has emerged as an approach to ...semi-supervised classification, and the application of GNN to hyperspectral images has attracted much attention. However, in the existing GNN-based methods a single graph neural network or graph filter is mainly used to extract HSI features, which does not take full advantage of various graph neural networks (graph filters). Moreover, the traditional GNNs have the problem of oversmoothing. To alleviate these shortcomings, we introduce a deep hybrid multi-graph neural network (DHMG), where two different graph filters, i.e., the spectral filter and the autoregressive moving average (ARMA) filter, are utilized in two branches. The former can well extract the spectral features of the nodes, and the latter has a good suppression effect on graph noise. The network realizes information interaction between the two branches and takes good advantage of different graph filters. In addition, to address the problem of oversmoothing, a dense network is proposed, where the local graph features are preserved. The dense structure satisfies the needs of different classification targets presenting different features. Finally, we introduce a GraphSAGE-based network to refine the graph features produced by the deep hybrid network. Extensive experiments on three public HSI datasets strongly demonstrate that the DHMG dramatically outperforms the state-of-the-art models.
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