•Project presents an application of convolutional neural networks (CNN) in cracks.•Different CNNs are established by the processes of structure design, training and testing.•The crack feature points ...are extracted by feature extraction CNN to establish 3D model.•CNN is able to recognize concealed cracks from other damage in GPR images with zero error.•CNNs could be accurately used for the recognition, location of concealed crack of asphalt pavement.
Concealed cracks in asphalt pavement are the cracks that originate below the surface of the pavement. These cracks are a major contributing factor to pavement damage, in addition to being a major contributing factor to the formation of reflection cracks. The detection of a concealed crack is considered challenging because the location of the crack is, by definition, difficult to find. Therefore, the research on the utilization of ground penetrating radar (GPR) to locate concealed cracks has gained significant interest in recent years. However, the manually processed GPR image used for the recognition, location, and measurement of concealed cracks is inefficient and inaccurate. This project presents an application of convolutional neural networks (CNNs) to GPR images that automatically recognizes, locates, measures, and produces a 3D reconstruction of concealed cracks. In this project, three different CNNs (recognition, location, and feature extraction) were established to accomplish the aforementioned tasks automatically. Each CNN is developed through processes of structural design, training, and testing. The recognition CNN was designed to distinguish concealed cracks from other types of damage in a GPR image, the location CNN determined the location and length measurement of concealed crack images based on the results provided by the recognition CNN, and crack feature points were extracted by the feature extraction CNN to establish the 3D reconstruction models of the concealed cracks. The 3D reconstruction models were then used to calculate crack volume and predict the growth tendency of cracks. The results indicated that the recognition CNN is able to distinguish concealed cracks from other types of damages in 6482 GPR images with zero errors. In addition, the length recognition results calculated from the location CNN possess a 0.2543cm mean squared error, a 0.978cm maximum length error, and a 0.504cm average error in the test samples. Meanwhile, the feature extraction CNN is able to provide feature points for a 3D reconstruction model. The results of this study suggest that the CNNs could be accurately used for the recognition, location, and 3D reconstruction of concealed cracks in asphalt pavement in real-world applications.
•Provided direct visualization information for the SEI film formation from aqueous solution for the first time.•SEI film form from aqueous solution mainly composed of Li2CO3 and LiF.•Young’s modulus ...of 4–6 nm thick SEI layer is 30 ± 10 GPa.
The structure and chemical composition of solid electrolyte interphase (SEI) play a critical role in the performance of Li-ion batteries. However, the complexity of SEI layer and lack of relevant and efficient characterization techniques limit understanding of SEI formation mechanism and associated properties. Moreover, compared with organic electrolyte-based SEI, few studies have focused on aqueous electrolyte-based solid-electrolyte interphase (A-SEI). Herein, we present in-situ AFM images to demonstrate the morphological evolution of SEI in the presence of 21 M lithium bis(trifluoromethane sulfonyl) imide (LiTFSI) aqueous electrolyte. Moreover, a combination of electrochemical-controlled atomic force microscopy (EC-AFM) and ex-situ X-ray photoelectron spectroscopy (XPS) is utilized to unveil the mechanism of SEI formation and identify its components. The results reveal that A-SEI layer is unevenly distributed and the inner and outer layers are mainly composed of Li2CO3 and LiF, respectively. The Young’s modulus of 4–6 nm thick SEI layer is found to be 30 ± 10 GPa. The current study provides novel insights into the formation of A-SEI by using in-situ AFM and provides a baseline for the future development of aqueous electrolytes for Li-ion batteries.
Wireless traffic volume detectors play a critical role for measuring the traffic-flow in a real-time for current Intelligent Traffic System. However, as a battery-operated electronic device, ...regularly replacing battery remains a great challenge, especially in the remote area and wide distribution. Here, we report a self-powered active wireless traffic volume sensor by using a rotating-disk-based hybridized nanogenerator of triboelectric nanogenerator and electromagnetic generator as the sustainable power source. Operated at a rotating rate of 1000 rpm, the device delivered an output power of 17.5 mW, corresponding to a volume power density of 55.7 W/m3 (P d = P/V, see Supporting Information for detailed calculation) at a loading resistance of 700 Ω. The hybridized nanogenerator was demonstrated to effectively harvest energy from wind generated by a moving vehicle through the tunnel. And the delivered power is capable of triggering a counter via a wireless transmitter for real-time monitoring the traffic volume in the tunnel. This study further expands the applications of triboelectric nanogenerators for high-performance ambient mechanical energy harvesting and as sustainable power sources for driving wireless traffic volume sensors.
Conductive hydrogels have become one of the most promising materials for skin-like sensors because of their excellent biocompatibility and mechanical flexibility. However, the limited stretchability, ...low toughness, and fatigue resistance lead to a narrow sensing region and insufficient durability of the hydrogel-based sensors. In this work, an extremely stretchable, highly tough, and anti-fatigue conductive nanocomposite hydrogel is prepared by integrating hydrophobic carbon nanotubes (CNTs) into hydrophobically associated polyacrylamide (HAPAAm) hydrogel. In this conductive hydrogel, amphiphilic sodium dodecyl sulfate was used to ensure uniform dispersion of CNTs in the hydrogel network, and hydrophobic interactions between the hydrogel matrix and the CNT surface formed, greatly improving the mechanical properties of the hydrogel. The obtained CNTs/HAPAAm hydrogel showed excellent stretchability (ca. 3000%), toughness (3.42 MJ m–3), and great anti-fatigue property. Moreover, it exhibits both high tensile strain sensitivity in the wide strain ranges (gauge factor = 4.32, up to 1000%) and high linear sensitivity (0.127 kPa–1) in a large-pressure region within 0–50 kPa. The CNTs/HAPAAm hydrogel-based sensors can sensitively and stably detect full-range human activities (e.g., elbow rotation, finger bending, swallowing motion, and pronouncing) and handwriting, demonstrating the CNTs/HAPAAm hydrogel’s potential as the wearable strain and pressure sensors for flexible devices.
The O-N-S co-doped hierarchical porous carbons are prepared by direct pyrolysis of kraft lignin which is the byproduct from papermaking black liquor. The proposed preparation method is extremely ...facile, green-environmental, and low-cost without any additional activating agents, additives or templates. The kraft lignin-derived carbon materials possess large specific surface areas (338−1307 m2 g−1), hierarchical porous structures and abundant multi-heteroatoms co-doping (9.84–19.91 wt%). Benefiting from above synergistic advantages, the as-fabricated symmetric supercapacitor in aqueous electrolyte delivers a high specific capacitance of 244.5 F g−1 at 0.2 A g−1, excellent rate-capability (81.8% retention of initial capacitance at 40.0 A g−1), and outstanding cycling stability (91.6% retention over 10000 cycles). Importantly, this device in aqueous electrolyte delivers an energy density of 8.5 W h kg−1 at a power density of 100 W kg−1. Furthermore, a remarkable energy density of 66.8 W h kg−1 at a power density of 1.75 kW kg−1 has been achieved and 32.2 W h kg−1 is still maintained even at an ultrahigh power density of 40.0 kW kg−1 when ionic liquid serves as electrolyte. This study demonstrates the successful conversion of low-valued natural biomass derivative into sustainable high-performance supercapacitor electrode materials with a simple, low-cost, and green-environmental production process.
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As an important protein source, soybean products can cause intestinal inflammation and injury in many animals including human beings, particularly infants and juvenile individuals. Research in this ...field has been performed for terrestrial animals and fish, but still lacks integrity and systematicness. In this study, the main biological processes in the intestinal tract of marine fish juvenile pearl gentian grouper in the state of soybean meal-induced enteritis (SBMIE) were analyzed. A total of 720 groupers with an approximate initial weight of 12.5 g were randomly divided into three groups: the fish meal (FM) control group, the 20% SBM group (SBM20), and the SBM40 group (n = 4). Three iso-nitrogenous and iso-lipidic diets were prepared and fed to fish for 10 weeks. Each barrel contained a water volume of about 1 m
in and was exposed to natural light and temperature. Results indicated that the growth and physiology of groupers fed with SBM were significantly negatively affected, with the gene expressions of intestinal structural protein abnormal. 16SrDNA high-throughput sequencing showed that the intestinal microflora played an important role in the pathogenesis of pearl gentian grouper SBMIE, which may activate a variety of pathogen pattern recognition receptors, such as toll-like receptors (TLRs), RIG-I-like receptors, and nod-like receptors. Transcriptome analysis revealed that changes of the SBMIE signaling pathway in pearl gentian groupers were conservative to some extent than that of terrestrial animals and freshwater fish. Moreover, the TLRs-nuclear factor kappa-B signaling pathway becomes activated, which played an important role in SBMIE. Meanwhile, the signal pathways related to nutrient absorption and metabolism were generally inhibited. Metabolomics analysis showed that isoflavones and saponins accounted for a large proportion in the potential biomarkers of pearl gentian grouper SBMIE, and most of the biomarkers had significantly positive or negative correlations with each other; 56 metabolites were exchanged between intestinal tissues and contents, which may play an important role in the development of enteritis, including unsaturated fatty acids, organic acids, amino acids, vitamins, small peptides, and nucleotides, etc. These results provide a basic theoretical reference for solving the intestinal issues of fish SBMIE and research of inflammatory bowel disease in mammals.
The experiments were done to investigate the effect of soil pH and organic matter content on EDTA-extractable heavy metal contents in soils and heavy metal concentrations in rice straw and grains. ...EDTA-extractable Cr contents in soils and concentrations in rice tissues were negatively correlated with soil pH, but positively correlated with organic matter content. The combination of soil pH and organic matter content would produce the more precise regression models for estimation of EDTA-Cu, Pb and Zn contents in soils, demonstrating the distinct effect of the two factors on the availability of these heavy metals in soils. Soil pH greatly affected heavy metal concentrations in rice plants. Furthermore, inclusion of other soil properties in the stepwise regression analysis improved the regression models for predicting straw Fe and grain Zn concentrations, indicating that other soil properties should be taken into consideration for precise predicting of heavy metal concentrations in rice plants.
Soil pH and organic matter content significantly affect heavy metal availability and accumulation in rice plants.
► Birnessite-type MnO2 hierarchical porous nanostructures were synthesized. ► The MnO2 exhibits specific area of 160m2g−1 and pore size distribution of 5.4nm. ► The MnO2 showed a specific capacitance ...of 269Fg−1 and good cycling performance. ► The hierarchical nanostructure has great potential for supercapacitor applications.
In this work, a rapid hydrothermal method has been developed to synthesize hierarchical porous nanostructures assembled from ultrathin birnessite-type MnO2 nanosheets without using any template and surfactant. The microstructure, morphology, and thermal stability of the products were characterized by X-ray diffraction, scanning electron microscopy, transmission electron microscopy, X-ray photoelectron spectroscopy, N2 adsorption–desorption isotherms, and thermal gravimetric analysis. The electrochemical properties of the as-prepared MnO2 as an electrode material for supercapacitor were investigated by cyclic voltammetry, galvanostatic charge/discharge and electrochemical impedance spectroscopy measurements in 1M Na2SO4 electrolyte, and a specific capacitance of 269Fg−1 was achieved at a current density of 0.3Ag−1. The specific capacitance retention and coulombic efficiency were 94% and 98% respectively after 2000 cycles at 1Ag−1. Furthermore, asymmetric supercapacitor was also fabricated with MnO2 as the positive electrode and activated carbon as the negative electrode in 1M Na2SO4 electrolyte with the cell voltage to 2V.
To solve the problem of the low recognition rate of mixed gases and consider the phenomenon of low prediction accuracy when traditional gas-concentration-prediction methods deal with nonlinear data, ...this paper proposes a mixed-gas identification and gas-concentration-prediction method based on a support vector machine (SVM) optimized by a sparrow search algorithm (SSA). Principal component analysis (PCA) is applied to perform data dimensionality reduction on the input data, and SSA is adopted to optimize the SVM hyperparameters to improve the recognition rate and gas-concentration-prediction accuracy of mixed gases. For the mixed-gas identification, the classification accuracy is significantly improved under the proposed SSA optimization SVM method (SSA-SVM), compared with random forest (RF), extreme-learning machine (ELM), and BP neural network methods. With respect to gas-concentration prediction, the maximum fitting degrees reached 99.34% for single gas-concentration prediction and 97.55% for mixed-gas-concentration prediction. The experimental results show that the SSA-SVM method had a high recognition rate and high concentration-prediction accuracy in gas-mixture detection.
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