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•A new facile strategy was developed to synthesize CuBTC and CuBTC@GO composites.•The as-synthesized CuBTC showed record-high CO2 adsorption capacity of 8.02 mmol/g.•The substantially ...increased CO2 capacity of CuBTC was due to increased porosity.•Binary breakthrough validated superior CO2/N2 selectivity on CuBTC@1%GO.•CuBTC@1%GO composite displayed remarkable regenerability after five cycles.
Easily regenerable adsorbents are highly desirable for CO2 capture. In this regard, a modified synthesis method for preparing CuBTC and its graphene oxide (CuBTC@GO) composites as adsorbents is developed using a mixed solvent strategy at 323 K for the first time. The addition of N, N-Dimethylformamide was vital for the crystallization of CuBTC at low temperature by accelerating the nucleation. The newly synthesized CuBTC showed much higher surface area and total pore volume, compared with CuBTC synthesized by conventional method. As a result, the as-synthesized CuBTC showed a CO2 adsorption capacity of 8.02 mmol/g at 273 K, 1 bar, which was 17–90% higher than the reported CO2 capacity of CuBTC prepared by conventional method. The fabrication of CuBTC@GO composites enhanced the CO2 adsorption capacity mainly through the improved porosity and dispersion force. Compared with CuBTC, an improved CO2/N2 selectivity for CuBTC@1%GO was obtained from the binary breakthrough experiments, which is beneficial to practical gas separations. The partition coefficient of CuBTC and CuBTC@GO composite were evaluated at different breakthrough levels, e.g., 5%, 10% and 100%, with an inlet CO2 partial pressure of 0.15 bar. CuBTC@1%GO displayed higher partition coefficient values than CuBTC at all three breakthrough levels. The cyclic adsorption experiments for regenerability evaluation showed that the CO2 adsorption reversibility for CuBTC@1%GO composite could maintain above 90%, while that of CuBTC dropped to less than 74% after five adsorption-desorption cycles. The CuBTC@GO composite would be a promising CO2 capture adsorbent with both high CO2 adsorption capacity and remarkable regeneration performance.
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The novel magnetically retrievable Bi2WO6/MCNOs (MCNOs, magnetic carbon nano-onions) composite was synthesized via a simple hydrothermal method. The characteristics of the as-prepared ...materials were explored by XRD, Raman, SEM, TEM, XPS, UV–vis DRS, N2 adsorption-desorption techniques and FT-IR techniques, and the paramagnetic nature of the as-prepared composite was determined by hysteresis loops measurements. In this work, rhodamine B (RhB), methyl orange (MO), methylene blue (MB), tetracycline (TC) and p-nitrophenol (PNP) were chosen as the typical organic pollutants to evaluate the photodegradation efficiency of the as-prepared composite. The results indicated that Bi2WO6/MCNOs have a superior performance on photocatalytic activity than pure Bi2WO6. Furthermore, a possible mechanism was proposed based on the physic-chemical and photocatalytic properties. This work will provide new methodology for designing next generation photocatalyst for environmental purification.
Abstract only
SIRT4 belongs to the sirtuin family, which comprises NAD+ dependent lysine decaylases. SIRT4 has ADP‐ribosyltransferase, lipoamidase and decaylase activeties. In the study, we observed ...differential regulated lysine malonylationin ovarian granulosa cell lines (KGN) by the lable‐free quantitive technique. In SIRT4 depleted cells, there were a total of 65 malonylationsites from 56 proteins, including 39 up‐regulated malonylated proteins and 17 down‐regulated proteins. The differential regulated malonylation proteins were enriched in metabolic process and response to stimulus in GO biological process. We also detected the malonylation levels of PGK1 K131 and ENO1 K80 sites were decreased significantly in SIRT4 depleted cell lines, which were the same sites of PGK1 and ENO1 changed in PCOS primary ovarian granulosa cells (GCs). In addition, SIRT4 was decreased in PCOS GCs. In a conclusion, SIRT4 may regulate mitochondrial metabolic ability in PCOS GCs through modifying lysine malonylation of proteins in glycolysis pathway.
Support or Funding Information
This work was supported in part by the National Key R&D Program of China (2017YFC1001003, 2018YFC1003203), the National Natural Science Funds for general program (81571400, 81771580).
Abstract
Multiple cropping is a widespread approach for intensifying crop production through rotations of diverse crops. Maps of cropping intensity with crop descriptions are important for supporting ...sustainable agricultural management. As the most populated country, China ranked first in global cereal production and the percentages of multiple-cropped land are twice of the global average. However, there are no reliable updated national-scale maps of cropping patterns in China. Here we present the first recent annual 500-m MODIS-based national maps of multiple cropping systems in China using phenology-based mapping algorithms with pixel purity-based thresholds, which provide information on cropping intensity with descriptions of three staple crops (maize, paddy rice, and wheat). The produced cropping patterns maps achieved an overall accuracy of 89% based on ground truth data, and a good agreement with the statistical data (R
2
≥ 0.89). The China Cropping Pattern maps (ChinaCP) are available for public download online. Cropping patterns maps in China and other countries with finer resolutions can be produced based on Sentinel-2 Multispectral Instrument (MSI) images using the shared code.
Boron nitride nanosheets (BNNSs) exfoliated from hexagonal boron nitride (h-BN) show great potential in polymer-based composites due to their excellent mechanical properties, highly thermal ...conductivity, and insulation properties. Moreover, the structural optimization, especially the surface hydroxylation, of BNNSs is of importance to promote their reinforcements and optimize the compatibility of its polymer matrix. In this work, BNNSs were successfully attracted by oxygen radicals decomposed from di-tert-butylperoxide (TBP) induced by electron beam irradiation and then treated with piranha solution. The structural changes of BNNSs in the modification process were deeply studied, and the results demonstrate that the as-prepared covalently functionalized BNNSs possess abundant surface hydroxyl groups as well as reliable structural integrity. Of particular importance is that the yield rate of the hydroxyl groups is impressive, whereas the usage of organic peroxide and reaction time is greatly reduced due to the positive effect of the electron beam irradiation. The comparisons of PVA/BNNSs nanocomposites further indicate that the hydroxyl-functionalized BNNSs effectively promote mechanical properties and breakdown strength due to the enhanced compatibility and strong two-phase interactions between nanofillers and the polymer matrix, which further verify the application prospects of the novel route proposed in this work.
Attention mechanisms have gradually become necessary to enhance the representational power of convolutional neural networks (CNNs). Despite recent progress in attention mechanism research, some open ...problems still exist. Most existing methods ignore modeling multi-scale feature representations, structural information, and long-range channel dependencies, which are essential for delivering more discriminative attention maps. This study proposes a novel, low-overhead, high-performance attention mechanism with strong generalization ability for various networks and datasets. This mechanism is called Multi-Scale Spatial Pyramid Attention (MSPA) and can be used to solve the limitations of other attention methods. For the critical components of MSPA, we not only develop the Hierarchical-Phantom Convolution (HPC) module, which can extract multi-scale spatial information at a more granular level utilizing hierarchical residual-like connections, but also design the Spatial Pyramid Recalibration (SPR) module, which can integrate structural regularization and structural information in an adaptive combination mechanism, while employing the Softmax operation to build long-range channel dependencies. The proposed MSPA is a powerful tool that can be conveniently embedded into various CNNs as a plug-and-play component. Correspondingly, using MSPA to replace the 3 × 3 convolution in the bottleneck residual blocks of ResNets, we created a series of simple and efficient backbones named MSPANet, which naturally inherit the advantages of MSPA. Without bells and whistles, our method substantially outperforms other state-of-the-art counterparts in all evaluation metrics based on extensive experimental results from CIFAR-100 and ImageNet-1K image recognition. When applying MSPA to ResNet-50, our model achieves top-1 classification accuracy of 81.74% and 78.40% on the CIFAR-100 and ImageNet-1K benchmarks, exceeding the corresponding baselines by 3.95% and 2.27%, respectively. We also obtained promising performance improvements of 1.15% and 0.91% compared to the competitive EPSANet-50. In addition, empirical research results in autonomous driving engineering applications also demonstrate that our method can significantly improve the accuracy and real-time performance of image recognition with cheaper overhead. Our code is publicly available at https://github.com/ndsclark/MSPANet.
•A new type of tubular column, which is composed of GFRP tube and discrete BFRP needles reinforced SWSSC, was proposed.•The axial compression properties and lateral bending properties of the tubular ...columns were tested.•Two types of coarse aggregates, i.e., gravel and coral, were adopted.•The proposed columns have promising application prospects in marine engineering.
This paper proposes a new type of tubular column, which is composed of glass fiber-reinforced polymer (GFRP) tube and discrete basalt fiber-reinforced polymer (BFRP) needles reinforced seawater sea-sand concrete (SWSSC). The discrete BFRP needles, with an aspect ratio of 10.0, were cut from BFRP bar production scrap and mixed into fresh concrete to replace 20% of the coarse aggregates by volume. The axial compression properties and lateral bending properties of the tubular columns were tested. The test variables included the wall thickness of the GFRP tubes (i.e., 3 mm and 4 mm), the type of coarse aggregates (i.e., gravel or coral), and the incorporation of BFRP needles or not. Additionally, unconfined bare columns were tested for comparison. The test results showed that the inclusion of BFRP needles had moderate adverse effects on the peak compressive strength of bare columns: the peak axial stress reduced slightly by 2.5% for gravel concrete and 7.2% for coral concrete, respectively. Besides, for the four types of concrete in this paper, the 3- and 4-mm GFRP tube-confinement increased the peak axial compressive strengths by 23 ~ 52% and 65 ~ 83%, respectively. In addition, the adoption of GFRP tubes was able to improve the bending performance of columns significantly, especially for energy consumption.
In this paper we present several methods to identify precursors that show great promise for early predictions of solar flare events. A data preprocessing pipeline is built to extract useful data from ...multiple sources, Geostationary Operational Environmental Satellites and Solar Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager (HMI), to prepare inputs for machine learning algorithms. Two classification models are presented: classification of flares from quiet times for active regions and classification of strong versus weak flare events. We adopt deep learning algorithms to capture both spatial and temporal information from HMI magnetogram data. Effective feature extraction and feature selection with raw magnetogram data using deep learning and statistical algorithms enable us to train classification models to achieve almost as good performance as using active region parameters provided in HMI/Space‐Weather HMI‐Active Region Patch (SHARP) data files. Case studies show a significant increase in the prediction score around 20 hr before strong solar flare events.
Plain Language Summary
A solar flare occurs when magnetic energy that has built up in the solar atmosphere is suddenly released. We show the potential of using data‐driven approaches to identify precursors of strong solar flare events that might strongly affect the near‐Earth space environment. We use data from the Geostationary Operational Environmental Satellites and Solar Dynamics Observatory/Helioseismic and Magnetic Imager (HMI) to prepare inputs for machine learning algorithms. A handful of case studies show that the prediction score of strong flare events increases significantly around 20 hr before the event. Moreover, for the classification of strong versus weak flares, using machine‐derived features gives performance comparable to that achieved using active region parameters provided in HMI/Space‐Weather HMI‐Active Region Patch data files.
Key Points
We adopt deep learning algorithms that take time series of active region observations as input to perform solar flare classifications
We demonstrate an overall similarity in classifier performance using machine learning‐derived versus human‐derived parameters
We illustrate the effectiveness of the proposed algorithms in identifying precursors for strong solar flare events from quiet times with case studies
Electrocardiogram (ECG) mapping can provide vital information in sports training and cardiac disease diagnosis. However, most electronic devices for monitoring ECG signals need to use multiple long ...wires, which limit their wearability and conformability in practical applications, while wearable ECG mapping based on integrated sensor arrays has been rarely reported. Herein, ultra‐flexible organic electrochemical transistor (OECT) arrays used for wearable ECG mapping on the skin surface above a human heart are presented. QRS complexes of ECG signals at different recording distances and directions relative to the heart are obtained. Furthermore, the ECG signals are successfully analyzed by the devices before and after exercise, indicating potential applications in some sports training and fitness scenarios. The OECT arrays that can conveniently monitor spacial ECG signals in the heart region may find niche applications in wearable electronics and healthcare products in the future.
Flexible organic electrochemical transistors can well conform on the skin surface and capture fine structure of transient electrophysiological (ECG) signals at different positions relative to a human heart. Wearable ECG mapping is achieved with an array of devices before and after exercise, which can provide more detailed information for the diagnosis of cardiac disease and the effect of sports training.
Battery cathodes are complex multiscale, multifunctional materials. The length scale at which the dominant impedance arises may be difficult to determine even with the most advanced experimental ...characterization efforts, and thus modeling can play an important role in analysis. Discharge and voltage relaxation curves, interrogated with theory, are used to distinguish between transport impedance that arise on the scale of the active crystal and on the scale of agglomerates (secondary particles) comprised of nanoscale crystals. Model-selection algorithms are applied to determine that the agglomerate scale is dominant in the Li Ni 0.33 Mn 0.33 Co 0.33 O 2 electrode studied here. Furthermore, conditions where the agglomerate and crystal-scale models yield distinct simulation results are demonstrated, providing approaches that can be applied to other systems.
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