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MoO3 has become a very promising energy storage material owing to its high theoretical capacity and layered structure. However, MoO3 suffers from low specific capacitance and fast degradation performance due to pulverization caused by volume change during discharge and charge process. Here, we report the MoO3 nanoplates (MoO3 NPs) from Mo-based metal–organic frameworks (Mo-MOFs) via a facile heating treatment. When used as an anode in sodium-ion batteries (SIBs), the material showed 154 mAh g−1 superior discharge capacity at 50 mA g−1 after 1200 cycles. Even at 500 mA g−1, it also showed 217 mAh g−1 high specific capacity after 500 cycles. This specific MoO3 material design strategy offers suitable conditions for relieving the volume expansion and provides multiple channels for Na+ transport and electron transfer in MoO3 during discharge and charge process. This work highlights the importance of MoO3 nanoplates in preventing the pulverization caused by volume expansion in SIBs.

As a linkage among water, energy, and carbon cycles, global actual evapotranspiration (ET) plays an essential role in agriculture, water resource management, and climate change. Although it is difficult to estimate ET over a large scale and for a long time, there are several global ET datasets available with uncertainty associated with various assumptions regarding their algorithms, parameters, and inputs. In this study, we propose a long-term synthesized ET product at a kilometer spatial resolution and monthly temporal resolution from 1982 to 2019. Through a site-pixel evaluation of 12 global ET products over different time periods, land surface types, and conditions, the high-performing products were selected for the synthesis of the new dataset using a high-quality flux eddy covariance (EC) covering the entire globe. According to the study results, Penman–Monteith–Leuning (PML), the operational Simplified Surface Energy Balance (SSEBop), the Moderate Resolution Imaging Spectroradiometer (MODIS, MOD16A2105), and the Numerical Terradynamic Simulation Group (NTSG) ET products were chosen to create the synthesized ET set. The proposed product agreed well with flux EC ET over most of the all comparison levels, with a maximum relative mean error (RME) of 13.94 mm (17.13 %) and a maximum relative root mean square error (RRMSE) of 38.61 mm (47.45 %). Furthermore, the product performed better than local ET products over China, the United States, and the African continent and presented an ET estimation across all land cover classes. While no product can perform best in all cases, the proposed ET can be used without looking at other datasets and performing further assessments. Data are available on the Harvard Dataverse public repository through the following Digital Object Identifier (DOI): https://doi.org/10.7910/DVN/ZGOUED (Elnashar et al., 2020), as well as on the Google Earth Engine (GEE) application through this link: https://elnashar.users.earthengine.app/view/synthesizedet (last access: 21 January 2021).

The rational design of efficient Earth-abundant electrocatalysts for the ethanol oxidation reaction (EOR) is the key to developing direct ethanol fuel cells (DEFCs). Among these, the smart structure is highly demanded for highly efficient and stable non-precious electrocatalysts based on transition metals (such as Ni, Co, and Fe). In this work, high-performance NiCo-layered double hydroxide@carbon nanotube (NiCo-LDH@CNT) architectures with hollow nanocage structures as electrocatalysts for EOR were prepared via sacrificial ZIF-67 templates on CNTs. Comprehensive structural characterizations revealed that the as-synthesized NiCo-LDH@CNTs architecture displayed 3D hollow nanocages of NiCo-LDH and abundant interfacial structure between NiCo-LDH and CNTs, which could not only completely expose active sites by increasing the surface area but also facilitate the electron transfer during the electrocatalytic process, thus, improving EOR activity. Benefiting from the 3D hollow nanocages and interfacial structure fabricated by the sacrificial ZIF-67-templated method, the NiCo-LDH@CNTs-2.5% architecture exhibited enhanced electrocatalytic activity for ethanol oxidation compared to single-component NiCo-LDH, where the peak current density was 11.5 mA·cm−2, and the jf/jb value representing the resistance to catalyst poisoning was 1.72 in an alkaline environment. These results provide a new perspective on the fabrication of non-precious metal electrocatalysts for EOR in DEFCs.

Evapotranspiration (ET) is one of the components in the water cycle and the surface energy balance systems. It is fundamental information for agriculture, water resource management, and climate change research. This study presents a scheme for regional actual evapotranspiration estimation using multi-source satellite data to compute key land and meteorological variables characterizing land surface, soil, vegetation, and the atmospheric boundary layer. The algorithms are validated using ground observations from the Heihe River Basin of northwest China. Monthly data estimates at a resolution of 1 km from the proposed algorithms compared well with ground observation data, with a root mean square error (RMSE) of 0.80 mm and a mean relative error (MRE) of −7.11%. The overall deviation between the average yearly ET derived from the proposed algorithms and ground-based water balance measurements was 9.44% for a small watershed and 1% for the entire basin. This study demonstrates that both accuracy and spatial depiction of actual evapotranspiration estimation can be significantly improved by using multi-source satellite data to measure the required land surface and meteorological variables. This reduces dependence on spatial interpolation of ground-derived meteorological variables which can be problematic, especially in data-sparse regions, and allows the production of region-wide ET datasets.

Syndecan-1 (SDC1) modified lipid bilayer (LB)-coated mesoporous silica nanoparticles (MSN) to co-deliver gemcitabine (GEM) and honokiol (HNK) were prepared for the targeting treatment of pancreatic cancer. The encapsulation efficiencies of GEM and HNK in SDC1-LB-MSN-GEM/HNK were determined to be 60.3 ± 3.2% and 73.0 ± 1.1%. The targeting efficiency of SDC1-LB-MSN-GEM/HNK was investigated in BxPC-3 cells in vitro. The fluorescence intensity in the cells treated with SDC1-LB-MSN-Cou6 was 2-fold of LB-MSN-Cou6-treated cells, which was caused by SDC1/IGF1R-mediated endocytosis. As anticipated, its cytotoxicity was significantly increased. Furthermore, the mechanism was verified that SDC1-LB-MSN-HNK induced tumor cell apoptosis through the mitochondrial apoptosis pathway. Finally, the biodistribution, tumor growth inhibition, and preliminary safety studies were performed on BALB/c nude mice bearing BxPC-3 tumor models. The tumor growth inhibition index of SDC1-LB-MSN-GEM/HNK was 56.19%, which was 1.45-fold and 1.33-fold higher than that of the free GEM/HNK and LB-MSN-GEM/HNK treatment groups, respectively. As a result, SDC1-LB-MSN-GEM/HNK combined advantages of both GEM and HNK and simultaneously targeted and eliminated pancreatic cancerous and cancer-associated stromal cells. In summary, the present study demonstrated a new strategy of synergistic GEM and HNK to enhance the therapeutic effect of pancreatic cancer via the targeting depletion of tumor stroma.

This study proposes an evapotranspiration (ET) disaggregation model based on slope units. Different slope units are first delineated based on digital elevation model data with high spatial resolution. Key factors influencing ET variability across topographies, such as radiation, vegetation, and moisture, are integrated using Sentinel-2 and DEM data to construct an indicating factor. A slope-scale ET disaggregation model is developed using ETWatch data (1 km resolution) and the integrated factor, yielding reliable 10 m resolution ET data that reflect slope-scale variations. The validation in Huairou and Baotianman shows coefficients of determination of 0.9 and 0.91, respectively, and root mean square errors of 0.45 mm and 0.47 mm. Compared to the original 1 km resolution ET data, the disaggregated results show improved accuracy, with R2 values increasing by 1% (Huairou) and 2% (Baotianman) and RMSE decreasing by 21% and 13%, respectively. This model offers a novel approach for estimating forest evapotranspiration in mountainous areas and significant potential for water resource management and sustainable land–water allocation.

As a vital role in the processes of the energy balance and hydrological cycles, actual evapotranspiration (ET) is relevant to many agricultural, ecological and water resource management studies. The available global or regional ET products provide ET estimations with various temporal ranges, spatial resolutions and calculation methods (algorithms, inputs and parameterization, etc.), leading to varying degrees of introduced uncertainty. Northern China is the main agriculturally productive region supporting the whole country; thus, understanding the spatial and temporal changes in ET is essential to ensure water resource and food security. We developed a synthesis ET dataset for Northern China at a 1000 m spatial resolution, with a monthly temporal resolution covering a period ranging from 1982 to 2017, using an in-depth assessment of several ET products. Specifically, assessments were performed using in situ measured ET from eddy covariance (EC) observation towers at the site-pixel scale over interannual months under the conditions of different land cover types, climatic zones and elevation levels to select the most optimally performing ET products to be used in the synthesized ET dataset. Eight indicators under 21 conditions were involved in the assessment sheet, while the statistics of the different ET product occurrences and corresponding ratios were analyzed to select the best-performing ET products to build the synthesis ET dataset using the weighted mean method. The weights were determined by the Taylor skill score (TSS), calculated with ET products and EC ET observation data. Based on the assessment results, the Penman–Monteith–Leuning (PML_v2), ETWatch and Operational Simplified Surface Energy Balance (SSEBop) datasets were selected for implementation in the synthesis ET dataset from 2003 to 2017, while Global Land Evaporation Amsterdam Model (GLEAM) v3.3a, complementary relationship (CR) ET, and Numerical Terradynamic Simulation Group (NTSG) datasets were chosen for the synthesis ET dataset from 1982 to 2002. The weighted mean synthesized results from 2003 to 2017 performed well when compared to the in situ measured EC ET values produced under all of the above conditions, while the synthesized results from 1982 to 2002 performed well through the water balance method in Heihe River Basin. These results can provide more stable ET estimations for Northern China, which can contribute to relevant agricultural, ecological and hydrological studies.

Evapotranspiration (ET) is the primary mechanism of water transformation between the land surface and atmosphere. Accurate ET estimation given complex terrain conditions is essential to guide water resource management in mountainous areas. This study is based on the ETWatch model driven by Sentinel-2 remote sensing data at a spatial resolution of 10 m incorporating a net radiation model considering the impact of a complex terrain. We tested our model with two years of data in two regions with a high relief near the Huairou (2020) and Baotianman (2019) weather stations. Regarding the validation results of the ET model, the coefficient of determination (R2) reached 0.84 in Huairou and 0.86 in Baotianman, while the root mean square error (RMSE) value reached 0.59 mm in Baotianman and 0.82 mm in Huairou. The validation results indicated that the model is applicable in regions with a complex terrain, and the ET results can capture topographic textures. In terms of the slope aspect, the ET value on south-facing slopes is higher than that on north-facing slopes in both study areas. Accurate ET monitoring in mountainous regions with a high relief yields a profound meaning in obtaining a better understanding of the characteristics of heat and water fluxes at different vegetation growth stages and underlying surface types, which can provide constructive suggestions for water management in mountainous areas.

Despite ongoing research efforts aimed at understanding the structural response of steel fiber reinforced concrete (SFRC), there is very limited research on the failure characteristics and theoretical compression-bending capacity of unreinforced steel fiber reinforced concrete (SFRC without rebars, USFRC). In this study, the cube compression tests, notched beam tests, and full-scale segment compression-bending tests are carried out to investigate the flexural performance of USFRC. The crack width–bending moment curves, load–deflection curves, and ultimate load of USFRC segments are obtained. Additionally, the theoretical compression-bending capacity of USFRC segments according to Model Code 2010 is investigated and the calculation methods applicable to different fiber contents, segment sizes, and mix proportions are obtained, which can provide a basis for predicting the performance of USFRC segments in related engineering applications, and some conclusions can be drawn. The results show that steel fibers can slightly improve the compressive strength of concrete, and the improvement capacity varies with different mix proportions and fiber contents. The addition of steel fibers can also improve the compressive failure mode of concrete. The relationships among the crack width, bending moment, and eccentricity can be expressed by a multivariate linear regression equation, and the relationship between the bending moment and deflection can be fitted by a quadratic equation. Both fitting effects are good. Based on the Model Code 2010 calculation model, a calculation method for the compression-bending capacity of USFRC is proposed, and the calculation method of residual tensile strength of steel fiber is modified. The new method can predict the compression-bending capacity of USFRC more accurately.

Drought is among the most common natural disasters in North China. In order to monitor the drought of the typically arid areas in North China, this study proposes an innovative multi-source remote sensing drought index called the improved Temperature–Vegetation–Soil Moisture Dryness Index (iTVMDI), which is based on passive microwave remote sensing data from the FengYun (FY)3B-Microwave Radiation Imager (MWRI) and optical and infrared data from the Moderate Resolution Imaging Spectroradiometer (MODIS), and takes the Shandong Province of China as the research area. The iTVMDI integrated the advantages of microwave and optical remote sensing data to improve the original Temperature–Vegetation–Soil Moisture Dryness Index (TVMDI) model, and was constructed based on the Modified Soil-Adjusted Vegetation Index (MSAVI), land surface temperature (LST), and downscaled soil moisture (SM) as the three-dimensional axes. The global land data assimilation system (GLDAS) SM, meteorological data and surface water were used to evaluate and verify the monitoring results. The results showed that iTVMDI had a higher negative correlation with GLDAS SM (R = −0.73) than TVMDI (R = −0.55). Additionally, the iTVMDI was well correlated with both precipitation and surface water, with mean correlation coefficients (R) of 0.65 and 0.81, respectively. Overall, the accuracy of drought estimation can be significantly improved by using multi-source satellite data to measure the required surface variables, and the iTVMDI is an effective method for monitoring the spatial and temporal variations of drought.