Search

Abstract Lattice oxygen in metal oxides plays an important role in the reaction of diesel oxidation catalysts, but the atomic-level understanding of structural evolution during the catalytic process remains elusive. Here, we develop a Mn2O3/SmMn2O5 catalyst using a non-stoichiometric exsolution method to explore the roles of lattice oxygen in NO oxidation. The enhanced covalency of Mn–O bond and increased electron density at Mn3+ sites, induced by the interface between exsolved Mn2O3 and mullite, lead to the formation of highly active lattice oxygen adjacent to Mn3+ sites. Near-ambient pressure X-ray photoelectron and absorption spectroscopies show that the activated lattice oxygen enables reversible changes in Mn valence states and Mn-O bond covalency during redox cycles, reducing energy barriers for NO oxidation and promoting NO2 desorption via the cooperative Mars-van Krevelen mechanism. Therefore, the Mn2O3/SmMn2O5 exhibits higher NO oxidation activity and better resistance to hydrothermal aging compared to a commercial Pt/Al2O3 catalyst.

Abstract Constructing π‐conjugated polymer structures through covalent bonds dominates the design of organic framework photocatalysts, which significantly depends on the selection of multiple donor‐acceptor building blocks to narrow the optical gap and increase the lifetimes of charge carriers. In this work, self‐bipolarized organic frameworks of single aromatic units are demonstrated as novel broad‐spectrum‐responsive photocatalysts for H2O2 production. The preparation of such photocatalysts is only to fix the aromatic units (such as 1,3,5‐triphenylbenzene) with alkane linkers in 3D space. Self‐bipolarized aromatic units can drive the H2O2 production from H2O and O2 under natural sunlight, wide pH ranges (3.0‐10.0) and natural water sources. Moreover, it can be extended to catalyze the oxidative coupling of amines. Experimental and theoretical investigation demonstrate that such a strategy obeys the mechanism of through‐space π‐conjugation, where the closely face‐to‐face overlapped aromatic rings permit the electron and energy transfer through the large‐area delocalization of the electron cloud under visible light irradiation. This work introduces a novel design concept for the development of organic photocatalysts, which will break the restriction of conventional through‐band π‐conjugation structure and will open a new way in the synthesis of organic photocatalysts.

The primary objective of this research paper is to explore strategies for enhancing the electrical performance of dual active layer thin film transistors (TFTs) utilizing LZTO/ZTO as the bilayer architecture. By systematically adjusting the thickness of the active layers, we achieved significant improvements in the performance of the LZTO/ZTO TFTs. An XPS analysis was performed to elucidate the impact of the varying O2 element distribution ratio within the LZTO/ZTO bilayer thin film on the TFTs performance, which was directly influenced by the modification in the active layer thickness. Furthermore, we utilized atomic force microscopy to analyze the effect of altering the active layer thickness on the surface roughness of the LZTO/ZTO bilayer film and the impact of this roughness on the TFTs electrical performance. Through the optimization of the ZTO active layer thickness, the LZTO/ZTO TFT exhibited an mobility of 10.26 cm2 V−1 s−1 and a switching current ratio of 5.7 × 107, thus highlighting the effectiveness of our approach in enhancing the electrical characteristics of the TFT device.

Evapotranspiration is one of agricultural water management's most significant and impactful hydrologic processes. A new multi-decomposition deep learning-based technique is proposed in this study to forecast weekly reference evapotranspiration (ETo) in western coastal regions of Australia (Redcliffe and Gold Coast). The time-varying filter-based empirical mode decomposition (TVF-EMD) technique was used to first break down the original meteorological variables/signals into intrinsic mode decomposition functions (IMFs), which included maximum and minimum temperature, relative humidity, wind speed, and solar radiation. Using a partial autocorrelation function (PACF), the significant lagged values were then calculated from the decomposed sub-sequences (i.e., IMFs). A novel Extra Tree- Boruta feature selection algorithm was used to extract important features from the decomposed IMFs. Four machine learning approaches, including bidirectional recurrent neural network (Bi-RNN), multi-layer perceptron neural network (MLP), random forest (RF), and extreme gradient boosting (XGBoost), were used to forecast weekly evapotranspiration using the TVF-EMD-based decomposed meteorological data. Different statistical metrics were applied to evaluate the model performances. The results showed that the decomposition of the input data by TVF-EMD significantly improved the accuracy compared with the non-decomposed inputs (single models without decomposition). The findings indicate that the TVF-BiRNN model, as presented, achieved the highest level of accuracy in simulating weekly ET0 at both the Redcliffe and Gold Coast stations (Redcliffe: R=0.9281, RMSE=3.8793 mm/week, MAPE = 9.2010%; Gold Coast: R=0.8717, RMSE=4.1169 mm/week, MAPE = 11.5408%). The novel hybrid modeling technique can potentially improve agricultural water management through its ability to generate more accurate ETo estimates weekly. The proposed methodology exhibits potential applicability to various other environmental and hydrological modeling issues.

One of the weaknesses of water resources management is the neglect of the nonstructural aspects that involve the most important relationships between water resources and socioeconomic parameters. Particularly, socioeconomic evaluation for different regions is crucial before implementing water resources management policies. To address this issue, 14 countries in the world that have continuous increasing trends of using renewable water per capita (RWPC) during 1998–2017 were used for the estimation of eight socioeconomic parameters associated with four key indicators (i.e., economy, demographics, technology communication, and health sanitation) by using four different data-driven methods, including artificial neural networks, support vector machines (SVMs), gene expression programming (GEP), and wavelet-gene expression programming (WGEP). The performances of the models were evaluated by using correlation coefficient (R), root-mean-square error (RMSE), and mean absolute error (MAE). It was found that the WGEP model had the best performance in estimating all parameters. The mathematical expressions for these socioeconomic parameters were explored and their potential to be expanded in different spatial and temporal dimensions was assessed. The derived equations provide a quantitative means for the future estimation of the socioeconomic parameters in the studied countries. HIGHLIGHTS The relationships between water resources and socioeconomic parameters were evaluated.; The mathematical equations of the hydro-socioeconomic parameters were explored.; Different data-driven methods were compared in the estimation of hydro-socioeconomic parameters and the best ones were determined.;

Thallium (Tl) compounds, highly toxic to biology, are usually released into flue gas during fossil/minerals combustion, and further distributed in water and soil. In this work, we fundamentally investigated the capture of gaseous Tl2O by industrial V2O5–WO3/TiO2 catalyst under working condition in Tl-containing flue gas. Experimental and theoretical results indicated that the Tl2O has significant electron-feeding capacity and easily donate electron to unoccupied orbitals of TiO2, leading to dismutation of Ti 2p and inartificial formation of p–n junction on TiO2 surface, which prompted Tl2O selectively interacted with TiO2 in flue gas. Herein, we proposed and verified an effective way to capture gaseous Tl2O, which offered almost the best choice to eliminate Tl emission from flue gas and expanded the function of the TiO2-based catalyst. The formation of p–n junction on commercial V2O5–WO3/TiO2 catalyst under working condition was revealed for the first time, which can be a valuable reference for both heterocatalysis and electro/photocatalysis.

Abstract Lake Urmia, the twentieth largest lake in the world, is the most valuable aquatic ecosystem in Iran. The lake water level has decreased in recent years due to human activities and climate change. Several studies have highlighted the significant roles of climatic and anthropogenic factors on the shrinkage of the lake. Management policies for water resources harvesting must be adopted to adapt to climate change and avoid the consequent problems stemming from the drought affecting Lake Urmia, and rationing must be applied to the upstream water demands. This study analyzes strategies and evaluates their effectiveness in overcoming the Urmia Lake crisis. Specifically, system dynamics analysis was performed for simulating the water volume of Lake Urmia, and the Hadley Centre coupled model was applied to project surface temperature and precipitation for two future periods: 2021–2050 and 2051–2080. Six management scenarios were considered for decreasing the allocation of agricultural water demand corresponding to two options: (1) one-reservoir option (Bukan reservoir only), and (2) six-reservoir option. The net inflow of Urmia Lake was simulated for the two future periods with the IHACRES model and with artificial neural network models under the six management scenarios. The annual average volumes of Lake Urmia would be 30 × 109 and 12 × 109 m3 over the first and second future periods, respectively, without considering the management scenarios. The lake volumes would rise by about 50% and 75% for the first and second periods, respectively under the management scenarios that involve strict protective measures and elimination of the effect of all dams and their reservoirs. Implementing strict measures would increase the annual average lake volume to 21 × 109 m3 in the second period; yet, this volume would be less than the long-term average and strategic volume. The human water use would be completely eliminated under Scenario 6. Nevertheless, Lake Urmia would experience a considerable loss of storage because of drought.

Herein, a novel self-supporting CuO/nickel-cobalt-sulfide (NCS) electrode was designed in a two-step electrodeposition technique followed by a calcination process. Three-dimensional copper foam (CF) was exploited as the current collector and spontaneous source for the in situ preparation of the CuO nanostructures, which ensured sufficient deposition space for the subsequent NCS layer, thus forming abundant electrochemical active sites. Such a hierarchical structure is conducive to providing a smooth path for promoting electronic transmission. Therefore, the optimized CuO/NCS electrode exhibits outstanding energy storage capability with extremely superior specific capacitance (Cs) of 7.08 F cm−2 at 4 mA cm−2 and coulombic efficiency of up to 94.83%, as well as excellent cycling stability with capacitance retention of 83.33% after 5000 cycles. The results presented in this work extend our horizons to fabricate novel hierarchical structured electrodes applied to energy storage devices.

Zeolite-confined metal nanoparticles (NPs) have attracted much attention owing to their superior sintering resistance and broad applications. Here the authors develop a facile in situ mesoporogen-free strategy to design and synthesize palladium NPs enveloped within a single-crystalline zeolite with intra-mesopores.

Using the existing measures for training numerical (non-categorical) prediction models can cause misclassification of droughts. Thus, developing a drought category-based measure is critical. Moreover, the existing fixed drought category thresholds need to be improved. The objective of this research is to develop a category-based scoring support vector regression (CBS-SVR) model based on an improved drought categorization method to overcome misclassification in drought prediction. To derive variable threshold levels for drought categorization, K-means (KM) and Gaussian mixture (GM) clustering are compared with the traditional drought categorization. For drought prediction, CBS-SVR is performed by using the best categorization method. The new drought model was applied to the Red River of the North Basin (RRB) in the USA. In the model training and testing, precipitation, temperature, and actual evapotranspiration were selected as the predictors, and the target variables consisted of multivariate drought indices, as well as bivariate and univariate standardized drought indices. Results indicated that the drought categorization method, variable threshold levels, and the type of drought index were the major factors that influenced the accuracy of drought prediction. The CBS-SVR outperformed the support vector classification and traditional SVR by avoiding overfitting and miscategorization in drought prediction. HIGHLIGHTS Developed a category-based scoring support vector regression (CBS-SVR) method for drought prediction.; Improved drought prediction by avoiding overfitting and miscategorization.; The CBS-SVR outperformed the traditional SVR.; The new method outperformed the support vector classification.;

Abstract From the perspective of the water–energy–food (WEF) security nexus, sustainable water-related infrastructure may hinge on multi-dimensional decision-making, which is subject to some level of uncertainties imposed by internal or external sources such as climate change. It is important to note that the impact of this phenomenon is not solely limited to the changing behavior patterns of hydro-climatic variables since it can also affect the other pillars of the WEF nexus both directly and indirectly. Failing to address these issues can be costly, especially for those projects with long-lasting economic lifetimes such as hydropower systems. Ideally, a robust plan can tolerate these projected changes in climatic behavior and their associated impacts on other sectors, while maintaining an acceptable performance concerning environmental, socio-economic, and technical factors. This study, thus, aims to develop a robust multiple-objective decision-support framework to address these concerns. In principle, while this framework is sensitive to the uncertainties associated with the climate change projections, it can account for the intricacies that are commonly associated with the WEF security network. To demonstrate the applicability of this new framework, the Karkheh River basin in Iran was selected as a case study due to its critical role in ensuring water, energy, and food security of the region. In addition to the status quo, a series of climate change projections (i.e., RCP 2.6, RCP 4.5, and RCP 8.5) were integrated into the proposed decision support framework as well. Resultantly, the mega decision matrix for this problem was composed of 56 evaluation criteria and 27 feasible alternatives. A TOPSIS/Entropy method was used to select the most robust renovation plan for a hydropower system in the basin by creating a robust and objective weighting mechanism to quantify the role of each sector in the decision-making process. Accordingly, in this case, the energy, food, and environment sectors are objectively more involved in the decision-making process. The results revealed that the role of the social aspect is practically negligible. The results also unveiled that while increasing the power plant capacity or the plant factor would be, seemingly, in favor of the energy sector, if all relevant factors are to be considered, the overall performance of the system might resultantly become sub-optimal, jeopardizing the security of other aspects of the water–energy–food nexus.

Abstract Simulation models are often affected by uncertainties that impress the modeling results. One of the important types of uncertainties is associated with the model input data. The main objective of this study is to investigate the uncertainties of inputs of the Heat-Flux (HFLUX) model. To do so, the Shuffled Complex Evolution Metropolis Uncertainty Algorithm (SCEM-UA), a Monte Carlo Markov Chain (MCMC) based method, is employed for the first time to assess the uncertainties of model inputs in riverine water temperature simulations. The performance of the SCEM-UA algorithm is further evaluated. In the application, the histograms of the selected inputs of the HFLUX model including the stream width, stream depth, percentage of shade, and streamflow were created and their uncertainties were analyzed. Comparison of the observed data and the simulations demonstrated the capability of the SCEM-UA algorithm in the assessment of the uncertainties associated with the model input data (the maximum relative error was 15%).

Abstract Population growth, urbanization, and industrial development have significantly increased water demands in many countries, raising the concerns about water resources sustainability to meet the needs of humans and the environment. Furthermore, the economy-oriented allocation of water resources has caused many socio-environmental problems. The main goal of this study is to develop a system dynamics modeling framework that integrates economic, social, and environmental dimensions for the decision of water resources allocation. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used to rank modeling scenarios and identify the best strategy for water allocation. In the application to East Azerbaijan province of Iran, six industry groups (including chemical, food and beverage, non-metal, machinery and equipment, metal, and textile), thirteen water allocation scenarios, and five criteria (including profit index, employment index, return of surface water, groundwater sustainability index, and total allocated water) were considered. The TOPSIS results showed that in the best scenario most water was allocated to the non-metal industry with a relative distance of 0.63 to the ideal solution. On the other hand, the current water allocation scenario ranked seventh, indicating that significant improvements are required to take into account the social, economic, and environmental factors for optimal reallocation of water resources among different industry users.

Agent-based modeling (ABM) has been employed to understand and capture the complexity of the coupled human-nature processes in water resource systems. One of the challenges is to model human decisions in the coupled human and natural systems. Hydraulic fracturing water uses were distributed through a depot-based water allocation system in the Bakken region of western North Dakota, United States. In this study, a spatially explicit ABM was developed to simulate this unique water allocation system. In the ABM, institution theory was used to model the State’s regulatory policies and procedures, while evolutionary programming was employed to allow the water-depot owners (or agents) to select appropriate strategies when applying for water permits. Cognitive maps simulated the water-depot agents’ ability and willingness to compete for more water sales. All agents had their influence boundaries that restricted their competitive behavior toward their neighbors, but not for non-neighboring agents. The decision-making process was constructed and parameterized with both quantitative and qualitative information. The ABM was calibrated against real-world water-use data, and the calibration results showed that the spatial ABM performed well in simulating the total number of water depots as well as the locations and water uses of water depots at the county level. By linking institution theory, evolutionary programming, and cognitive maps, our study exhibited a new exploration of modeling the highly complex dynamics of the decision-making process involved in coupled human-nature water resource systems.

In semi-distributed hydrologic models, it is difficult to account for the impacts of wetlands on hydrologic processes, as they are based on lumped, subbasin-scale wetland concepts. It is a challenge to incorporate the influences of individual small wetlands into watershed-scale models by using lumped parameterization. The objective of this study was to improve watershed-scale hydrologic modeling by taking into account real wetland features during the wetland parameterization. To achieve this objective, a joint modeling framework was proposed to couple a surface delineation algorithm with a semi-distributed hydrologic model and then applied to the Upper Turtle River watershed in North Dakota, USA. The delineation algorithm identified the topographic properties of wetlands, which were further utilized for wetland parameterization. A nonlinear area–storage relationship was determined and used in the estimation of the wetland-related parameters. The results demonstrated that the new joint modeling approach effectively avoided misestimating the wetland-related parameters by accounting for real topographic characteristics (e.g., storage, ponding area, and contributing area) of identified wetlands and their influences, and provided improved modeling of the hydrologic processes in such a wetland-dominated watershed.

The SCS curve number (SCS-CN) method has gained widespread popularity for simulating rainfall excess in various rainfall events due to its simplicity and practicality. However, it possesses inherent structural issues that limit its performance in accurately simulating rainfall excess and infiltration over time. The objective of this study was to develop a modified CN method with temporally varying rainfall intensity (MCN-TVR) by combining a soil moisture accounting (SMA) based SCS-CN method with the SMA method in the Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS). In the MCN-TVR, the SMA-based SCS-CN method is utilized to simulate the cumulative rainfall excess and infiltration, while the SMA method in the HEC-HMS serves as an infiltration control function. A key advantage of the MCN-TVR is that it eliminates the need for additional input parameters by inherently linking the parameters in the two SMA-based methods. Sixteen hypothetical 24 h SCS Type II rainfall events with different soil types and five real rainfall events for the Rush River Watershed in North Dakota were used to assess the performances of the MCN-TVR method and the SMA-based SCS-CN method. In the hypothetical simulations, the rainfall excess simulated by the SMA-based SCS-CN and MCN-TVR models was compared to that simulated by a Green–Ampt model. Discrepancies were observed between the rainfall excess simulated by the SMA-based SCS-CN and Green–Ampt models, especially for coarse soils under relatively light rainfall. However, the MCN-TVR model, incorporating an infiltration control function, demonstrated its improved performance closer to the Green–Ampt model. For all the hypothetical events, the Nash–Sutcliffe efficiency (NSE) coefficient of the rainfall excess simulated by the MCN-TVR method compared to the Green–Ampt model was greater than 0.99, while the root mean standard deviation ratio (RSR) was less than 0.03. In the real applications, the SMA-based SCS-CN model failed to provide acceptable simulation of the direct runoff for rainfall events with durations of less than the time of concentration. In contrast, the MCN-TVR model successfully simulated the direct runoff for all the events with NSE values ranging from 0.65 to 0.91 and RSR values from 0.31 to 0.56.

Activation of O2 is a critical step in heterogeneous catalytic oxidation. Here, the authors adopt the concept of increased electron donors induced by nitrogen vacancy to develop an efficient strategy for preparing highly active and stable catalysts for molecular O2 activation.

This study aims to conduct a thorough investigation to compare the abilities of quantile mapping (QM) techniques as a bias correction method for the raw outputs from general circulation model (GCM)/regional climate model (RCM) combinations. The Karkheh River basin in Iran was selected as a case study, due to its diverse topographic features, to test the performances of the bias correction methods under different conditions. The outputs of two GCM/RCM combinations (ICHEC and NOAA-ESM) were acquired from the coordinated regional climate downscaling experiment (CORDEX) dataset for this study. The results indicated that the performances of the QMs varied, depending on the transformation functions, parameter sets, and topographic conditions. In some cases, the QMs' adjustments even made the GCM/RCM combinations' raw outputs worse. The result of this study suggested that apart from DIST, PTF:scale, and SSPLIN, the rest of the considered QM methods can provide relatively improved results for both rainfall and temperature variables. It should be noted that, according to the results obtained from the diverse topographic conditions of the sub-basins, the empirical quantiles (QUANT) and robust empirical quantiles (RQUANT) methods proved to be excellent options to correct the bias of rainfall data, while all bias correction methods, with the notable exceptions of performed PTF:scale and SSPLIN, performed relatively well for the temperature variable. HIGHLIGHTS Quantile mapping (QM) techniques are among the most important and popular bias correction methods. This study aims to provide a comprehensive comparison to identify the potential strengths and weaknesses of these methods in coping with hydro-climatic variables.; This study aims to shed light on the general abilities of QM methods in correcting the bias of both temperature and rainfall variables, both of which can have direct and indirect impacts on water resources. Furthermore, this study explores these capabilities in diverse topographic conditions. Coping with such topographic conditions is a known challenge for both general and regional circulation models.; This study explores the projections of the coordinated regional climate downscaling experiment (CORDEX) dataset. The main idea behind the CORDEX project is to coordinate the results of local climatic studies. As such, the findings of this study can help expand the capabilities and applicability of this project.; The results of this study revealed that some QM methods could, in fact, worsen the accuracy of general and regional circulation models, which highlights the importance of selecting a suitable bias correction method.; According to the results obtained from the diverse topographic conditions of the sub-basins, the empirical quantiles (QUANT) and robust empirical quantiles (RQUANT) methods proved to be excellent options to correct the bias of rainfall data, while all bias correction methods, with the notable exceptions of performed PTF:scale and SSPLIN, performed relatively well for the temperature variable.;

Herein, a scalable electrodeposition strategy is proposed to achieve hierarchical CuO/nickel–cobalt–sulfide (NCS) electrodes using two-step potentiostatic deposition followed by high-temperature calcination. The introduction of CuO provides support for the further deposition of NSC to ensure a high load of active electrode materials, thus generating more abundant active electrochemical sites. Meanwhile, dense deposited NSC nanosheets are connected to each other to form many chambers. Such a hierarchical electrode prompts a smooth and orderly transmission channel for electron transport, and reserves space for possible volume expansion during the electrochemical test process. As a result, the CuO/NCS electrode exhibits superior specific capacitance (Cs) of 4.26 F cm−2 at 20 mA cm−2 and remarkable coulombic efficiency of 96.37%. Furthermore, the cycle stability of the CuO/NCS electrode remains at 83.05% within 5000 cycles. The multistep electrodeposition strategy provides a basis and reference for the rational design of hierarchical electrodes to be applied in the field of energy storage.

The optimal operation of reservoirs is known as a complex issue in water resources management, which requires consideration of numerous variables (such as downstream water demand and power generation). For this optimization, researchers have used evolutionary and meta-heuristic algorithms, which are generally inspired by nature. These algorithms have been developed to achieve optimal/near-optimal solutions by a smaller number of function evaluations with less calculation time. In this research, the flower pollination algorithm (FPA) was used to optimize: (1) Aidoghmoush single-reservoir system operation for agricultural water supply, (2) Bazoft single-reservoir system operation for hydropower generation, (3) multi-reservoir system operation of Karun 5, Karun 4, and Bazoft, and (4) Bazoft single-reservoir system for rule curve extraction. To demonstrate the effectiveness of the FPA, it was first applied to solve the mathematical test functions, and then used to determine optimal operations of the reservoir systems with the purposes of downstream water supply and hydropower generation. In addition, the FPA was compared with the particle swarm optimization (PSO) algorithm and the non-linear programming (NLP) method. The results for the Aidoghmoush single-reservoir system showed that the best FPA solution was similar to the NLP solution, while the best PSO solution was about 0.2% different from the NLP solution. The best values of the objective function of the PSO were approximately 3.5 times, 28%, and 43% worse than those of the FPA for the Bazoft single-reservoir system for hydropower generation, the multi-reservoir system, and the Bazoft single-reservoir system for rule curve extraction, respectively. The FPA outperformed the PSO in finding the optimal solutions. Overall, FPA is one of the new evolutionary algorithms, which is capable of determining better (closer to the ideal solution) objective functions, decreasing the calculation time, simplifying the problem, and providing better solutions for decision makers.

Bimetallic materials are of great interest in scientific research and applications due to their unique chemical and physical properties. Here, we report a solid strategy to prepare AuNi bimetallic nanoparticles in large spherical cages of ordered mesoporous silica with uniform size distribution (AuxNi/mSiO2). The surface chemical composition and catalytic performance of BMNPs (Bimetallic Nanoparticles) are precisely varied via confined inter-particle hetero-atom migration. The AuxNi/mSiO2 catalyst displayed greatly improved catalytic performance and excellent stability in gas-phase oxidation of benzyl alcohol. Moreover, the strategy has been successfully extended to the synthesis of PtyNi/mSiO2, which performed higher activity and selectivity compared to the monometallic Pt catalyst in CO oxidation. Keywords: Bimetallic nanocatalysts, Au/Pt-Ni, Synergistic effect, Alcohol oxidation, Selective oxidation of CO

Herein, nickel–cobalt sulfide (NCS) nanoflakes covering the surface of Cu(OH)2 nanorods were achieved by a facile two-step electrodeposition strategy. The effect of CH4N2S concentration on formation mechanism and electrochemical behavior is investigated and optimized. Thanks to the synergistic effect of the selected composite components, the Cu(OH)2/NCS composite electrode can deliver a high areal specific capacitance (Cs) of 7.80 F cm−2 at 2 mA cm−2 and sustain 5.74 F cm−2 at 40 mA cm−2. In addition, coulombic efficiency was up to 84.30% and cyclic stability remained 82.93% within 5000 cycles at 40 mA cm−2. This innovative work provides an effective strategy for the design and construction of hierarchical composite electrodes for the development of energy storage devices.

A high-performance ZnSnO (ZTO) thin-film transistor (TFT) was fabricated, with ZTO deposited by rf magnetron sputtering. XPS was used to analyze and study the effects of different annealing temperatures on the element composition and valence state of ZTO films. Then, the influence mechanism of annealing treatment on the electrical properties of ZTO thin films was analyzed. The results show that, with an increase in annealing temperature, the amount of metal bonding with oxygen increases first and then decreases, while the oxygen vacancy decreases first and then increases. Further analysis on the ratio of Sn2+ is presented. Electrical results show that the TFT annealed at 600 °C exhibits the best performance. It exhibits high saturation mobilities (μSAT) up to 12.64 cm2V−1s−1, a threshold voltage (VTH) of −6.61 V, a large on/off current ratio (Ion/Ioff) of 1.87 × 109, and an excellent subthreshold swing (SS) of 0.79 V/Decade.

Surface depressions are one of the important impact factors of hydrologic processes and catchment responses. However, in many hydrologic models, the influence of depressions is often simulated in a lumped manner, which results in the insufficient characterization of the filling–spilling–merging–splitting dynamics of depressions and the threshold behavior of the overland flow. The objective of the research reported in this paper is to improve the simulation of depression-influenced hydrologic processes by capturing the threshold control of depressions. To achieve this objective, a Depression-oriented Soil and Water Assessment Tool (SWAT-D) is developed. Specifically, the intrinsic changing patterns of contributing area and depression storage are first determined and further incorporated into the SWAT to simulate the filling–spilling of depressions and depression-influenced overland flow dynamics. The SWAT-D was applied to a depression-dominated watershed in the Prairie Pothole Region to evaluate its performance and capability. The simulated and observed hydrographs at the watershed outlet showed good agreement, with only a 7% deviation between the simulated and observed volumes of discharges in 2004. The NSE values for the simulated monthly average discharges during calibration and validation periods were 0.78 and 0.71, respectively, indicating the ability of the SWAT-D in reproducing the depression-influenced catchment responses. In addition, the SWAT-D was compared with other depression-oriented modeling techniques (i.e., the lumped depression approach and probability distribution models), and the comparisons emphasized the improvement of the SWAT-D and the importance of the research reported in this paper.

A Cu(OH)2/Ni3S2 composite has been designed and in situ constructed on Cu foam substrate by facile two-step electrodeposition. Cu(OH)2 is achieved on Cu foam by galvanostatic electrodeposition, and the subsequent coating of Ni3S2 is realized by cyclic voltammetric (CV) electrodeposition. The introduction of Cu(OH)2 provides skeleton support and a large specific surface area for the Ni3S2 electrodeposition. Benefiting from the selection of different components and preparation technology, the Cu(OH)2/Ni3S2 composite exhibits enhanced electrochemical properties with a high specific capacitance of 4.85 F cm−2 at 2 mA cm−2 and long-term cyclic stability at 80.84% (4000 cycles).

CE-QUAL-W2 is widely used for simulating hydrodynamics and water quality of the aquatic environments. Currently, the model calibration is mainly based on trial and error, and therefore it is subject to the knowledge and experience of users. The Particle Swarm Optimization (PSO) algorithm has been tested for automatic calibration of CE-QUAL-W2, but it has an issue of prematurely converging to a local optimum. In this study, we proposed an Improved Global-Best Harmony Search (IGHS) algorithm to automatically calibrate the CE-QUAL-W2 model to overcome these shortcomings. We tested the performance of the IGHS calibration method by simulating water temperature of Devils Lake, North Dakota, which agreed with field observations with R2 = 0.98, and RMSE = 1.23 and 0.77 °C for calibration (2008–2011) and validation (2011–2016) periods, respectively. The same comparison, but with the PSO-calibrated CE-QUAL-W2 model, produced R2 = 0.98 and Root Mean Squared Error (RMSE) = 1.33 and 0.91 °C. Between the two calibration methods, the CE-QUAL-W2 model calibrated by the IGHS method could lower the RMSE in water temperature simulation by approximately 7–15%.

The use of cheap and porous substrates to support visible light photocatalyst is an effective solution to avoid the agglomeration of catalyst. Herein, the Bi _2 MoO _6 @Diatomite composite has been prepared through hydrothermal reaction with Bi(NO _3 ) _3 and Na _2 MoO _4 as precursors and diatomite as substrate. XRD, SEM, TEM, XPS, and DRS are applied to characterize these composite photocatalysts. The photocatalytic performance of the obtained composites were studied through the Cr(VI) reduction and tetracycline hydrochloride (TC) degradation under visible light irradiation ( λ > 400 nm). The photocatalytic experimental results display that the sample with 6% diatomite mass faction (Bi _2 MoO _6 @6%DE) has the highest catalytic performance in all materials. In the Cr(VI) solution, the Bi _2 MoO _6 @6%DE can reduce 90.1% of Cr(VI) in 60 min, while Bi _2 MoO _6 can only reduce 71.7% of pollutants in the same time. In the TC solution, the Bi _2 MoO _6 @6%DE can reduce 74.7% of TC in 120 min, while Bi _2 MoO _6 can only reduce 30.5% of pollutants in the same time. The photocatalytic mechanism was further investigated through the test of active radical capture. The main active radicals in the reduction of Cr(VI) is photogenerated electrons. The ·HO ^− , ·O _2 ^− and holes (h ^+ ) are active free radicals in the removal process of TC, and photogenerated holes (h ^+ ) have the greatest influence. This composite has good photocatalytic activity and recyclability, so it has great application potential in waste water treatment.

Predicting surface runoff and flooding in seasonally frozen areas such as the Red River of the North Basin (RRB) in USA is a challenging task. It depends on the knowledge of the complex process of infiltration in frozen soil, such as phase changes of water, ice content and distribution in the infiltration zone (the top 0−30 cm of the soil profile), soil pore size distribution, soil temperature and freeze−thaw cycles. In this study, the infiltration rates into frozen soil (Colvin silty clay loam according to the United States Department of Agriculture (USDA) Classification, and Chernozem according to Food and Agriculture Organization of the United Nations (FAO) international soil Classification) were measured at three different initial water contents: permanent wilting point (PWP), θpwp; field capacity (FC), θfc; and between FC and PWP, θmid. Laboratory infiltration experiments were conducted using a Cornell sprinkle infiltrometer with three replications for each initial water content. Volumetric soil water content (θv) and soil temperature at three depths were also continuously monitored using sensors. The average infiltration rates were 0.66, 0.38, and 0.59 cm/min for three initial water contents (θpwp, θmid, and θfc, respectively). Initial infiltration into frozen soil occurred quickly in the soil with θpwp because the soil was dry. Melted ice water contributed to the total soil water content over time, so it made the initial infiltration comparatively slower in the soil with θmid. Initial infiltration was also slower in the soil with θfc because the wet soil had very small pore space, so the soil rapidly reached its saturation after the infiltration started. The Horton infiltration equation was fitted with the observed infiltration rates for the soils with three initial water contents, and the goodness of fit was evaluated by using the coefficient of determination (R2) and the root-mean-square error (RMSE). The final infiltration rates from the fitted Horton equations were 0.060, 0.010, and 0.027 cm/min for the initial water contents (θpwp, θmid, and θfc, respectively). The soil water content along the soil profile changed with the amount of infiltrating water over time. However, the initial soil water content and melt water from ice resulting from soil temperature rise regulated the change in soil water content. The amount of ice melt water contribution to soil water content change varied among the soils with different initial water contents (θpwp, θmid, and θfc, respectively). The θv changed gradually in the θpwp soil, rapidly at 0 °C in the θmid soil, and less in the θfc soil. The change in pore distribution due to freeze−thaw cycles and soil packing altered the soil hydraulic properties and the infiltration into the soil. This study can provide critical information for flood forecasting model and subsurface drainage design in the RRB.

We prepared transparent conducting composite electrodes composed of silver nanowires (Ag NWs) and reduced graphene oxide (r-GO). We present a simple approach to welding the cross-positions of the Ag NWs by applying pressure at a relatively low temperature (100°C). We examined the Ag NWs/r-GO composite films in terms of their transmission, conductivity, and stability. The plasmonic features of the Ag NWs were used to assist the ultraviolet (UV) light-induced reduction of the GO coating. The r-GO coatings used to form Ag NWs/r-GO composite structures increased the conductivity of the film by providing more efficient electron conductive pathways. The G/D intensity ratios of the GO and r-GO produced by the UV light-induced method without and with Ag NWs were 0.95, 1.01, and 1.04, respectively. The lowest sheet resistance of the composite films was 7 ohm/sq with approximately 82% transparency in the visible spectrum region. No degradation of the films was observed after 2 months. This excellent environmental stability might facilitate applications of Ag NWs/r-GO composite films in optoelectronic devices.

DEM-based topographic characterization and quantification of surface depression storage are critical to hydrologic and environmental modeling. Mixed conclusions have been obtained from previous studies on the relationship between maximum depression storage (MDS) and DEM grid spacing, which is affected by different factors, such as topographic characteristics, surface delineation methods and DEM interpolation/aggregation methods. The objective of this study was to evaluate the effects of DEM resolution on topographic characterization with the consideration of these three factors. Twenty-three topographic surfaces (including ideal surfaces, laboratory-scale soil surfaces and watershed-scale land surfaces) were selected, and five software packages, ArcHydro, PCRaster, HEC-GeoHMS, TauDEM and PD (puddle delineation), were used for surface delineation. Our results indicated that MDS, maximum ponding area (MPA) and the number of puddles (NP) decreased with increasing grid spacing for most smoother surfaces due to the loss of topographic detail. For most rough surfaces (e.g., mountain-type surfaces with significant variations in surface elevations), however, the changing patterns of MDS and MPA varied with an increase in grid spacing mainly due to the unreal “artificial depressions/puddles” generated during the interpolation/aggregation process. This study emphasizes the importance of topographic characteristics, DEM resolution and surface delineation methods.

Ultrafine Pd nanocrystals were grown on the cobalt phosphide (Co2P) decorated Vulcan XC-72 carbon (C/Co2P), which is realized by first implementing the corresponding metal precursor and then the further chemical reduction process. The as-synthesized C/Co2P/Pd composite was further constructed to form a gas permeable electrode. This electrode can be applied for formaldehyde (HCHO) detection. The results demonstrate that the Co2P nanocrystal can significantly improve the sensing performance of the C/Co2P/Pd electrode for catalytic oxidation of HCHO, which is considered to be attributed to the effective electron transfer from Co2P to Pd in the C/Co2P/Pd composites. Furthermore, the assembled C/Co2P/Pd sensor exhibits high sensitivity of 617 nA/ppm and good selectivity toward various interfering gases such as NO2, NO, SO2, CO2, and CO. It also shows the excellent linear response that the correlation coefficient is 0.994 in the concentration range of 1–10 ppm. Therefore, the proposed cost-effective C/Co2P/Pd nanocomposite, which owns advantages such as high activity and good stability, has the potential to be applied as an effective electrocatalyst for amperometric HCHO detection.

Enhancing the catalytic activity of supported metal nanoparticle is a great demand but extremely challenging to make. We reported a simple strategy for enhancing the activities by employing the polyvinylpyrrolidone (PVP) additive, where a series of supported Au nanoparticle catalysts including Au/C, Au/BN, Au/TiO2, and Au/SBA-15 exhibited significantly higher activities in the oxidation of various alcohols and carbon monoxide by molecular oxygen after adding PVP to the reaction system. The XPS study indicates that PVP could electronically interact with Au to form high active Au sites for molecular oxygen, thus causing improved activities for the various oxidation reactions.

Lysimeter experiments were conducted under greenhouse conditions to investigate canola (Brassica napus L.) plant water use, growth, and yield parameters for three different water table depths of 30, 60, and 90 cm. Additionally, control experiments were conducted, and only irrigation was applied to these lysimeters without water table limitations. The canola plant’s tolerance level to shallow groundwater was determined. Results showed that groundwater contributions to canola plant for the treatments at 30, 60, and 90 cm water table depths were 97%, 71%, and 68%, respectively, while the average grain yields of canola were 4.5, 5.3, and 6.3 gr, respectively. These results demonstrate that a 90 cm water table depth is the optimum depth for canola plants to produce a high yield with the least amount of water utilization.

The Ag/Ni(OH)2 composite electrode has been designed and in situ constructed on a copper substrate by combining electroless deposition technology with electrodeposition. The products can be directly used as a high performance binder free electrode. The synergistic effect between the Ag nanocubes (AgNCs) as backbones and the deposited Ni(OH)2 as the shell can significantly improve the electrochemical properties of the composite electrode. Moreover, this in situ growth strategy forms a strong bonding force of active materials to the substrate, which can improve the cycling performance and lower the equivalent series resistance. The Ag/Ni(OH)2 composite electrode exhibits enhanced electrochemical properties with a high specific capacitance of 3.704 F cm−2, coulombic efficiency of 88.3% and long-term cyclic stability.

In this study the effects of 1-Octadecanethiol (ODT, 1-CH3 [CH2]17SH) passivation on GaAs (100) surface and GaAs/Al2O3 MOS capacitors are investigated. The results measured by X-ray photoelectric spectroscopy (XPS), Raman spectroscopy and scan electron microscopy (SEM) show that the ODT passivation can obviously suppress the formation of As-O bonds and Ga-O bonds on the GaAs surface and produce good surface morphology at the same time, and especially provide better protection against environmental degradation for at least 24 h. The passivation time is optimized by photoluminescence (PL), and the maximum enhancement of PL intensity was 116%. Finally, electrical property of a lower leakage current was measured using the metal-oxide-semiconductor capacitor (MOSCAP) method. The results confirm the effectiveness of ODT passivation on GaAs (100) surface.

Hierarchical Ag@Ni(OH)2 nanospheres were achieved directly on copper foam substrate through a convenient two-step process. Ag nanoflowers were formed on copper substrate by galvanic replacement technology between AgNO3 and copper foam followed by electrodeposition of a layer of Ni(OH)2. Ag nanostructures as cores not only dominated the final morphology of the composites, but also improved the electrical conductivity, increased the specific surface area of the active electrode material, and even directly participated in the electrochemical reactions. The resulted Ag@Ni(OH)2 nanospheres could be directly used as high-performance binder-free electrodes and exhibited enhanced electrochemical performance with a high specific capacitance of 1.864 F cm−2 and long cycling lifespans of 90.43% capacity retaining after 3000 cycles.

The mesoporous silica SBA-15 and mesoporous K2CO3-SiO2 composite material were synthesized. Characterization of microstructure and morphology of materials indicated that the composite material had saved the porous framework of mesoporous silica SBA-15. Humidity sensing properties of different inverse proportion K2CO3-SiO2 composite material were studied and we found that the sample with 0.16 g/g K2CO3 exhibited excellent linearity in the wide humidity range. The complex impedance changed five orders of magnitude from 11% RH to 95% RH. The rapid response and recovery time were 10 s and 38 s, respectively. Finally a feasible ion transfer mechanism was brought forward to explain the sensing mechanism.

A series of Au catalysts with different sizes were synthesized and employed on amine group functionalized ordered mesoporous silica solid supports as catalyst for the aerobic oxidation of various alcohols. The mesoporous silica of MCM-41 supported Au nanoparticles (Au-1) exhibited the smallest particle size at ~1.8 nm with superior catalytic activities owing to the confinement effect of the mesoporous channels. Au-1 catalyst is also very stable and reusable under aerobic condition. Therefore, this presented work would obviously provide us a platform for synthesizing more size-controlled metal catalysts to improve the catalytic performances.

Surface microrelief affects overland flow generation and the related hydrologic processes. However, such influences vary depending on other factors such as rainfall characteristics, soil properties, and initial soil moisture conditions. Thus, in-depth research is needed to better understand and evaluate the combined effects of these factors on overland flow dynamics. The objective of this experimental study was to examine how surface microrelief, in conjunction with the factors of rainfall, soil, and initial moisture conditions, impacts overland flow generation and runoff processes in both laboratory and field settings. A series of overland flow experiments were conducted for rough and smooth surfaces that represented distinct microtopographic characteristics and the experimental data were analyzed and compared. Across different soil types and initial moisture conditions, both laboratory and field experiments demonstrated that a rough soil surface experienced a delayed initiation of runoff and featured a stepwise threshold flow pattern due to the microrelief-controlled puddle filling-spilling-merging dynamics. It was found from the field experiments that a smooth plot surface was more responsive to rainfall variations especially during an initial rainfall event. However, enhanced capability of overland flow generation and faster puddle connectivity of a rough field plot occurred during the subsequent rain events.

Correction of digital elevation models (DEMs) for flat areas is a critical process for hydrological analyses and modeling, such as the determination of flow directions and accumulations, and the delineation of drainage networks and sub-basins. In this study, a new algorithm is proposed for flat correction/removal. It uses the puddle delineation (PD) program to identify depressions (including their centers and overflow/spilling thresholds), compute topographic characteristics, and further fill the depressions. Three different levels of elevation increments are used for flat correction. The first and second level of increments create flows toward the thresholds and centers of the filled depressions or flats, while the third level of small random increments is introduced to cope with multiple threshold conditions. A set of artificial surfaces and two real-world landscapes were selected to test the new algorithm. The results showed that the proposed method was not limited by the shapes, the number of thresholds, and the surrounding topographic conditions of flat areas. Compared with the traditional methods, the new algorithm simplified the flat correction procedure and reduced the final elevation increments by 5.71–33.33%. This can be used to effectively remove/correct topographic flats and create flat-free DEMs.

Previous studies of land degradation, topsoil erosion, and hydrologic alteration typically focus on these subjects individually, missing important interrelationships among these important aspects of the Earth’s system. However, an understanding of water–soil–vegetation dynamic interactions is needed to develop practical and effective solutions to sustain the globe’s eco-environment and grassland agriculture, which depends on grasses, legumes, and other fodder or soil-building crops. This special issue is intended to be a platform for a discussion of the relevant scientific findings based on experimental and/or modeling studies. Its 12 peer-reviewed articles present data, novel analysis/modeling approaches, and convincing results of water–soil–vegetation interactions under historical and future climates. Two of the articles examine how lake/pond water quality is related to human activity and climate. Overall, these articles can serve as important references for future studies to further advance our understanding of how water, soil, and vegetation interactively affect the health and productivity of the Earth’s ecosystem.

Modeling hydrologic processes for depression-dominated areas such as the North American Prairie Pothole Region is complex and reliant on a clear understanding of dynamic filling-spilling-merging-splitting processes of numerous depressions over the surface. Puddles are spatially distributed over a watershed and their sizes, storages, and interactions vary over time. However, most hydrologic models fail to account for these dynamic processes. Like other traditional methods, depressions are filled as a required preprocessing step in the Soil and Water Assessment Tool (SWAT). The objective of this study was to facilitate hydrologic modeling for depression-dominated areas by coupling SWAT with a Puddle Delineation (PD) algorithm. In the coupled PD-SWAT model, the PD algorithm was utilized to quantify topographic details, including the characteristics, distribution, and hierarchical relationships of depressions, which were incorporated into SWAT at the hydrologic response unit (HRU) scale. The new PD-SWAT model was tested for a large watershed in North Dakota under real precipitation events. In addition, hydrologic modeling of a small watershed was conducted under two extreme high and low synthetic precipitation conditions. In particular, the PD-SWAT was compared against the regular SWAT based on depressionless DEMs. The impact of depressions on the hydrologic modeling of the large and small watersheds was evaluated. The simulation results for the large watershed indicated that SWAT systematically overestimated the outlet discharge, which can be attributed to the failure to account for the hydrologic effects of depressions. It was found from the PD-SWAT modeling results that at the HRU scale surface runoff initiation was significantly delayed due to the threshold control of depressions. Under the high precipitation scenario, depressions increased the surface runoff peak. However, the low precipitation scenario could not fully fill depressions to reach the overflow thresholds in the selected sub-basins. These results suggest the importance of depressions as gatekeepers in watershed modeling.

The effect of microtopography on soil-water movement is a topic of interest for a range of disciplines, with experimental studies investigating the relationship between the two lacking. Laboratory experiments were conducted by simulating rainfall across packed soil surfaces to investigate the effect of microtopography on wetting front movement within experimental soil profiles. In small soil box experiments, the observed wetting fronts for soil profiles showed considerably deeper movement beneath a smooth surface than depressions for 12–60 min rainfalls. For large soil box experiments, the wetting front reached moisture sensors installed at 5 and 10 cm depths and corresponding to various rough and smooth surface features at significantly different times, with movement being most rapid beneath the smooth surface. Wetting front movement was “quicker” beneath surface peaks than depressions for the rough surface as attributed to 2D/3D unsaturated flow. This study provides valuable experimentally based insight into the effect of microtopography on soil-water movement.