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The effect of the combined application of nano-hydroxyapatite (NHAP) or nano-carbon black (NCB) on the phytoextraction of Pb by ryegrass was investigated as an enhanced remediation technique for soils by field-scale experiment. After the addition of 0.2% NHAP or NCB to the soil, temporal variation of the uptake of Pb in aboveground parts and roots were observed. Ryegrass shoot concentrations of Pb were lower with nano-materials application than without nano-materials for the first month. However, the shoot concentrations of Pb were significantly increased with nano-materials application, in particular NHAP groups. The ryegrass root concentrations of Pb were lower with nano-materials application for the first month. These results indicated that nano-materials had significant effects on stabilization of lead, especially at the beginning of the experiment. Along with the experimental proceeding, phytotoxicity was alleviated after the incorporation of nano-materials. The ryegrass biomass was significantly higher with nano-materials application. Consequently, the Pb phytoextraction potential of ryegrass significantly increased with nano-materials application compared to the gounps without nano-materials application. The total removal rates of soil Pb were higher after combined application of NHAP than NCB. NHAP is more suitable than NCB for in-situ remediation of Pb-contaminated soils. The ryegrass translocation factor exhibited a marked increase with time. It was thought that the major role of NHP and NBA might be to alleviate the Pb phytotoxicity and increase biomass of plants.

AbstractElevated carbon-dioxide concentration [eCO2] is a key climate change factor affecting plant growth and yield. Conventionally, crop modeling work has evaluated the effect of climatic parameters on crop growth, without considering CO2. It is conjectured that a novel multimodal ensemble approach may improve the accuracy of modelled responses to eCO2. To demonstrate the applicability of a multimodel ensemble of crop models to simulation of eCO2, APSIM, CropSyst, DSSAT, EPIC and STICS were calibrated to observed data for crop phenology, biomass and yield. Significant variability in simulated biomass production was shown among the models particularly at dryland sites (44%) compared to the irrigated site (22%). Increased yield was observed for all models with the highest average yield at dryland site by EPIC (49%) and lowest under irrigated conditions (17%) by APSIM and CropSyst. For the ensemble, maximum yield was 45% for the dryland site and a minimum 22% at the irrigated site. We concluded from our study that process-based crop models have variability in the simulation of crop response to [eCO2] with greater difference under water-stressed conditions. We recommend the use of ensembles to improve accuracy in modeled responses to [eCO2].

Plug-in hybrid electric vehicles (PHEVs) have a larger battery and can replace a certain amount of conventional fossil fuels with grid electricity, which differs from the traditional hybrid electric vehicles (HEVs). The application of the onboard electrical energy significantly influences the energy utilization efficiency and thus impacts the fuel economy. In this paper, the basic PHEV operation modes are defined as pure electric driving (PED), hybrid driving charge depleting (HDCD) and hybrid driving charge sustaining (HDCS) based on the battery state of charge (SoC) profile. For a plug-in hybrid electric bus (PHEB), three different energy management strategies, which are combined with two or three of the basic operation modes, are put forward and comparatively examined based on simulation models. If some trip information can be approximately known in advance such as the trip distance and the mean power demand, the PED + HDCD + HDCS strategy comprised optimally of the PED mode, the HDCD mode and the HDCS mode would be the best energy management strategy.

Schisandra Sphenanthera polysaccharides fractions (SSPs), namely SSP40, SSP60, and SSP80, were obtained by gradient precipitation with 40 %, 60 %, and 80 % (v/v) ethanol, respectively. It was found that gradient ethanol precipitation (GEP) significantly affected the physicochemical and structural characteristics of SSPs, including molecular weight, monosaccharide composition, and surface morphology. Compared to fractions SSP40 and SSP60, SSP80 was observed to have a lower molecular weight (22.58 kDa) and certain specific monosaccharide composition, such as lower glucose content and higher galactose, arabinose, rhamnose, and galacturonic acid content. Furthermore, the apparent porosity of the SSPs increased with increasing ethanol concentration in GEP. After fermentation at 37 °C for 48 h, fraction SSP80 prominently promoted the production of more short-chain fatty acids (SCFAs), increasing from an initial 1.39 ± 0.08 to 26.75 ± 0.54 mmol/L. The SSP fraction types extracted by GEP greatly affected the modulation of the intestinal microflora at different levels. The SSP80 fraction with excellent structure demonstrated the best ability to modulate the intestinal microflora by increasing the relative abundance of Bacteroides, Faecalibacterium and Dialister and decreasing the relative abundance of Escherichia-Shigella. The remarkable differences in modulating the intestinal microflora confirmed the importance of carefully selecting GEP to fraction SSPs that promote health.

Abstract The improvement of thermodynamic efficiency of power plants is of great interest for the whole energy industry. The use of Kalina cycle has a great potential to improve the thermal efficiency of a nuclear power plant. This cycle uses a mixture of ammonia and water as working fluid. In this paper, we discuss the development of an Ammonia-Water mixture Property Code (AWProC). The estimation of the mixture properties are based on the Gibbs free energy functions. The code is verified and validated against experimental data available in the literature and REFPROP code. It is shown that AWProC can accurately estimate the thermodynamic properties of ammonia-water mixtures over a wide range of conditions, including high temperature and pressure regions. The code is then used to investigate the feasibility of applying the Kalina cycle to a typical Pressurizer Water Reactor (PWR) plant as an effective way to improve the plant efficiency. The fundamental of Basic-Kalina (B-K) cycle is described in detail firstly. Then, two modified configurations, Recuperation-Kalina (R-K) and Flash-Kalina (F-K) cycles respectively, are proposed for a typical 1000 MWe PWR. The simulation results indicate that the R-K type cycle can reach about 31.2% efficiency with simple equipment requirements, while the F-K type cycle can reach efficiencies up to about 34.8%, but at the expenses of a slightly more complex design. The present work demonstrates the applicability of the Kalina cycle as a way to improve the thermal efficiency of a nuclear power plant. This concept is meaningful for improving nuclear power plants economic and competitiveness.

<p>Increasing renewable energy penetrations bring complex feasibility and stability problems. Data-driven methods are applied in extracting and embedding these feasibility and stability rules in power system operations and planning. This paper presents a method of alternate support vector machine decision trees for rule extraction problems. The method has significant improvements to the classical decision-tree-based algorithms in terms of efficiency, stability and versatility. Finally, we apply the method to several power and energy system scenarios to show its effectiveness.</p>