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A method based on solid-phase extraction-ultra performance liquid chromatography-tandem mass spectrometry (SPE-UPLC-MS/MS) was established for the determination of gpenicillin, cloxacillin, ampicillin residues in milk. Using self-made covalent triazine frameworks (CTFs) as the solid-phase extraction sorbents, the main factors influencing the efficiency of the solid-phase extraction columns, such as the sorbent amount, eluent type, eluent volume, and flow rate, were optimized. The extraction and purification conditions for the samples were also investigated. The optimal extraction effect was achieved at a flow rate of 3 mL/min with 60 mg CTFs and 6 mL eluent solution (acetonitrile). Separation was carried out on a Waters ACQUITY UPLC BEH C18 column, and 0.1% formic acid aqueous solution-acetonitrile was used as the mobile phases for gradient elution. The filtrate was detected by ultra performance liquid chromatography-tandem mass spectrometry, identified by electrospray ionization (ESI) in the positive mode using multiple reaction monitoring, and quantified using external standards. The calibration curves of the three penicillins showed good linearity and the correlation coefficients of the linear regression equations for the three target analytes were all greater than 0.999. The limits of detection (LODs) and limits of quantification (LOQs) were 0.05-0.10 μg/kg and 0.1-0.4 μg/kg, respectively. The average recoveries of the three analytes were 84.9%-94.1%, and the relative standard deviations (RSDs

Bending modulus is a key parameter to characterize the stiffness of materials. Commonly, it is believed that the bending modulus is closely related to the thickness as described by the thin plate theory. However, the thin plate theory fails in multilayer van der Waals materials like multilayer graphene, suggesting a more complex relationship between the bending modulus and thickness. Here, rippled graphene structures containing non-hexagonal carbon rings with different thicknesses are constructed to study the thickness-dependent bending modulus by the first-principles calculations. It is found that the bending modulus of rippled graphene depends on several factors, such as geometry, bending curvature, and thickness. Particularly, for the egg-tray graphene structures with similar structural pattern and bending curvature, i.e., eliminating the effects of structural pattern and bending curvature, the bending modulus could show a linear relationship to the thickness. Moreover, this linear relationship is very robust even in the case of changing the thickness through heteroatom doping.

Low-energy, high-flux He exposure to tungsten (W) surface leads to the formation of surface nanostructures and severe morphological changes, which may eventually erode the W divertor and threaten the operation of the reactor. In this study, the response of polycrystalline W under low-energy He+ irradiation at different temperatures has been investigated in order to analyze the early stage of nanostructure formation. It is found that the interactions of high-density over-pressured He bubbles result in the formation of surface stress layers. The significant effect of temperature on the surface stress leads to differences in the incubation dose of W fuzz growth. The interaction between the planar network and the underlying W matrix is weak under surface stress, and the W fuzz grows on the surface once the stress reaches a threshold. Thereafter, tensile stress-driven cracking causes the 3D growth of W nanofibers.

The measurement of impurity distribution in the divertor region of tokamaks is key to studying edge impurity transport. Therefore, a space-resolved vacuum-ultraviolet (VUV) spectrometer is designed to measure impurity emission in the divertor region on EAST. For good spectral resolution, an eagle-type VUV spectrometer with 1 m long focal length with spherical holograph grating is used in the system. For light collection, a collimating mirror is installed between the EAST plasma and the VUV spectrometer to extend the observing range to cover the upper divertor region. Two types of detectors, i.e. a back-illuminated charge-coupled device detector and a photomultiplier-tube detector, are adopted for the spectral measurement and high-frequency intensity measurement for feedback control, respectively. The angle between the entrance and exit optical axis is fixed at 15°. The detector can be moved along the exit axis to maintain a good focusing position when the wavelength is scanned by rotating the grating. The profile of impurity emissions is projected through the space-resolved slit, which is set horizontally. The spectrometer is equipped with two gratings with 2400 grooves/mm and 2160 grooves/mm, respectively. The overall aberration of the system is reduced by accurate detector positioning. As a result, the total spectral broadening can be reduced to about 0.013 nm. The simulated performance of the system is found to satisfy the requirement of measurement of impurity emissions from the divertor area of the EAST tokamak.

The impact of rough surface morphology on the angular distribution of eroded impurities has been investigated with the three-dimensional (3D) rough surface code SURO and a newly developed analytic model. The property of the rough surface structure can be described by the shadow angle of the rough surface in SURO, which is defined as the ratio of the horizontal characteristic length to the initial surface roughness. The SURO simulation results show that the influence of the rough surface on the angular distribution of eroded impurities comes into play when the shadow angle is larger than a threshold value. The larger shadow angle of the rough surface leads to a stronger shift of the angular distribution of the eroded impurities. Different rough surface topographies have been used in the SURO code to check the angular distribution of the eroded impurities. It is found that the shift tendency of the angular distribution is similar for different structures of the rough surface. Based on the numerical modelling results, an analytical model has been developed to investigate the impact of the shadow angle on the angular distribution of the eroded impurities, which shows the consistent result as the SURO simulations.

Toroidal distributions of divertor particle flux during neon (Ne) and nitrogen (N2) seeded discharges were investigated in the Large Helical Device (LHD). By using 14 toroidally distributed divertor probe arrays, which were positioned at radially inner side where the divertor flux concentrates in the inward-shifted magnetic axis configuration, it is found that Ne puffing leads to toroidally quasi-uniform reduction of divertor particle fluxes; whereas toroidally localized reductions were observed with N2 puffing. The toroidally asymmetric reduction pattern with N2 puffing is strongly related to the magnetic field structure around the N2 puffing port. Assuming that nitrogen particles do not recycle, EMC3-EIRENE simulation shows similar reduction pattern with the experiment around the N2 puffing port. Keywords: Impurity seeding, LHD, Toroidal divertor probe arrays, Neon, Nitrogen, EMC3-EIRENE

The formation mechanism of nanocone structure on silicon (Si) surface irradiated by helium plasma has been investigated by experiments and simulations. Impurity (molybdenum) aggregated as shields on Si was found to be a key factor to form a high density of nanocone in our previous study. Here to concrete this theory, a simulation work has been developed with SURO code based on the impurity concentration measurement of the nanocones by using electron dispersive x-ray spectroscopy. The formation process of the nanocone from a flat surface was presented. The modeling structure under an inclining ion incident direction was in good agreement with the experimental result. Moreover, the redeposition effect was proposed as another important process of nanocone formation based on results from the comparison of the cone diameter and sputtering yield between cases with and without the redeposition effect.

Hydrogen fuel cell vehicle is one of the important modalities of the next generation of new energy vehicles. As the basic infrastructure of hydrogen fuel cell vehicles, hydrogen refueling stations (HRSs) have drawn attention from all social circles. However, hindered by various barriers, the development of HRSs is much slower than the Chinese government's expectation. Hence, this study aims to explore critical barriers of the HRS development in China based on a modified fuzzy decision-making trial and evaluation laboratory (DEMATEL) method. Firstly, a barrier list is identified through a literature survey and expert consultation, which consists of four categories (economic, technological, social and political-regulatory aspect) and 12 potential barriers. Later, considering the subjectivity of threshold setting in the traditional fuzzy DEMATEL method, the K-means clustering algorithm is utilized to optimize the threshold value, and thus a modified fuzzy DEMATEL approach is proposed to investigate the prominence of these barriers and the cause-effect relationships among them. The results show that six barriers are regarded as key barriers to China's HRS development, and corresponding policy measures are formulated to mitigate or eliminate these critical barriers.

The electricity market of China is currently in the process of a new institutional reform. Diversified electricity retail entities are gradually being established with the opening of the marketing electricity side. In the face of a complex market environment and fierce competition, the operating efficiency can directly reflect the current market position and development of electricity retail companies. TOPSIS method can make full use of the information of original data, calculate the distance between evaluated objects and the ideal solutions and get the relative proximity, which is generally used in the overall department and comprehensive evaluation of the benefits. Least squares support vector machine (LSSVM), with high convergence precision, helps save the training time of algorithm by solving linear equations and is used to predict the comprehensive evaluation value. Considering the ultimate goal of sustainable development, a comprehensive evaluation model on operating efficiency of electricity retail companies based on the improved TOPSIS method and LSSVM optimized by modified ant colony algorithm is proposed in this paper. Firstly, from the view of sustainable development, an operating efficiency evaluation indicator system is constructed. Secondly, the entropy weight method is applied to empower the indicators objectively. After that, based on the improved TOPSIS method, the reverse problem in the evaluation process is eliminated. According to the relative proximity between the evaluated objects and the absolute ideal solutions, the scores of comprehensive evaluation for operating efficiency can then be ranked. Finally, the LSSVM optimized by modified ant colony algorithm is introduced to realize the simplified expert scoring process and fast calculation in the comprehensive evaluation process, and its improved learning and generalization ability can be used in the comprehensive evaluation of similar projects. The example analysis proves that the comprehensive evaluation model proposed in this paper can provide scientific and effective evaluation results of the operating efficiency of electricity retail companies.

The electric power industry is of great significance in promoting social and economic development and improving people’s living standards. Power grid construction is a necessary part of infrastructure construction, whose sustainability plays an important role in economic development, environmental protection and social progress. In order to effectively evaluate the sustainability of power grid construction projects, in this paper, we first identified 17 criteria from four dimensions including economy, technology, society and environment to establish the evaluation criteria system. After that, the grey incidence analysis was used to modify the traditional Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), which made it possible to evaluate the sustainability of electric power construction projects based on visual angle of similarity and nearness. Then, in order to simplify the procedure of experts scoring and computation, on the basis of evaluation results of the improved TOPSIS, the model using Modified Fly Optimization Algorithm (MFOA) to optimize the Least Square Support Vector Machine (LSSVM) was established. Finally, a numerical example was given to demonstrate the effectiveness of the proposed model.

As an important implementing body of the national energy strategy, grid enterprises bear the important responsibility of optimizing the allocation of energy resources and serving the economic and social development, and their levels of sustainable development have a direct impact on the national economy and social life. In this paper, the model of fuzzy group ideal point method and combination weighting method with improved group order relation method and entropy weight method is proposed to evaluate the sustainable development of power grid enterprises. Firstly, on the basis of consulting a large amount of literature, the important criteria of the comprehensive evaluation of the sustainable development of power grid enterprises are preliminarily selected. The opinions of the industry experts are consulted and fed back for many rounds through the Delphi method and the evaluation criteria system for sustainable development of power grid enterprises is determined, then doing the consistent and non dimensional processing of the evaluation criteria. After that, based on the basic order relation method, the weights of each expert judgment matrix are synthesized to construct the compound matter elements. By using matter element analysis, the subjective weights of the criteria are obtained. And entropy weight method is used to determine the objective weights of the preprocessed criteria. Then, combining the subjective and objective information with the combination weighting method based on the subjective and objective weighted attribute value consistency, a more comprehensive, reasonable and accurate combination weight is calculated. Finally, based on the traditional TOPSIS method, the triangular fuzzy numbers are introduced to better realize the scientific processing of the data information which is difficult to quantify, and the queuing indication value of each object and the ranking result are obtained. A numerical example is taken to prove that the model of fuzzy group ideal point method and combination weighting method with improved group order relation method and entropy weight method is feasible and effective for evaluating the sustainable development of power grid enterprises.

To investigate carbon-based material erosion and ensuing eroded carbon species density distribution, a ParticleIn-Cell Monte Carlo collision code plus a plasma-surface interaction code were combined with a Monte Carlo impurity transport code ITCD. It is found that a ”density hill“ forms at a distance of 3-4 mm from divertor target plate under certain circumstances: high plasma temperature, small incident angle and strength of the magnetic field. By means of the study with the combined code on several scenarios with different plasma temperatures, the incident angles and strengths of the magnetic field, the simulation results reveal that they have a significant influence on the chemical erosion of carbon-based material and the spatial distribution of eroded carbon species density above divertor target plate: the eroded carbon species density is smaller at a lower plasma temperature; the scenario with smaller incident angle and greater strength of the magnetic field can reduce the chemical erosion of carbon-based material and the eroded carbon species density above divertor target plate evidently. c

Thermal desorption of hydrogen molecules from H + irradiated graphite is studied using dynamic Monte Carlo simulation. The purpose of this study is to understand the experimentally observed phenomena that the thermal desorption of H 2 from the graphite exhibits sometimes single desorption peak, sometimes double peaks, and even three desorption peaks under certain circumstances. The study result reveals that the fluence of pre-implanted H + , the concentration of trap sites, porosity, and mean crystallite volume are important parameters in determining the number of desorption peaks. It is found that low implantation fluence and high concentration of trap sites easily lead to the occurrence of single desorption peak at around 1000 K, and high implantation fluence and low concentration of trap sites favor the occurrence of double desorption peaks, with a new desorption peak at around 820 K. It is also found that small porosity of graphite and large crystallite volume benefit the occurrence of single desorption peak at around 1000 K while large porosity of graphite and small crystallite volume facilitate the occurrence of double desorption peaks, respectively, at around 820 and 1000 K. In addition, experimentally observed third desorption peak at lower temperature is reproduced by simulation with assuming the graphite containing a small concentration of solute hydrogen atoms.

Presently, China is the largest CO2 emitting country in the world, which accounts for 28% of the CO2 emissions globally. China’s CO2 emission reduction has a direct impact on global trends. Therefore, accurate forecasting of CO2 emissions is crucial to China’s emission reduction policy formulating and global action on climate change. In order to forecast the CO2 emissions in China accurately, considering population, the CO2 emission forecasting model using GM(1,1) (Grey Model) and least squares support vector machine (LSSVM) optimized by the modified shuffled frog leaping algorithm (MSFLA) (MSFLA-LSSVM) is put forward in this paper. First of all, considering population, per capita GDP, urbanization rate, industrial structure, energy consumption structure, energy intensity, total coal consumption, carbon emission intensity, total imports and exports and other influencing factors of CO2 emissions, the main driving factors are screened according to the sorting of grey correlation degrees to realize feature dimension reduction. Then, the GM(1,1) model is used to forecast the main influencing factors of CO2 emissions. Finally, taking the forecasting value of the CO2 emissions influencing factors as the model input, the MSFLA-LSSVM model is adopted to forecast the CO2 emissions in China from 2018 to 2025.

The intermediate compounds, K 3 DyCl 6 , K 2 DyCl 5 and KDy 2 Cl 7 , in the DyCl 3 –KCl system, are confirmed by X-ray powder diffraction method. With CALPHAD technology the phase diagram of the DyCl 3 –KCl system is re-optimized and calculated using ChemSage software. A set of thermodynamic functions has been optimized based on an interactive computer-assisted analysis. The calculated phase diagram and optimized thermodynamic parameters are thermodynamically self-consistent.

Three dimensional (3D) divertor particle flux footprints induced by the lower hybrid wave (LHW) have been systematically investigated in the EAST superconducting tokamak during the recent experimental campaign. We find that the striated particle flux (SPF) peaks away from the strike point (SP) closely fit the pitch of the edge magnetic field line for different safety factors q 95, as predicted by a field line tracing code taking into account the helical current filaments (HCFs) in the scrape-off-layer (SOL). As LHW power increases, it requires the fuelling to be increased e.g. by super molecular beam injection (SMBI), to maintain a similar plasma density, which may be attributed to the pump-out effect due to LHW, and may thus be beneficial for EAST steady state operations. The 3D SPF structure is observed with a LHW power threshold (P LHW ~ 0.9 MW). The ratio of the particle fluxes between SPF and outer strike point (OSP), i.e. , increases with the LHW power. Upon transition to divertor detachment, the particle flux at the main OSP decreases, as expected, however, the particle flux at SPF continues increasing, in contrast to the RMP-induced striations that vanish with increasing divertor density. In addition, we also find that the in–out asymmetry of the 3D particle flux footprint pattern exhibits a clear dependence on the toroidal field direction (B × ∇ B ↓ and B × ∇ B↑). Experiments using neon impurity seeding show a promising capability in 3D particle and heat flux control on EAST. LHW-induced particle and heat flux striations are also present in the H-mode plasmas, reducing the peak heat flux and erosion at the main strike point, thus facilitating long-pulse operation with a new steady-state H-mode over 60 s being recently achieved in EAST.

As an important part of power system planning and the basis of economic operation of power systems, the main work of power load forecasting is to predict the time distribution and spatial distribution of future power loads. The accuracy of load forecasting will directly influence the reliability of the power system. In this paper, a novel short-term Empirical Mode Decomposition-Grey Relational Analysis-Modified Particle Swarm Optimization-Least Squares Support Vector Machine (EMD-GRA-MPSO-LSSVM) load forecasting model is proposed. The model uses the de-noising method combining empirical mode decomposition and grey relational analysis to process the original load series and forecasts the processed subsequences by the algorithm of modified particle swarm optimization and least square support vector machine. Then, the final forecasting results can be obtained after reconstructing the forecasting series. This paper takes the Jibei area as an example to produce an empirical analysis for load forecasting. The model input includes the hourly load one week before the forecasting day and the daily maximum temperature, daily minimum temperature, daily average temperature, relative humidity, wind force, date type of the forecasting day. The model output is the hourly load of the forecasting day. The models of BP neural network, SVM (Support vector machine), LSSVM (Least squares support vector machine), PSO-LSSVM (Particle swarm optimization-Least squares support vector machine), MPSO-LSSVM (Modified particle swarm optimization-Least squares support vector machine), EMD-MPSO-LSSVM are selected to compare with the model of EMD-GRA-MPSO-LSSVM using the same sample. The comparison results verify that the short-term load forecasting model of EMD-GRA-MPSO-LSSVM proposed in this paper is superior to other models and has strong generalization ability and robustness. It can achieve good forecasting effect with high forecasting accuracy, providing a new idea and reference for accurate short-term load forecasting.

In the view of the physical properties an empirical approach is well developed for estimating enthalpy of mixing in the MCl–ReCl 3 melts from experimental data (M=alkali metals and Re=rare earth metals). The interaction parameter of enthalpy of mixing at constant temperature, constant pressure and specific composition is expressed as λ i = a + bY + cY 2 + dY 3 , where Y is so-called the modified relative ionic potential, related to the valence, radius of the ionic and electronegativity of relative element M or Re. The coefficients a , b , c , d can be obtained from a multi-regression method with experimental data. The calculated results by present method agree well with the experimental data.