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Summary Carbon nanobelts that consist of a loop of fully fused benzene rings are long-standing and challenging targets of organic synthesis and are key steps toward the ultimate synthesis of uniform carbon nanotubes (CNTs) of single chirality and predefined diameter. Herein, we report the synthesis of an unprecedented armchair carbon nanobelt and the first chiral carbon nanobelt, which represent sidewall segments of armchair and chiral CNTs, (12,12)CNT and (18,12)CNT, respectively. These carbon nanobelts were synthesized from 2,5-di(benzyloxy)-1,4-benzoquinone in six steps by π-expansion of the corresponding polyarylated carbon nanorings through Scholl reactions. Scanning tunneling microscopy revealed these nanobelts to be nanoparticles of roughly uniform size. In contrast to earlier unsuccessful attempts to synthesize carbon nanobelts from the corresponding carbon nanorings, the belt-forming step reported herein is accompanied by either a small increase or even a decrease in strain energy, as found from the theoretical calculations.

This study contributes to proposing the improved bird swarm algorithm optimization least squares support vector machine (IBSA-LSSVM) model to predict the remaining life of lithium-ion batteries. By improving the prediction accuracy of the model, the safety and reliability of the new energy storage system are improved. In order to avoid the bird swarm algorithm (BSA) getting into the local optimal solution, the levy flight strategy is introduced into the improved bird swarm algorithm (IBSA), which improves the convergence performance of the algorithm. Hence, this study is to verify the effectiveness of the proposed hybrid IBSA-LSSVM model. The following work has been done: (1) test functions are used to test particle swarm optimization (PSO), differential evolution algorithm (DE), BSA and IBSA; (2) the back propagation neural network (BP) model, support vector machine (SVM) model, quantum particle swarm optimization support vector machine (QPSO-SVM) model, BSA-LSSVM model and IBSA-LSSVM model are tested with the B5, B6 and B18 batteries. The following findings are obtained: (1) the five test functions are used to test the PSO, DE, BSA and IBSA algorithms in 20 dimensions, 50 dimensions and 80 dimensions. The results show that the convergence accuracy and convergence stability of IBSA algorithm is higher than those of the other three algorithms; (2) the residual life of B5, B6 and B18 batteries are predicted by the BSA-LSSVM, SVM, QPSO-SVM, BP and IBSA-LSSVM models. The test results show that the root mean square error of the IBSA-LSSVM model for B5 battery is 0.01, the root mean square error for B6 battery is 0.06, and the root mean square error for B18 battery is 0.02. The results show that the prediction accuracy of proposed model is higher than that of the other models.

The strong volatility and randomness of wind power impact the grid and reduce the voltage quality of the grid when wind power is connected to the grid in large scale. The power sector takes the wind abandonment measures to ensure the grid voltage stability. The enhanced crow search algorithm optimization-extreme learning machine (ENCSA-ELM) model is proposed to accurately forecast short-term wind power to improve the utilization efficiency of clean energy. (1) The enhanced crow search algorithm (ENCSA) is proposed and applied to short-term wind power forecast. The convergence performance test revealed that the local development and global exploration capabilities of the ENCSA were enhanced, and the test result of the proposed ENCSA algorithm outperformed other well-known nature inspired algorithms and state-of-the-art CSA variants; (2) The output and input of the forecasting models were determined by analysis of the wind power impact factors and the wind power samples in autumn, winter and spring were forecasted by the ENCSA-ELM model; and (3) The forecast results were analyzed by multiple evaluation indexes. The simulation experiments revealed that the error interval and evaluation indexes of the ENCSA-ELM model outperformed the state-of-the-art wind power forecast methods, traditional machine learning models and ELM optimized by other algorithms. The RMSE value and MAPE value of the proposed model were controlled below 20% and 4%. Accurate wind power prediction maintains the voltage stability of power grid and increases the utilization efficiency of clean energy.

This study contributes to construct the mathematical model of hybrid dynamic economic emission dispatch (HDEED) considering renewable energy generation and propose a novel solving approach based on enhanced moth-flame optimization algorithm. Renewable energy power generation technology has an important impact on reducing pollutant emissions and promoting sustainable development. Therefore, this study aims to investigate the HDEED problem in consideration of renewable energy generation and improve the economic and environmental benefits of the power system. First, a moth-flame optimization algorithm based on position disturbance updating strategy (MFO_PDU) was proposed aiming at the non-convex, non-linear and high-dimensional characteristics of HDEED problem. Second, the mathematical model of HDEED on the basis of Wind-Solar-Thermal integrated energy was constructed, while taking into account the valve point effect, equality constraints and inequality constraints. Finally, three cases including test systems of different scales were formulated and employed to verify the proposed approach, and the compromise solution was determined through membership function. The results revealed that the fuel cost obtained by the MFO_PDU algorithm was 11.31%, 4.01% and 5.27% smaller than those of HHO, TSA and MFO algorithms for small-scale test system. Accordingly, the research outcomes contribute in reducing the fuel cost and pollutant emissions of power generation system, and further improving the utilization and penetration rate of renewable energy.

This study proposes improved tunicate swarm algorithm (ITSA) for solving and optimizing the dynamic economic emission dispatch (DEED) problem. The DEED optimization target is to reduce the fuel cost and pollutant emission of the power system. In addition, DEED is a complex optimization problem and contains multiple optimization goals. To strengthen the ability of the ITSA algorithm for solving DEED, the tent mapping is employed to generate initial population for improving the directionality in the optimization process. Meanwhile, the gray wolf optimizer is used to generate the global search vector for improving global exploration ability, and the Levy flight is introduced to expand the search range. Three test systems containing 5, 10 and 15 generator units are employed to verify the solving performance of ITSA. The test results show that the ITSA algorithm can provide a competitive scheduling plan for test systems containing different units. ITSA proposed algorithm gives the optimal economic and environmental dynamic dispatch scheme for achieving more precise dispatch strategy.

The contribution is to propose a novel hybrid model based on modal reconstruction to predict short-term photovoltaic (PV) power. PV power generation large-scale grid connection causes the impact on the power system due to the instability and intermittence, and PV curtailment measures are taken to reduce the impact from voltage fluctuation. Accurate forecast is necessary to make a reasonable generation plan. A novel hybrid forecasting model is proposed, and an enhanced gray wolf optimization algorithm is proposed to improve the convergence ability and to solve the influence of extreme learning machine random parameters. The proposed algorithm has stronger convergence stability and higher convergence accuracy compared with the existing algorithms. The ensemble empirical mode decomposition algorithm is used to decompose the PV power under different weather conditions. The complexity of each component is calculated by the sample entropy, and the components are reconstructed to reduce the computational cost of forecasting models. The results revealed that mean absolute percentage error and root mean square error of the proposed model are smaller than 3% and 0.36 under various weather conditions. Meanwhile, the determination coefficient of the proposed model is more than 98%.

A series of nanostructures of Zinc Sulfide (ZnS), including the small Zn n S n ( n = 1–15) clusters, the magic cage-like Zn 12 S 12 and its derivants (Zn 12 S 12 -based dimers, trimers, nanowires and monolayer) were systematically investigated by using a first-principles bottom-up calculation. The results show that the Zn 12 S 12 cage-like structure with six isolated four-membered rings (4NRs) and eight six-membered rings (6NRs) has T h symmetry and a large HOMO-LUMO gap, indicating that the Zn 12 S 12 cluster would be ideal building blocks for the synthesis of new cluster-assembled materials. Via the aggregation of Zn 12 S 12 , we find that the Zn 12 S 12 cages can bind into various porous materials with different dimensionality by the most stable bonding H H 1 . Interestingly, all of these cluster-assembled structures are found to be more stable than the individual Zn 12 S 12 cage and rock salt (RS) phase of ZnS. These fascinating findings will be advantageous for promoting the potential applications of the inorganic ZnS-based nanosystems in the new type of electronic nanodevices and high-performance semiconductor materials.

White adipose tissue (WAT) browning is a potential strategy to treat obesity, and is characterized by the formation of brown adipocytes induced by cold or β-3 adrenergic receptor (β-3AR) agonist treatment. The hedgehog (Hh) signaling at the primary cilium is closely related to obesity, and plays a key role in the differentiation and adipogenesis of adipocytes. However, little is known about its effects on WAT browning. In this study, browning models were used to evaluate the activity and effect of Hh signaling on WAT browning using Hh antagonists, agonist, and small-interfering RNAs (siRNAs) specific for glioma-associated oncogene homologue 1 (Gli1), smoothened (Smo), and suppressor of fused (Sufu). We observed that Hh signaling activity was inhibited during the browning process both in vivo and in vitro. Further, Hh signaling inhibition enhanced WAT browning, while its activation attenuated norepinephrine-induced browning. Thus, the inhibition of Hh signaling promotes WAT browning and therefore, Hh signaling may be a therapeutic target against obesity and associated comorbidities.

Photovoltaic power generation is greatly affected by weather conditions while the photovoltaic power has a certain negative impact on the power grid. The power sector takes certain measures to abandon photovoltaic power generation, thus limiting the development of clean energy power generation. This study is to propose an accurate short-term photovoltaic power prediction method. A new short-term photovoltaic power output prediction model is proposed Based on extreme learning machine and intelligent optimizer. Firstly, the input of the model is determined by correlation coefficient method. Then the chicken swarm optimizer is improved to strengthen the convergence. Secondly, the improved chicken swarm optimizer is used to optimize the weights and the extreme learning machine thresholds to improve the prediction effect. Finally, the improved chicken swarm optimizer extreme learning machine model is used to predict the photovoltaic power under different weather conditions. The testing results show that the average mean absolute percentage error and root mean square error of improved chicken swarm optimizer - extreme learning machine model are 5.54% and 3.08%. The proposed method is of great significance for the economic dispatch of power systems and the development of clean energy.

By ab initio molecular dynamics simulations, we examined the variation in the oxygen reduction reaction pathways on Pt(1 1 1) in the presence of H, HBr or HClO4, with dozens of explicit water molecules. The free energy changes and barriers were calculated by thermodynamic integration. Significant variations among the three models were observed, indicating the importance of considering the effect of an anion. The dissociation of adsorbed O2* and the association of a proton with an adsorbed O2* were often coupled with each other due to the reactivity of an adsorbed O* atom towards both H2O and solvated H+.

E-tutors play an important role in after-school distance learning project. This study focused on developing the readiness instrument for evaluation of the e-tutors, and hoped to assist the mentor to understand the teaching e-tutors well. Teaching readiness index scale was used as research instruments. Besides, qualitative data was also collected for analysis in the study. Distance Learning companion project started from October 22, 2013 to January 9, 2014. Forty four e-tutors participated for one hour and a half a course, twice per week. The results indicated that at the beginning of distance learning companion project, forty-two e-tutors showed high job readiness and high psychological readiness (R4), and two e-tutors showed low job readiness and high psychological readiness (R2). After a semester of teaching and training, 44 were high job readiness and high psychological (R4). According to the results of the study, job readiness was improved and the psychological readiness was maintained. In the study, it was found that the content of the training workshop mainly focused on job readiness, but neglected psychological readiness. In the future, more content about psychological readiness should be designed in the training workshop to support the e-tutors.

In this article, the statistical properties for the renormalized permeability obtained from the renormalization of the correlated permeability field are investigated. In contrast to the uncorrelated porous media, the scaling of the variance of the renormalized permeability field exhibits a crossover behavior. When the correlation lengths are larger compared with the domain scale covered by the renormalization procedure, the variance of the renormalized permeability will decrease slowly and the scaling exponent will be close to zero. As the renormalization number increases, the covered domain scale will eventually become larger than the correlation lengths, and then the scaling property will transit to the uncorrelated case. The convergent values of the renormalized permeability for isotropic and anisotropic correlated media are also investigated. Both the theoretical analysis and the simulation results show that larger correlation length in one direction will lead to a larger convergent value in the corresponding direction. For the log-normal permeability field, numerical simulations show that the crossover scaling and also the convergent value for the renormalized permeability can be fitted very well by simple mathematical functions.

Objective To evaluate the effects of Xuebijing (XBJ) injection in heat stroke (HS) rats and to investigate the mechanisms underlying these effects. Methods Sixty anesthetized rats were randomized into three groups and intravenously injected twice daily for 3 days with 4 mL XBJ (XBJ group) or phosphate buffered saline (HS and Sham groups) per kg body weight. HS was initiated in the HS and XBJ groups by placing rats in a simulated climate chamber (ambient temperature 40°C, humidity 60%). Rectal temperature, aterial pressure, and heart rate were monitored and recorded. Time to HS onset and survival were determined, and serum concentrations of tumor necrosis factor (TNF)-α, interleukin (IL)-1β, IL-6, alanine-aminotransferase (ALT), and aspartate-aminotransferase (AST) were measured. Hepatic tissue was harvested for pathological examination and electron microscopic examination. Kupffer cells (KCs) were separated from liver at HS initiation, and the concentrations of secreted TNF-α, IL-β and IL-6 were measured. Results Time to HS onset and survival were significantly longer in the XBJ than in the HS group. Moreover, the concentrations of TNF-α, IL-1β, IL-6, ALT and AST were lower and liver injury was milder in the XBJ than in the HS group. Heat-stress induced structural changes in KCs and hepatic cells were more severe in the HS than in the XBJ group and the concentrations of TNF-α, IL-β and IL-6 secreted by KCs were lower in the XBJ than in the HS group. Conclusion XBJ can alleviate HS-induced systemic inflammatory response syndrome and liver injury in rats, and improve outcomes. These protective effects may be due to the ability of XBJ to inhibit cytokine secretion by KCs.

Starting from the investigation on the detailed flow pattern, finite analytic numerical method is developed to solve the two-dimensional fluid flows in heterogeneous porous media. It is shown that only for some specific permeability distributions the pressure has the piecewise linear distribution, where harmonic average scheme works very well. In general case, the pressure will have the power-law behavior and its gradient will diverge as approaching the node joining the different permeability areas. The nodal flow effects cause the flow fingering into the high permeability region. It is a challenge problem to numerically describe the nodal fingering effects. With the help of the specific properties of pressure and its gradient around the node, a local analytical nodal solution is derived and then applied to construct a finite analytic numerical scheme. Numerical examples show that the detailed flow pattern can be reconstructed with the proposed numerical scheme under few grid refinements. Only with 2x2 or 3x3 subdivisions, the proposed numerical scheme can provide rather accurate solutions. The convergent speed of the numerical scheme is independent of the permeability heterogeneity. In contrast, the refinement ratio for the grid cell needs to be increased dramatically to get an accurate result when the traditional numerical method is used for strong heterogeneous cases.

Recently online discussion forums have become alternative information resources for student learning. This paper investigated the influences of the quality of online discussion forums on students’ discussion behaviors and learning performance. The statistical results revealed that students would have better learning performance and more discussion behaviors when an elaborated online discussion forum was provided as a learning resource.

A novel property of red phosphorus for visible light driven photocatalytic H 2 formation from water by photogenerated electrons has been discovered. The detection of hydroxyl radicals and results from photoconductivity measurements confirmed the photogeneration of electrons and holes. Theoretical calculations also indicated that the reduction of water by photogenerated electrons would be energetically possible. A P-type semiconductor behavior of red phosphorus was observed. Our findings may provide insights for developing phosphorus-based photocatalysts.

The chemisorption of O 2 on Al(1 1 1), a model reaction for surface oxidation and catalysis, has been a long standing puzzle due to the presence of a reaction barrier in experiment, not reproduced in theory, which is often explained by non-adiabatic effects. We demonstrate in this Letter, by using Al 13 - as a cluster model for Al(1 1 1), that self-interaction errors in the commonly used exchange–correlation functionals are responsible for the missing barrier by overestimating the charge transfer along the reaction path. Such errors should be an important consideration in the theoretical modeling of surface and cluster oxidation processes.

Molecule hydrogen is known to have a weak van der Waals potential, which makes it difficult to raise its storage efficiency for physisorption based methods. In this report, we explore the other side of such a weak potential, the well-known compressibility of hydrogen. A (20, 0) single wall carbon nanotube based nanocontainer is designed, in which a C60 ‘‘peapod’’ at the cap section of the nanotube serves as a molecular valve. Diffusion barriers through such a valve is examined by molecular dynamics simulations under various conditions. It is demonstrated that H2 can first be filled into the container upon compression at low temperature, and then be locked inside it after the release of external pressure. The internal pressure that can be achieved in this design is in the GPa range at room temperature, which is much higher than the typical pressure of a few hundred bar currently employed for hydrogen storage. At 2.5 GPa, the storage weight ratio approaches a promising 7.7%.

Using the addition of an O2 to a (10, 0) tube as a model case, the link between pressure and the reactivity of a carbon nanotube is examined by first principles calculations. Attaching functional groups to nanotube side-walls have been envisioned as a method to produce desired structural and electronic properties, although challenges remain for activating and controlling such reactions, especially for tubes with large diameters. Upon pressure, the circular section of a nanotube is flattened and differentiation in chemical reactivity is induced, with the bent section more reactive than the flat section. In the reaction with singlet O2, both the adsorption energy and the activation barrier for the crucial initial step of cyclo-addition become more favorable on the bent sections, as higher pressure is applied. These values are also found to be linearly correlated with the pyramidalization angle θp, which can be further related to external pressure. The estimate shows that it is easier to activate tubes with larger diameters by applying pressure, since such tubes are easier to deform. Our results indicate that pressure, as an easily controlled macroscopic variable, can be used for both selective adsorption and chemical activation.

The chemisorption of ozone on the side wall of an infinitely long (5,5) tube, and its subsequent desorption and decomposition, is reexamined by planewave/pseudopotential based density functional theory (DFT) and ONIOM calculations. Previous ONIOM results were found to be inadequate due to the small size of the inner layer. Analysis on the molecular vibrations for ozonized (5,5) tubes and for an epoxy adduct also identified bands around 920 and 1380 cm−1 regions, which were previously observed in experiment.

Based on solving the Navier–Stokes equations in the two-dimensional percolation porous media for 500 different configurations, the scaling relations for the fluid permeability k and the inertial parameter β in the Forchheimer equation are studied. In the vicinity of the critical threshold pc, the fluid permeability k and the inertial parameter β will crossover from the fractal behaviors: k∼L−μ1, β∼Lμ2, where μ1≈1.0, μ2≈2.0 for the small size L, to the constants: k∼(p−pc)α1, β∼(p−pc)−α2, where α 1 ≈ 4 3 , α 2 ≈ 8 3 . Compared to the viscous flow, the resistance to flow will have a larger critical exponent for the finite Reynolds number flows.

Designing products for recyclability is driven by environmental and economic goals. To obtain good recyclability, two measures can be adopted. One is better recycling strategy and technology; whilst the other is design for recycling (DFR). Recyclability assessment is one of the most important contents in DFR. First, this paper discusses the content of DFR, and strategies and types related to product recycling, and points out that whether recyclability is easy or difficult depends on the product design. Then the methods and the procedure of recyclability assessment based on artificial neural network are explored in detail. Finally, a case study shows that this method is simple and operable.

We study the statistical properties of fluid flows in percolation porous media at very low Reynolds number by numerical simulations for 20,000 different configurations. It is shown that there are some important differences between fluid flows in macroscopically homogeneous and fractal porous media. For fluid flows in macroscopically homogeneous media, the pressure is definite, but the velocity is random and depends on the structural details of porous media. The permeability k for fluid flows in fractal changes with the size L as k∼L−α where α≈1.0, not approaching the constant expected from Darcy's law. The statistical distribution of pressure in fractal is independent of the size L and can be approximated by a function of the triangular shape.

The reaction mechanism for the dissociative adsorption of molecular chlorine Cl 2 on Si(100) has been studied by density functional theory calculations. The direct activated channel with one end of Cl 2 bonded to a surface Si atom to form a terminal adsorbate dominates the channels studied. The energy barrier for the subsequent complete dissociation from such an adsorbate depends on the adsorption site, and the ordering of the energy barriers can account for the ordering of the statistical weights observed in STM experiments. Our results also fill in the details of the reaction mechanisms as previously probed by a molecular beam surface scattering experiment.

The potential of density functional theory (DFT/PW91)-based ab initio molecular dynamics (MD) method for a priori elucidation and direct simulation of chemical reaction without an empirical potential energy surface was tested for the unimolecular thermal dissociation of acetic acid. The accuracy of DFT calculations with PW91 and BLYP types of functionals for energy barriers was confirmed, and within a limited number (32) of trajectories, all three dissociation channels were identified. Internal rotations in acetic acid were found to facilitate H migration among functional groups, which played an important role in its dissociation.

The hydrolysis of Cl 2 in water and its reverse reaction, modeled by a box containing one Cl 2 and 19 H 2 O molecules, are studied by ab initio molecular dynamics. It is observed that the hydrolysis reaction involves only Cl 2 and one H 2 O molecule directly in bond breaking and formation and that solvated chloride and hydrogen ions are directly produced in the hydrolysis. For the reverse reaction between HCl and HOCl, the approaching HOCl must first penetrate a solvation shell of water around the chloride ion, and the calculated free energy barrier is approximately 4.6 kcal mol −1 , much lower than that in the gas phase. The presence of (H 2 OCl) + ⋯Cl − is identified when the Cl–Cl distance is constrained at 2.2 or 2.4 A.

Ab initio molecular dynamics simulations are performed for the reaction between HCl and HOCl on an ice surface. The interaction between the two chlorine atoms, and the role played by the acidic dissociation of HCl, are elucidated. The catalytic effect of the ice surface is reproduced and explained.

The formation and desorption mechanisms of SiCl4 on Si(100), which are some of the main reaction pathways in etching silicon with chlorine, have been studied with first-principle calculations. Starting from the Si(100)-(2×1) surface with 1 ML (monolayer) of chlorine to saturate the dangling bonds of SiSi dimers, further addition of chlorine atoms leads to the breaking of the dimer bonds or back-bonds and, therefore, formation of SiCl2(ad) which has a high desorption energy barrier. When chlorine atoms penetrate below the first silicon layer, they couple with SiCl2(ad) to form various precursors of SiCl4(ad). The calculations have identified three physisorbed SiCl4(ad) species, and four chemisorbed SiCl4(ad) species. The formation and desorption barriers of these species are lower than that of SiCl2(ad). Hence, at low temperatures, SiCl2 desorption is barred by its high desorption energy, which makes the formation and desorption of SiCl4 more favorable. At high temperatures, desorption of SiCl2 can proceed and thus reduces the probability of SiCl4(ad) formation. The availability of hyperthermal chlorine species or ion bombardment can promote both the formation of SiCl4(ad) and desorption of SiCl2(ad), and thereby increases the etching rate and changes the reaction dynamics. A mild energetic condition will favor the formation and desorption of SiCl4. The etching mechanism model provides a good framework to explain the experimental data in the literature on etching silicon with thermal and hyperthermal chlorine.