Direct visualization of structural defects in 2D semiconductors

Direct visualization of the structural defects in two-dimensional (2D) semiconductors at a large scale plays a significant role in understanding their electrical/optical/magnetic properties, but is challenging. Although traditional atomic resolution imaging techniques, such as transmission electron microscopy and scanning tunneling microscopy, can directly image the structural defects, they provide only local-scale information and require complex setups. Here, we develop a simple, non-invasive wet etching method to directly visualize the structural defects in 2D semiconductors at a large scale, including both point defects and grain boundaries. Utilizing this method, we extract successfully the defects density in several different types of monolayer molybdenum disulfide samples, providing key insights into the device functions. Furthermore, the etching method we developed is anisotropic and tunable, opening up opportunities to obtain exotic edge states on demand.     

Shedding vortex simulation method based on viscous compensation technology research

Owing to the influence of the viscosity of the flow field, the strength of the shedding vortex decreases gradually in the process of backward propagation. Large-scale vortexes constantly break up, forming smaller vortexes. In engineering, when numerical simulation of vortex evolution process is carried out, a large grid is needed to be arranged in the area of outflow field far from the boundary layer in order to ensure the calculation efficiency. As a result, small scale vortexes at the far end of the flow field cannot be captured by the sparse grid in this region, resulting in the dissipation or even disappearance of vortexes. In this paper, the effect of grid scale is quantified and compared with the viscous effect through theoretical derivation. The theoretical relationship between the mesh viscosity and the original viscosity of the flow field is established, and the viscosity term in the turbulence model is modified. This method proves to be able to effectively improve the intensity of small-scale shedding vortexes at the far end of the flow field under the condition of sparse grid. The error between the simulation results and the results obtained by using fine mesh is greatly reduced, the calculation time is shortened, and the high-precision and efficient simulation of the flow field is realized.     

Optimization of heterogeneous Catalyst-assisted fatty acid methyl esters biodiesel production from Soybean oil with different Machine learning methods

There is a growing attention to the bio and renewable energies due to fast depletion of fossil fuels as well as the global warming problem. Here, we developed a modeling and simulation method by means of artificial intelligence (AI) for prediction of the bioenergy production from vegetable bean oil. AI methods are well known for prediction of complex and nonlinear process. Three distinct Adaptive Boosted models including Huber regression, LASSO, and Support Vector Regression (SVR) as well as artificial neural network (ANN) were applied in this study to predict actual yield of Fatty acid methyl esters (FAME) production. All boosted utilizing the Adaptive boosting algorithm. The important influencing parameters on the biodiesel production such as the catalyst loading (CAO/Ag, wt%) and methanol to oil (Soybean oil) molar ratio were selected as the input variables of models while the yield of FAME production was selected as output. Model hyper-parameters were tuned to maintain generality while improving prediction accuracy. The models were evaluated using three distinct metrics Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R². Error rates of 8.16780E-01, 4.43895E-01, 2.06692E + 00, and 3.92713 E-01 were obtained with the MAE metric for boosted Huber, SVR, LASSO and ANN models. On the other hand, the RMSE error of these models were about 1.092E-02, 1.015E-02, 2.669E-02, and 1.01174E-02, respectively. Finally, the R-square score were calculated for boosted Huber, boosted SVR, and boosted LASSO as 0.976, 0.990, 0.872, and 0.99702, respectively. Therefore, it can be concluded that although the boosted SVR and ANN models were better models for prediction of process efficiency in terms of error, but all algorithms had high accuracy. The optimum yield of 83.77% and 81.60% for biodiesel production were observed at optimum operating values from boosted SVR and ANN models, respectively.     

Generalized Jacobi spectral Galerkin method for fractional pantograph differential equation

This work is concerned with the extension of the Jacobi spectral Galerkin method to a class of nonlinear fractional pantograph differential equations. First, the fractional differential equation is converted to a nonlinear Volterra integral equation with weakly singular kernel. Second, we analyze the existence and uniqueness of solutions for the obtained integral equation. Then, the Galerkin method is used for solving the equivalent integral equation. The error estimates for the proposed method are also investigated. Finally, illustrative examples are presented to confirm our theoretical analysis.     

A Structural Entropy Measurement Principle of Propositional Formulas in Conjunctive Normal Form

The satisfiability (SAT) problem is a core problem in computer science. Existing studies have shown that most industrial SAT instances can be effectively solved by modern SAT solvers while random SAT instances cannot. It is believed that the structural characteristics of different SAT formula classes are the reasons behind this difference. In this paper, we study the structural properties of propositional formulas in conjunctive normal form (CNF) by the principle of structural entropy of formulas. First, we used structural entropy to measure the complex structure of a formula and found that the difficulty solving the formula is related to the structural entropy of the formula. The smaller the compressing information of a formula, the more difficult it is to solve the formula. Secondly, we proposed a λ-approximation strategy to approximate the structural entropy of large formulas. The experimental results showed that the proposed strategy can effectively approximate the structural entropy of the original formula and that the approximation ratio is more than 92%. Finally, we analyzed the structural properties of a formula in the solution process and found that a local search solver tends to select variables in different communities to perform the next round of searches during a search and that the structural entropy of a variable affects the probability of the variable being flipped. By using these conclusions, we also proposed an initial candidate solution generation strategy for a local search for SAT, and the experimental results showed that this strategy effectively improves the performance of the solvers CCAsat and Sparrow2011 when incorporated into these two solvers.     

Mg,N掺杂β-Ga2O3光电性质的第一性原理计算

通过基于密度泛函理论的第一性原理计算,研究了Mg单掺杂、N单掺杂和不同浓度的Mg-N共掺杂β-Ga₂O₃的结构性质、电子性质和光学性质,以期获得性能比较优异的p型β-Ga₂O₃材料。建立了五种模型:Mg单掺杂、N单掺杂、1个Mg-N共掺杂、2个Mg-N共掺杂和3个Mg-N共掺杂β-Ga₂O₃。经过计算,3个Mg-N共掺杂β-Ga₂O₃体系的结构最稳定。此外,在5种模型中,3个Mg-N共掺杂β-Ga₂O₃体系的禁带宽度是最小的,并且N 2p和Mg 3s贡献的占据态抑制了氧空位的形成,从而增加了空穴浓度。因此,3个Mg-N共掺杂β-Ga₂O₃体系表现出优异的p型性质。3个Mg-N共掺杂体系的吸收峰出现明显红移,在太阳盲区的光吸收系数较大,这归因于导带Ga 4s、Ga 4p、Mg 3s向价带O 2p、N 2p的带间电子跃迁。本工作将为p型β-Ga₂O₃日盲光电材料的研究和应用提供理论指导。     

Experimental and numerical simulation of effects of CO2/N2 concentration and initial temperature on combustion characteristics of biomass syngas

Biomass syngas is a form of renewable energy with very broad application prospects, and it has different combustion characteristics according to the fuel composition and processing technology of biomass syngas. The influence of combustion composition, diluent and temperature variation on combustion characteristics were studied in this paper. The FFCM-1 mechanism was used to investigate the combustion characteristics of CO/CH₄/H₂ under varied diluents CO₂/N₂ and temperature by using spherical expansion flame method and ANSYS CHEMKIN-PRO. The experimental laminar burning velocity was compared with the simulation results of FFCM-1 mechanism. The results reveal that the experimental data are in good agreement with the simulation results, which are somewhat different under the condition of rich fuel. The laminar burning velocity decreases significantly with the increase of diluent CO₂/N₂, with the effect of diluent CO₂ being more significant. The laminar burning velocity increase dramatically with the increase of initial temperature, and the adiabatic flame temperature also decreases with the increase of diluent. The reduction caused by diluent CO₂ is much larger than that caused by diluent N₂. The change of initial temperature also affects the adiabatic flame temperature, but the range of variation is not as pronounced as that of diluent. Not only was the interaction between the combustion characteristics of CO/CH₄/H₂ under different diluents and temperature changes explored in this paper, but the influence mechanism was also revealed in depth.