Two different exchange-correlation functionals with van der Waals correction are employed to investigate the electronic properties of 2D heterostructure with a special configuration of Moiré pattern. It is found that, for both PBE and HSE06 functional, the new van der Waals heterostructure that consists of monolayer and 2D lepidocrocite-type exhibits a type-II band alignment between the and layers, and the electronic structures of monolayer and 2D are well retained in their respective layers due to a weak interlaminar coupling, which indicates that the new heterostructure may have potential applications in many fields such as photocatalysis, photoelectric devices, solar cells, etc. Furthermore, a novel design scheme based on the new 2D van der Waals heterostructure is proposed for a solar cell, and the corresponding power conversion efficiency is estimated to be about 8%.
The purpose of this paper is to establish a direct methanol fuel cell (DMFC) prediction model by using the support vector regression (SVR) approach combined with particle swarm optimization (PSO) algorithm for its parameter selection. Two variables, cell temperature and cell current density were employed as input variables, cell voltage value of DMFC acted as output variable. Using leave-one-out cross-validation (LOOCV) test on 21 samples, the maximum absolute percentage error (APE) yields 5.66%, the mean absolute percentage error (MAPE) is only 0.93% and the correlation coefficient (R 2 ) as high as 0.995. Compared with the result of artificial neural network (ANN) approach, it is shown that the modeling ability of SVR surpasses that of ANN. These suggest that SVR prediction model can be a good predictor to estimate the cell voltage for DMFC system.
Studies have shown there are several process/geometry parameters affecting the mechanical properties of the carbon nanotubes/epoxy composites. The relationship between the response and process/geometry parameters is highly nonlinear and quite complicated. It is very valuable to have an accurate model to estimate the response under different process/geometry parameters. In this paper, the support vector regression (SVR) combined with particle swarm optimization (PSO) for its parameter optimization was employed to construct mathematical models for prediction of mechanical properties of the carbon nanotubes/epoxy composites according to an experimental data set. The leave-one-out cross-validation (LOOCV) test results by SVR models support that the generalization ability of SVR model is high enough. The statistical mean absolute percentage error for tensile strength, elongation and elastic modulus are 3.96%, 3.14% and 2.62%, the correlation coefficients (R 2 ) achieve as high as 0.991, 0.990 and 0.997, respectively. This study suggests that the established SVR model can be used to accurately foresee the mechanical properties of carbon nanotubes/epoxy composites and can be used to optimize designing or controlling of the experimental process/geometry in practice.
A novel design and analysis of a butt-coupler are presented to be coupled with an edge-emitting laser and a silicon waveguide. The coupler is fairly effective when used with an edge-emitting laser of a certain integrated size and a high refractive index core material. Butt-coupling is not sensitive to polarization as both TE and TM modes achieve similar efficiency. The laser alignment tolerance and silicon waveguide gap in both lateral and vertical positions were studied. Although the air gap between the laser and the silicon cladding reduces coupling efficiency greatly, gap filling can help solve this problem. This design is proved to be quite effective in dealing with high index contrast and huge size mismatch between the laser and semiconductor waveguide.
Traditional Chinese medicine (TCM) has been widely practiced and is considered as an alternative to conventional medicine. TCM herbal prescriptions contain a mixture of herbs that collectively exert therapeutic actions and modulating effects. Traditionally defined herbal properties, related to the pharmacodynamic, pharmacokinetic and toxicological, as well as physicochemical properties of their principal ingredients, have been used as the basis for formulating TCM multi-herb prescriptions. These properties are used in this work to develop a computer program for predicting whether a multi-herb recipe is a valid TCM prescription. This program is based on a statistical learning method, support vector machine (SVM), and it is trained by using 575 well-known TCM prescriptions and 1961 non-TCM recipes generated by random combination of TCM herbs. Testing results by using 72 well-known TCM prescriptions and 5039 non-TCM recipes showed that 73.6% of the TCM prescriptions and 99.9% of non-TCM recipes are correctly classified by this system. A further test by using 48 TCM prescriptions published in recent years found that 68.7% of these are correctly classified. These accuracies are comparable to those of SVM classification of other biological systems. Our study indicates the potential of SVM for facilitating the analysis of TCM prescriptions.
Abstract Long‐lifetime room‐temperature phosphorescence (RTP) materials are important for many applications, but they are highly challenging materials owing to the spin‐forbidden nature of triplet exciton transitions. Herein, a facile, quick and gram‐scale method for the preparation of ultralong RTP (URTP) carbon dots (CDs) was developed via microwave‐assisted heating of ethanolamine and phosphoric acid aqueous solution. The CDs exhibit the longest RTP lifetime, 1.46 s (more than 10 s to naked eye) for CDs‐based materials to date. The doping of N and P elements is critical for the URTP which is considered to be favored by a n→π* transition facilitating intersystem crossing (ISC) for effectively populating triplet excitons. In addition, possibilities of formation of hydrogen bonds in the interior of the CDs may also play a significant role in producing RTP. Potential applications of the URTP CDs in the fields of anti‐counterfeiting and information protection are proposed and demonstrated.
According to an experimental dataset under different process parameters, support vector regression (SVR) combined with particle swarm optimization (PSO) for its parameter optimization was employed to establish a mathematical model for prediction of the tensile strength of poly (lactic acid) (PLA)/graphene nanocomposites. Four variables, while graphene loading, temperature, time and speed, were employed as input variables, while tensile strength acted as output variable. Using leave-one-out cross validation test of 30 samples, the maximum absolute percentage error does not exceed 1.5%, the mean absolute percentage error (MAPE) is only 0.295% and the correlation coefficient [Formula: see text] is as high as 0.99. Compared with the results of response surface methodology (RSM) model, it is shown that the estimated errors by SVR are smaller than those achieved by RSM. It revealed that the generalization ability of SVR is superior to that of RSM model. Meanwhile, multifactor analysis is adopted for investigation on significances of each experimental factor and their influences on the tensile strength of PLA/graphene nanocomposites. This study suggests that the SVR model can provide important theoretical and practical guide to design the experiment, and control the intensity of the tensile strength of PLA/graphene nanocomposites via rational process parameters.
This study develops support vector regression (SVR) models for describing the complex nonlinear relationship between tribological properties (friction coefficient and wear rate) and experimental factors including load, content of filled nanoparticles and speed of relative sliding for the ultra high molecular weight polyetwearhylene composites filled with nano-ZnO particles (UHMWPE/nano-ZnO). The particle swarm optimization (PSO) algorithm is employed for optimizing the parameters of SVR models and obtaining the optimal process parameters for preparing UHMWPE/nano-ZnO. The comparison of results achieved by SVR and multivariable linear regression (MLR) exhibits the superior simulation accuracy and generalization performance of the SVR approach. Meanwhile, multifactor analysis is adopted for investigation on the significances of each experimental factor and their influences on the tribological properties of UHMWPE/nano-ZnO. This study suggests that the SVR is an efficient and novel approach in development of the UHMWPE/nano-ZnO with lower friction coefficient and perfect wear resistance.
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