基于氟代异靛蓝并[7,6-g]异靛蓝的共轭聚合物的合成及其半导体性质

采用Stille缩聚,合成了3个异靛蓝并[7,6-g]异靛蓝(DIID)和乙烯单元交替排列的共轭聚合物P0F、P2F和P4F,三者在DIID单元中分别含0、2和4个氟原子(F).3个聚合物均具有良好的平面性,前线分子轨道几乎在整个共轭骨架上离域.它们均具有宽的吸收光谱,吸收范围在400~1000 nm,光学带隙约为1.25 eV;随着氟原子数目的增加,聚合物的最高占有分子轨道(HOMO)和最低空分子轨道(LUMO)能级依次下降0.1~0.2 eV.以这3个聚合物作为活性层,制备了顶栅-底接触型有机场效应晶体管器件,随着氟原子数目的增加,聚合物的传输性质由双极传输变为n型传输.P0F和P2F是双极传输型聚合物,空穴迁移率(μ_h)分别达到0.11和0.30 cm~2 V~(-1) s~(-1),电子迁移率(me)分别达到0.22和1.19 cm~2 V~(-1) s~(-1).P4F是n型聚合物,me达到0.18 cm~2 V~(-1) s~(-1).     

Unsupervised and semi-supervised extreme learning machine with wavelet kernel for high dimensional data

Extreme learning machine (ELM) not only is an effective classifier in supervised learning, but also can be applied on unsupervised learning and semi-supervised learning. The model structure of unsupervised extreme learning machine (US-ELM) and semi-supervised extreme learning machine (SS-ELM) are same as ELM, the difference between them is the cost function. We introduce kernel function to US-ELM and propose unsupervised extreme learning machine with kernel (US-KELM). And SS-KELM has been proposed. Wavelet analysis has the characteristics of multivariate interpolation and sparse change, and Wavelet kernel functions have been widely used in support vector machine. Therefore, to realize a combination of the wavelet kernel function, US-ELM, and SS-ELM, unsupervised extreme learning machine with wavelet kernel function (US-WKELM) and semi-supervised extreme learning machine with wavelet kernel function (SS-WKELM) are proposed in this paper. The experimental results show the feasibility and validity of US-WKELM and SS-WKELM in clustering and classification.     

关于预不变凸函数的Hermite-Hadamard型不等式的一个注记

从预不变凸函数的定义和性质出发,用数学分析方法得到了由已有的预不变凸函数的Hermite-Hadamard型不等式的右端部分所决定的差函数的上界和下界.从而加细了预不变凸函数的Hermite-Hadamard型不等式.     

Generalized harmonic number summation formulae via hypergeometric series and digamma functions

By computing the derivatives of five classical hypergeometric summation theorems, and applying the related properties of the digamma function, we derive a large number of closed summation formulae for generalized harmonic numbers.     

New algebraic structures from Hermitian one-matrix model

Virasoro constraint is the operator algebra version of one-loop equation for a Hermitian one-matrix model, and it plays an important role in solving the model. We construct the realization of the Virasoro constraint from the Conformal Field Theory (CFT) method. From multi-loop equations of the one-matrix model, we get a more general constraint. It can be expressed in terms of the operator algebras, which is the Virasoro subalgebra with extra parameters. In this sense, we named as generalized Virasoro constraint. We enlarge this algebra with central extension, this is a new kind of algebra, and the usual Virasoro algebra is its subalgebra. And we give a bosonic realization of its subalgebra.     

A Sustainable Redox-Flow Battery with an Aluminum-Based,Deep-Eutectic-Solvent Anolyte

Nonaqueous redox-flow batteries are an emerging energy storage technology for grid storage systems, but the development of anolytes has lagged far behind that of catholytes due to the major limitations of the redox species, which exhibit relatively low solubility and inadequate redox potentials. Herein, an aluminum-based deep-eutectic-solvent is investigated as an anolyte for redox-flow batteries. The aluminum-based deep-eutectic solvent demonstrated a significantly enhanced concentration of circa 3.2 m in the anolyte and a relatively low redox potential of 2.2 V vs. Li⁺/Li. The electrochemical measurements highlight that a reversible volumetric capacity of 145 Ah L⁻¹ and an energy density of 189 Wh L⁻¹ or 165 Wh kg⁻¹ have been achieved when coupled with a I₃⁻/I⁻ catholyte. The prototype cell has also been extended to the use of a Br₂-based catholyte, exhibiting a higher cell voltage with a theoretical energy density of over 200 Wh L⁻¹. The synergy of highly abundant, dendrite-free, multi-electron-reaction aluminum anodes and environmentally benign deep-eutectic-solvent anolytes reveals great potential towards cost-effective, sustainable redox-flow batteries.     

In situ synthesis of poly(ethylene terephthalate-<Emphasis Type="Italic">co</Emphasis>-isophthalate)–SiO<Subscript>2</Subscript> nanocomposites and their optical properties

In this paper, poly(ethylene terephthalate-co-isophthalate) was prepared and a series of PETI/SiO₂ nanocomposites with different amounts of silica nanoparticles was synthesized by in situ polymerization. Optical properties of the resultant nanocomposites were investigated by WGW photoelectric haze meter and UV–Vis-NIR spectrophotometer. Chemical structure and its influence on spherulite size of nano-SiO₂ were characterized by Fourier transformed infrared spectra and polarized optical microscopy, respectively. The differential scanning calorimetry results reveal that the addition of nano-SiO₂ has an impact on the thermal performance of the nanocomposite. Different proportions of SiO₂ were compared and the degree of crystallinity was found to have influence on optical properties of as-prepared nanocomposites. In addition, the results indicate that the low loading levels of SiO₂ can improve the optical properties of the composite and an optimum content was selected. Possible reasons for this enhanced role might be the different combination structure of the nanocomposites when the amount of SiO₂ varied.     

An efficient multi-objective PSO algorithm assisted by Kriging metamodel for expensive black-box problems

The huge computational overhead is the main challenge in the application of community based optimization methods, such as multi-objective particle swarm optimization and multi-objective genetic algorithm, to deal with the multi-objective optimization involving costly simulations. This paper proposes a Kriging metamodel assisted multi-objective particle swarm optimization method to solve this kind of expensively black-box multi-objective optimization problems. On the basis of crowding distance based multi-objective particle swarm optimization algorithm, the new proposed method constructs Kriging metamodel for each expensive objective function adaptively, and then the non-dominated solutions of the metamodels are utilized to guide the update of particle population. To reduce the computational cost, the generalized expected improvements of each particle predicted by metamodels are presented to determine which particles need to perform actual function evaluations. The suggested method is tested on 12 benchmark functions and compared with the original crowding distance based multi-objective particle swarm optimization algorithm and non-dominated sorting genetic algorithm-II algorithm. The test results show that the application of Kriging metamodel improves the search ability and reduces the number of evaluations. Additionally, the new proposed method is applied to the optimal design of a cycloid gear pump and achieves desirable results.     

Optimal Sliding Mode Robust Control for Fractional-Order Systems with Application to Permanent Magnet Synchronous Motor Tracking Control

This paper presents an optimal sliding mode output tracking control scheme for a class of fractional-order uncertain systems. Firstly, an augmented fractional-order system, composed of the original system and the external system, is constructed to transform the optimal output tracking issue into the design problem of linear quadratic regulator. The optimal tracking control problem for the nominal augmented fractional-order system is then studied. Secondly, the fractional-integral sliding mode controller is introduced to robustify the augmented fractional-order system, which satisfy the matching conditions. As a result, the original system output can track the external system output trajectory effectively even the uncertainties exist. Finally, the developed design techniques are applied to the tracking control of fractional-order permanent magnet synchronous motor. The simulation results demonstrate the validity of this approach.