Side chain engineering of naphthalene diimide–bithiophene‐based polymer acceptors in all‐polymer solar cells

Two novel polymeric acceptors based on naphthalene diimide (NDI) and 2.2′‐bithiophene, named as P(NDI2THD‐T2) and P(NDI2TOD‐T2), were designed and synthesized for all polymer solar cells application. The structural and electronic properties of the two acceptors were modulated through side‐chain engineering of the NDI units. The optoelectronic properties of the polymers and the morphologies of the blend films composed of the polymer acceptors and a donor polymer PTB7‐Th were systemically investigated. With thiophene groups introduced into the side chains of the NDI units, both polymers showed wider absorption from 350 nm to 900 nm, compared with the reference polymer acceptor of N2200. No redshift of absorption spectra from solutions to films indicated reduced aggregation of the polymers due to the steric hindrance effect of thiophene rings in the side chains. The photovoltaic performance were characterized for devices in a configuration of ITO/PEDOT:PSS/PTB7‐Th:acceptors/2,9‐bis(3‐(dimethylamino)propyl)anthra[2,1,9‐def:6,5,10‐def]diisoquinoline‐1,3,8,10(2H,9H)‐tetraone (PDIN)/Al. With the addition of diphenyl ether as an additive, the power conversion efficiencies (PCEs) of 2.73% and 4.75% for P(NDI2THD‐T2) and P(NDI2TOD‐T2) based devices were achieved, respectively. The latter showed improved J_sc, Fill Factor (FF), and PCE compared with N2200 based devices. © 2017 Wiley Periodicals, Inc. J. Polym. Sci., Part A: Polym. Chem. 2017 , 55, 3679–3689     

复杂换热网络MINLP中的非线性特性分析

针对复杂换热网络MINLP问题,本文应用多维函数渐变法探索和分析了连续变量和整型变量引起的网络性能非线性和非凸特性。研究表明,当系统规模增大时,不但连续变量的叠加效应将增加系统的非线性,整型变量将使得非线性特性更为严重。     

Bursting synchronization in networks with long-range coupling mediated by a diffusing chemical substance

Many networks of physical and biological interest are characterized by a long-range coupling mediated by a chemical which diffuses through a medium in which oscillators are embedded. We considered a one-dimensional model for this effect for which the diffusion is fast enough so as to be implemented through a coupling whose intensity decays exponentially with the lattice distance. In particular, we analyzed the bursting synchronization of neurons described by two timescales (spiking and bursting activity), and coupled through such a long-range interaction network. One of the advantages of the model is that one can pass from a local (Laplacian) type of coupling to a global (all-to-all) one by varying a single parameter in the interaction term. We characterized bursting synchronization using an order parameter which undergoes a transition as the coupling parameters are changed through a critical value. We also investigated the role of an external time-periodic signal on the bursting synchronization properties of the network. We show potential applications in the control of pathological rhythms in biological neural networks.     

Optimization of neural network for ionic conductivity of nanocomposite solid polymer electrolyte system (PEO-LiPF6-EC-CNT)

In this study, the ionic conductivity of a nanocomposite polymer electrolyte system (PEO-LiPF₆-EC-CNT), which has been produced using solution cast technique, is obtained using artificial neural networks approach. Several results have been recorded from experiments in preparation for the training and testing of the network. In the experiments, polyethylene oxide (PEO), lithium hexafluorophosphate (LiPF₆), ethylene carbonate (EC) and carbon nanotubes (CNT) are mixed at various ratios to obtain the highest ionic conductivity. The effects of chemical composition and temperature on the ionic conductivity of the polymer electrolyte system are investigated. Electrical tests reveal that the ionic conductivity of the polymer electrolyte system varies with different chemical compositions and temperatures. In neural networks training, different chemical compositions and temperatures are used as inputs and the ionic conductivities of the resultant polymer electrolytes are used as outputs. The experimental data is used to check the system’s accuracy following the training process. The neural network is found to be successful for the prediction of ionic conductivity of nanocomposite polymer electrolyte system.     

Mechanistic model for deformation of polymer nanocomposite melts under large amplitude shear

We report the mechanical response of a model nanocomposite system of poly(styrene) (PS)-silica to large-amplitude oscillatory shear deformations. Nonlinear behavior of PS nanocomposites is discussed with the changes in particle dispersion upon deformation to provide a complete physical picture of their mechanical properties. The elastic stresses for the particle and polymer are resolved by decomposing the total stress into its purely elastic and viscous components for composites at different strain levels within a cycle of deformation. We propose a mechanistic model which captures the deformation of particles and polymer networks at small and large strains, respectively. We show, for the first time, that chain stretching in a polymer nanocomposite obtained in large amplitude oscillatory deformation is in good agreement with the nonlinear chain deformation theory of polymeric networks. © 2013 Wiley Periodicals, Inc. J Polym Sci Part B: Polym Phys, 2013     

Poly(2‐oxazoline) Hydrogels: State‐of‐the‐Art and Emerging Applications

The synthesis of poly(2‐oxazoline)s has been known since the 1960s. In the last two decades, they have risen in popularity thanks to improvements in their synthesis and the realization of their potential in the biomedical field due to their “stealth” properties, stimuli responsiveness, and tailorable properties. Even though the bulk of the research to date has been on linear forms of the polymer, they are also of interest for creating network structures due to the relatively easy introduction of reactive functional groups during synthesis that can be cross‐linked under a variety of conditions. This opinion article briefly reviews the history of poly(2‐oxazoline)s and examines the in vivo data on soluble poly(2‐oxazoline)s to date in an effort to predict how hydrogels may perform as implantable materials. This is followed by an overview of the most recent hydrogel synthesis methods and emerging applications, and is concluded with a section on the future directions predicted for these fascinating yet underutilized polymers.     

“Kick‐Starting” oxetane photopolymerizations

In the presence of small amounts of 2,2‐dialkyl‐, 2,2,3‐trialkyl‐, or 2,2,3,3‐tetraalkyl substituted epoxides such as isobutylene oxide, 1,2‐limonene oxide, and 2,2,3,3,‐tetramethyl oxirane, the photoinitiated cationic ring‐opening polymerizations of 3,3‐disubstituted oxetanes are dramatically accelerated. The acceleration affect was attributed to an increase in the rate of the initiation step of these latter monomers. Both mono‐ and disubstituted oxetane monomers are similarly accelerated by the above‐mentioned epoxides to give crosslinked network polymers. The potential for the use of such “kick‐started” systems in applications such as coatings, adhesives, printing inks, dental composites and in three‐dimensional imaging is discussed. © 2014 Wiley Periodicals, Inc. J. Polym. Sci., Part A: Polym. Chem. 2014 , 52, 2934–2946     

The construction and approximation of feedforward neural network with hyp erb olic tangent function

In this paper, we discuss some analytic properties of hyperbolic tangent function and estimate some approximation errors of neural network operators with the hyperbolic tangent activation functionFirstly, an equation of partitions of unity for the hyperbolic tangent function is givenThen, two kinds of quasi-interpolation type neural network operators are constructed to approximate univariate and bivariate functions, respectivelyAlso, the errors of the approximation are estimated by means of the modulus of continuity of functionMoreover, for approximated functions with high order derivatives, the approximation errors of the constructed operators are estimated.     

半度量空间的刻画

We use the notions of weak base g-functions and pair networks to give some new characterizations of semi-metric spaces.     

Chemical data,ways and means,past and future

A brief outline of various data handling methods, from linear learning machines, principal component analysis, experimental design, and modeling to visualization, optimization, and validation together with a personal view on the historical development of the use of these methods, is given. Some future trends in handling chemical data are proposed as well. Copyright © 2014 John Wiley & Sons, Ltd.