A Novel Buffering Technique for Aqueous Processing of Zinc Oxide Nanostructures and Interfaces,and Corresponding Improvement of Electrodeposited ZnO‐Cu2O Photovoltaics

A novel buffering method is presented to improve the stability of zinc oxide processed in aqueous solutions. By buffering the aqueous solution with a suitable quantity of sacrificial zinc species, the dissolution of functional zinc oxide structures and the formation of unwanted impurities can be prevented. The method is demonstrated for ZnO films and nanowires processed in aqueous solutions used for the selective etching of mesoporous anodic alumina templates and the electrochemical deposition of Cu₂O. In both cases, improved ZnO stability is observed with the buffering method. ZnO‐Cu₂O heterojunction solar cells (bilayer and nanowire cells) synthesized using both traditional and buffered deposition methods are characterized by impedance spectroscopy and solar simulation measurements. Buffering the Cu₂O deposition solution is found to reduce unwanted recombination at the heterojunction and improve the photovoltaic performance.     

Intraphase Microstructure–Understanding the Impact on Organic Solar Cell Performance

A comprehensive study of the effect of intraphase microstructure on organic photovoltaic (OPV) device performance is undertaken. Utilizing a bilayer device architecture, a small molecule donor (TIPS‐DBC) is deposited by both spin‐coating and by thermal evaporation in vacuum. The devices are then completed by thermal evaporation of C₆₀, an exciton blocking layer and the cathode. This bilayer approach enables a direct comparison of device performance for donor layers in which the same material exhibits subtle differences in microstructure. The electrical performance is shown to differ considerably for the two devices. The bulk and interfacial properties of the donor layers are compared by examination with photoelectron spectroscopy in air (PESA), optical absorption spectroscopy, charge extraction of photo‐generated charge carriers by linearly increasing voltage (photo‐CELIV), time‐resolved photoluminescence measurements, X‐ray reflectometry (XR), and analysis of dark current behavior. The observed differences in device performance are shown to be influenced by changes to energy levels and charge transport properties resulting from differences in the microstructure of the donor layers. Importantly, this work demonstrates that in addition to the donor/acceptor microstructure, the intraphase microstructure can influence critical parameters and can therefore have a significant impact on OPV performance.     

Numerical Modeling of Multimaterial Thermoelectric Devices Under Static and Cyclic Thermal Loading

Selection of materials for thermoelectric devices is generally based on a figure of merit that is a function of the Seebeck coefficient, electrical conductivity, and thermal conductivity. While this figure of merit is a useful metric for comparing materials, the relative importance of the constituent properties depends on the particular application and conditions. In addition, multiple materials can be used together to improve the performance or extend the operating range, and determining the performance of such multimaterial combinations requires analysis beyond simply averaging the properties of the constituent materials. In this paper, finite-element numerical simulations under static and cyclic thermal loadings are used to investigate how device performance can be improved by judicious location of the different materials within the device. The results show that the performance of a device with two different materials can be better than that of either of the individual materials. The greatest improvement in performance occurs with cyclic heating, where the overall performance is strongly influenced by the behavior under transient conditions during heating and cooling.     

Photoresponsive Sponge‐Like Coating for On‐Demand Liquid Release

Many publications report on stimuli responsive coatings, but only a few on the controlled release of species in order to change the coating surface properties. A sponge‐like coating that is able to release and absorb a liquid upon exposure to light has been developed. The morphology of the porous coating is controlled by the smectic liquid crystal properties of the monomer mixture prior to its polymerization, and homeotropic order is found to give the largest contraction. The fast release of the liquid can be induced by a macroscopic contraction of the coating caused by a trans to cis conversion of a copolymerized azobenzene moiety. The liquid secretion can be localized by local light exposure or by creating a surface relief. The uptake of liquid proceeds by stimulating the back reaction of the azo compound by exposure at higher wavelength or by thermal relaxation. The surface forces of the sponge‐like coating in contact with an opposing surface can be controlled by light‐induced capillary bridging revealing that the controlled release of liquid gives access to tunable adhesion.     

Learning from Constraints for Formal Property Checking

Constraints are commonly used in both simulation and formal verification in order to specify expected input conditions and state transitions. Constraint solving is a process to determine input vectors which satisfy the set of constraints during constrained random simulation. Even though constraints are used in formal property checking to restrict the search space, constraint solving has never had direct application to formal property checking. There are often many simple, yet powerful, invariants that can be learned from constraint solving during constrained random simulation. These invariants are shown in this paper to significantly simplify the formal verification problem. We use approximate constraint solving to compute an approximate set of valid input vectors. The approximate set of valid input vectors are a strict superset of the set of all legal input vectors. We use BDD techniques to compute these input vectors during constrained random simulation, then process the resulting BDDs for learning invariants which can be used during formal property checking. This paper presents efficient BDD algorithms to learn invariants from the BDDs generated from approximate constraint solving. We also present how these learned invariants can be applied to the formal property checking. Experimental results show that invariants learned during constraint solving can significantly improve the performance of formal property checking with many industrial designs.     

云计算概念、模型和关键技术

云计算指IP技术架构下的网络计算,其本质是ICT业务的一种新的应用方式。绝大数企业和运营商的数据中心的改造将是云计算未来发展的主要任务：使云计算技术更为普遍和更为广泛地为绝大部分企业、机构、团体和运营商服务。云计算技术将不仅提供传统意义的IT资源和应用服务,而且将支持包括IT、通信、电视、移动和物联等一切互联网技术融合后的资源使用和业务应用。云计算发展的关键技术主要有统一交换构架、统一虚拟化和统一计算系统,云计算发展的战略推手将是组建开放产业联盟和推动开放技术标准。     

High field transport in GaAs,InP and InAs

Calculations of the steady state and transient electron drift velocities and impact ionization rate are presented for GaAs, InP and InAs based on a Monte Carlo simulation using a realistic band structure derived from an empirical pseudopotential. The impact ionization results are obtained using collision broadening of the initial state and are found to fit the experimental data well through a wide range of applied fields. In InP the impact ionization rate is much lower than in GaAs and no appreciable anisotropy has been observed. This is due in part to the larger density of states in InP and the corresponding higher electron-phonon scattering rate. The transient drift velocities are calculated under the condition of high energy injection. The results for InP show that higher velocities can be obtained over 1000–1500 Å device lengths for a much larger range of launching energies and applied electric fields than in GaAs. For the case of InAs, due to the large impact ionization rate, high drift velocities can be obtained since the ionization acts to limit the transfer of electrons to the satellite minima. In the absence of impact ionization, the electrons show the usual runaway effect and transfer readily occurs, thus lowering the drift velocity substantially.     

Factoring Algorithms for Computing K-Terminal Network Reliability

Let GK denote a graph G whose edges can fail and with a set K ? V specified. Edge failures are independent and have known probabilities. The K-terminal reliability of GK, R(GK), is the probability that all vertices in K are connected by working edges. A factoring algorithm for computing network reliability recursively applies the formula R(GK) = piR(GK * ei) + qiR(GK - ei) where GK * ei is GK, with edge ei contracted, GK - ei is GK with ei deleted and pi ? 1 - qi is the reliability of edge ei. Various reliability-preserving reductions can be performed after each factoring operation in order to reduce computation. A unified framework is provided for complexity analysis and for determining optimal factoring strategies. Recent results are reviewed and extended within this framework.