Efficient and 1,8-diiodooctane-free ternary organic solar cells fabricated via nanoscale morphology tuning using small-molecule dye additive

The ternary strategy for incorporating multiple photon-sensitive components into a single junction has emerged as an effective method for optimizing the nanoscale morphology and improving the device performance of organic solar cells (OSCs).In this study,efficient and stable ternary OSCs were achieved by introducing the small-molecule dye (5E,5'E)-5,5'-(4',4″-(1,2-diphenylethene-1,2-diyl)bis(biphenyl-4',4-diyl))bis(methan-1-yl-1-ylidene)bis(3-ethyl-2-thioxothia zolidin-4-one) (BTPERn) into poly[4,8-bis(5-(2-ethylhexyl)thiophen-2-yl)benzo[1,2-b:4,5-b']dithiopheneco-3-fluorothieno[3,4-b]thiophene-2-carboxylate] (PTB7-Th):[6,6]-phenyl C71 butyric acid methyl ester (PC71BM) blend films processed using a 1,8-diiodooctane (DIO)-free solvent.The incorporation of BTPE-Rn enhanced the short-circuit current density and fill factor of the ternary OSCs compared with those of binary OSCs.An investigation of the optical,electronic,and morphological properties of the ternary blends indicated that the third component of BTPE-Rn not only promoted the photon utilization of blends through the energy-transfer process but also improved the electron mobility of the blends owing to the fullerene-rich nanophase optimization.More importantly,this ternary strategy of utilizing a small-molecule dye to replace the photounstable DIO additive enhanced the operational stability of the OSCs.     

Nanowire encapsulation with polymer for electrical isolation and enhanced optical properties

Light management and electrical isolation are essential for the majority of optoelectronic nanowire (NW) devices.Here,we present a cost-effective technique,based on vapor-phase deposition of parylene-C and subsequent annealing,that provides conformal encapsulation,anti-reflective coating,improved optical properties,and electrical insulation for GaAs nanowires.The process presented allows facile encapsulation and insulation that is suitable for any nanowire structure.In particular,the parylene-C encapsulation functions as an efficient antireflection coating for the nanowires,with reflectivity down to ＜1％ in the visible spectrum.Furthermore,the parylene-C coating increases photoluminescence intensity,suggesting improved light guiding to the NWs.Finally,based on this process,a NW LED was fabricated,which showed good diode performance and a clear electroluminescence signal.We believe the process can expand the fabrication possibilities and improve the performance of optoelectronic nanowire devices.     

Challenges in Liquid‐Phase Exfoliation,Processing, and Assembly of Pristine Graphene

Recent developments in the exfoliation, dispersion, and processing of pristine graphene (i.e., non‐oxidized graphene) are described. General metrics are outlined that can be used to assess the quality and processability of various “graphene” products, as well as metrics that determine the potential for industrial scale‐up. The pristine graphene production process is categorized from a chemical engineering point of view with three key steps: i) pretreatment, ii) exfoliation, and iii) separation. How pristine graphene colloidal stability is distinct from the exfoliation step and is dependent upon graphene interactions with solvents and dispersants are extensively reviewed. Finally, the challenges and opportunities of using pristine graphene as nanofillers in polymer composites, as well as as building blocks for macrostructure assemblies are summarized in the context of large‐scale production.     

Microstructure and Processing of 3D Printed Tungsten Microlattices and Infiltrated W–Cu Composites

Tungsten is of industrial relevance due its outstanding intrinsic properties (e.g., highest melting‐point of all elements) and therefore difficult to 3D‐print by conventional methods. Here, tungsten micro‐lattices are produced by room‐temperature extrusion‐based 3D‐printing of an ink comprising WO₃–0.5%NiO submicron powders, followed by H₂‐reduction and Ni‐activated sintering. The green bodies underwent isotropic linear shrinkage of ≈50% during the thermal treatment resulting in micro‐lattices, with overall 35–60% open‐porosity, consisting of 95–100% dense W–0.5%Ni struts having ≈80–300 μm diameter. Ball‐milling the powders and inks reduced the sintering temperature needed to achieve full densification from 1400 to 1200 °C and enabled the ink to be extruded through finer nozzles (200 μm). Partial sintering of the struts is achieved when NiO is omitted from the ink, with submicron interconnected‐porosity of ≈34%. Several tungsten micro‐lattices are infiltrated with molten copper at 1300 °C under vacuum, resulting in dense, anisotropic W–Cu composites with 40–65% tungsten volume fraction. Partially sintered struts (containing nickel) with submicron open porosity are also infiltrated with Cu, resulting in co‐continuous W–Cu composites with wide W struts/Cu channels at the lattice scale (hundreds of micrometers), and fine W–Cu interpenetrating network at the strut scale (hundreds of nanometers) allowing for the design of anisotropic mechanical and electrical properties.

     

Visual information processing in primate cone pathways. I. A model

At the retinal level, the strategies utilized by biological visual systems allow them to outperform machine vision systems, serving to motivate the design of electronic or "smart" sensors based on similar principles. Design of such sensors in silicon first requires a model of retinal information processing which captures the essential features exhibited by biological retinas. In this paper, a simple retinal model is presented, which qualitatively accounts for the achromatic information processing in the primate cone system. The computer retina model exhibits many of the properties found in biological retinas such as data reduction through nonuniform sampling, adaptation to a large dynamic range of illumination levels, variation of visual acuity with illumination level, and enhancement of spatiotemporal contrast information. The main emphasis of the model presented here is to demonstrate how different adaptation mechanisms play a role in extending the operating range of the primate retina.     

Detection and quantification of valve stiction by the method of unknown input estimation

Valve stiction is one of the most common causes of oscillations in industrial process control loops. Such oscillations can degrade the overall performance of the loop and eventually the final product quality. The detection and quantification of valve stiction in industrial process control loops is thus important. From previous studies in the literature, a sticky valve has been shown to have a distinct signature of the stiction phenomena in its valve positioner data. However, the position of the modulating control valves is seldom available. We consider the problem of estimating the valve position as an unknown input estimation problem. In this work, we propose a novel application of the unknown input estimator in order to estimate the valve position given the process model and the data of the process variable and controller output. Using the estimated valve position, we can detect and also quantify the amount of stiction. We demonstrate the efficacy of the method through simulation examples where a sticky valve is deliberately introduced in the closed loop using a two-parameter stiction model available in the literature. Application of the proposed methodology to a laboratory scale flow control loop is presented. An industrial case study is also presented in which the algorithm accurately detects and quantifies stiction in the level control loop of a power plant.     

The modeling of mode choices of intercity freight transportation with the artificial neural networks and adaptive neuro-fuzzy inference system

Mode choice modeling is probably the most important element of transportation planning. It affects the general efficiency of travel and the allocation of resources. The development of mode choice models has recently witnessed significant advances in many fields, such as passenger and freight transport. A large number of mathematical models have been used to model the traveler’s choice of mode and destination and the shipper’s choice of mode, shipment size and supply market, among others. Such models are not only becoming almost intractable but also data intensive, difficult to calibrate and update, and intransferable. These models cover a wide range of mathematical complexity and accuracy. This paper describes a new approach to mode choice of intercity freight transport modeling using artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS) models. The new approach combines the learning ability of artificial neural networks and the transparent nature of fuzzy logic. The approach is found to be highly adaptive and efficient in investigating non-linear relationships among different variables. The adaptive neuro-fuzzy inference system model is tested on the freight transport market in Turkey, Germany, France and Austria by using information on the freight flows and their attributes. The ANNs and ANFIS models are more successful in the representation of the non-linear behavior of mode choice of intercity freight transport compared to the classical models.     

Consistency of noise covariance estimation in joint input–output closed-loop subspace identification with application in LQG benchmarking

The LQG trade-off curve has been used as a benchmark for control loop performance assessment. The subspace approach to estimating the LQG benchmark has been proposed in the literature which requires certain intermediate matrices in subspace identification as well as the covariance matrix of the noise. It is shown in this paper that many existing closed-loop identification methods do not give a consistent estimate of the noise covariance matrix. As a result, we propose an alternative subspace formulation for the joint input–output closed-loop identification for which the consistency of the required subspace matrices and noise covariance is guaranteed. Simulation studies and experimental results are provided to demonstrate the utility of the proposed method.     

An incremental neural network for tissue segmentation in ultrasound images

This paper presents an incremental neural network (INeN) for the segmentation of tissues in ultrasound images. The performances of the INeN and the Kohonen network are investigated for ultrasound image segmentation. The elements of the feature vectors are individually formed by using discrete Fourier transform (DFT) and discrete cosine transform (DCT). The training set formed from blocks of 4x4 pixels (regions of interest, ROIs) on five different tissues designated by an expert is used for the training of the Kohonen network. The training set of the INeN is formed from randomly selected ROIs of 4x4 pixels in the image. Performances of both 2D-DFT and 2D-DCT are comparatively examined for the segmentation of ultrasound images.