Strong-segregation limit of the self-consistent field theory for diblock copolymer melts

The self-consistent field theory (SCFT) introduced by Helfand for diblock copolymer melts is expected to converge to the strong-segregation theory (SST) of Semenov in the asymptotic limit, $\chi N \rightarrow \infty$. However, past extrapolations of the lamellar/cylinder and cylinder/sphere phase boundaries, within the standard unit-cell approximation, have cast some doubts on whether or not this is actually true. Here we push the comparison further by extending the SCFT calculations to $\chi N = 512,000$, by accounting for exclusion zones in the coronae of the cylindrical and spherical unit cells, and by examining finite-segregation corrections to SST. In doing so, we provide the first compelling evidence that SCFT does indeed reduce to SST.

Determination of the rheological parameters of self-compacting concrete matrix using slump flow test

The classification of a concrete mixture as self-compacting (SCC) is performed by a series of empirical characterization tests that have been designed to assess not only the flowability of the mixture but also its segregation resistance and filling ability. The objective of the present work is to correlate the rheological parameters of SCC matrix, yield stress and plastic viscosity, to slump flow measurements. The focus of the slump flow test investigation was centered on the fully yielded flow regime and an empirical model relating the yield stress to material and flow parameters is proposed. Our experimental data revealed that the time for a spread of 500 mm which is used in engineering practice as reference for measurement parameters, is an arbitrary choice. Our findings indicate that the non-dimensional final spread is linearly related to the non-dimensional yield-stress. Finally, there are strong indications that the non-dimensional viscosity of the mixture is associated with the non-dimensional final spread as well as the stopping time of the slump flow; this experimental data set suggests an exponential decay of the final spread and stopping time with viscosity. © Appl. Rheol.

Growth mechanism of graphene on platinum: Surface catalysis and carbon segregation

A model of the graphene growth mechanism of chemical vapor deposition on platinum is proposed and verified by experiments. Surface catalysis and carbon segregation occur, respectively, at high and low temperatures in the process, representing the so-called balance and segregation regimes. Catalysis leads to self-limiting formation of large area monolayer graphene, whereas segregation results in multilayers, which evidently "grow from below." By controlling kinetic factors, dominantly monolayer graphene whose high quality has been confirmed by quantum Hall measurement can be deposited on platinum with hydrogen-rich environment, quench cooling, tiny but continuous methane flow and about 1000°C growth temperature. © 2014 AIP Publishing LLC.

Self-organized superlattices along the [001] growth direction in In0.52Al0.48As layers grown on nominally (001) InP substrates by molecular beam epitaxy

Horizontal self-organized superlattice structures consisting of alternating In-rich and Al-rich layers formed naturally during solid-source molecular beam epitaxy (MBE) growth of In0.52Al0.48As on exactly (001) InP substrates, with In and At fluxes unchanged. The growth temperatures were changed from 490 to 510 degrees C, the most commonly used growth temperature for In0.52Al0.48As alloy. No self-organized superlattices (SLs) were observed at the growth temperature 490 degrees C, and self-organized SLs were observed in InAlAs layers at growth temperatures ranging from 498 to 510 degrees C. The results show that the period of the SLs is very highly regular, with the value of similar to 6 nm, and the composition of In or Al varies approximately sinusoidally along the [001] growth direction. The theoretical simulation results confirm that the In composition modulation amplitude is less than 0.02 relative the In composition of the In0.52Al0.48As lattice matched with the InP substrate. The influence of InAs self-organized quantum wires on the spontaneously formed InxAl1-xAs/InyAl1-yAs SLs was also studied and the formation of self-organized InxAl1-xAs/InyAl1-yAs SLs was attributed to the strain-mediated surface segregation process during MBE growth of In0.52Al0.48As alloy. (C) 2005 Published by Elsevier Ltd.

Photoluminescence study of self-assembled InAs/GaAs quantum dots covered by an InAlAs and InGaAs combination layer

We have fabricated a quantum dot (QD) structure for long-wavelength temperature-insensitive semiconductor laser by introducing a combined InAlAs and InGaAs overgrowth layer on InAs/GaAs QDs. We found that QDs formed on GaAs (100) substrate by InAs deposition followed by the InAlAs and InGaAs combination layer demonstrate two effects: one is the photoluminescence peak redshift towards 1.35 mum at room temperature, the other is that the energy separation between the ground and first excited states can be up to 103 meV. These results are attributed to the fact that InAs/GaAs intermixing caused by In segregation at substrate temperature of 520 degreesC can be considerably suppressed by the thin InAlAs layer and the strain in the quantum dots can be reduced by the combined InAlAs and InGaAs layer. (C) 2002 American Institute of Physics.

Study of morphology and phase diagram of the H-shaped (AC)B(CA) ternary block copolymers using self-consistent field theory

By using a combinatorial screening method based on the self-consistent field theory (SCFT) for polymer systems, the micro-phase morphologies of the H-shaped (AC)B(CA) ternary block copolymer system are studied in three-dimensional (3D) space. By systematically varying the volume fractions of the components A, B, and C, six triangle phase diagrams of this H-shaped (AC)B(CA) ternary block copolymer system with equal interaction energies among the three components are constructed from the weaker segregation regime to the strong segregation regime, In this study, thirteen 3D micro-phase morphologies for this H-shaped ternary block copolymer system are identified to be stable and seven 3D microphase morphologies are found to be metastable.

Patterned self-adaptive polymer brushes by "grafting to" approach and microcontact printing

Patterned self-adaptive PS/P2VP mixed polymer brushes were prepared by "grafting to" approach combining with microcontact printing (muCP). The properties of the patterned surface were investigated by lateral force microscopy (LFM), XPS and water condensation figures. In the domains with grafted P2VP, the PS/P2VP mixed brushes demonstrated reversible switching behavior upon exposure to selective solvents for different components. The chemical composition of the top layer as well as the surface wettability can be well tuned due to the perpendicular phase segregation in the mixed brushes. While in the domains without grafted P2VP, the grafted PS did not have the capability of switching. The development and erasing of the pattern is reversible under different solvent treatment.

AIRSTREAM: A Neural Network Model of Auditory Scene Analysis and Source Segregation

Multiple sound sources often contain harmonics that overlap and may be degraded by environmental noise. The auditory system is capable of teasing apart these sources into distinct mental objects, or streams. Such an "auditory scene analysis" enables the brain to solve the cocktail party problem. A neural network model of auditory scene analysis, called the AIRSTREAM model, is presented to propose how the brain accomplishes this feat. The model clarifies how the frequency components that correspond to a give acoustic source may be coherently grouped together into distinct streams based on pitch and spatial cues. The model also clarifies how multiple streams may be distinguishes and seperated by the brain. Streams are formed as spectral-pitch resonances that emerge through feedback interactions between frequency-specific spectral representaion of a sound source and its pitch. First, the model transforms a sound into a spatial pattern of frequency-specific activation across a spectral stream layer. The sound has multiple parallel representations at this layer. A sound's spectral representation activates a bottom-up filter that is sensitive to harmonics of the sound's pitch. The filter activates a pitch category which, in turn, activate a top-down expectation that allows one voice or instrument to be tracked through a noisy multiple source environment. Spectral components are suppressed if they do not match harmonics of the top-down expectation that is read-out by the selected pitch, thereby allowing another stream to capture these components, as in the "old-plus-new-heuristic" of Bregman. Multiple simultaneously occuring spectral-pitch resonances can hereby emerge. These resonance and matching mechanisms are specialized versions of Adaptive Resonance Theory, or ART, which clarifies how pitch representations can self-organize durin learning of harmonic bottom-up filters and top-down expectations. The model also clarifies how spatial location cues can help to disambiguate two sources with similar spectral cures. Data are simulated from psychophysical grouping experiments, such as how a tone sweeping upwards in frequency creates a bounce percept by grouping with a downward sweeping tone due to proximity in frequency, even if noise replaces the tones at their interection point. Illusory auditory percepts are also simulated, such as the auditory continuity illusion of a tone continuing through a noise burst even if the tone is not present during the noise, and the scale illusion of Deutsch whereby downward and upward scales presented alternately to the two ears are regrouped based on frequency proximity, leading to a bounce percept. Since related sorts of resonances have been used to quantitatively simulate psychophysical data about speech perception, the model strengthens the hypothesis the ART-like mechanisms are used at multiple levels of the auditory system. Proposals for developing the model to explain more complex streaming data are also provided.

Texture Segregation By Visual Cortex: Perceptual Grouping, Attention, and Learning

A neural model is proposed of how laminar interactions in the visual cortex may learn and recognize object texture and form boundaries. The model brings together five interacting processes: region-based texture classification, contour-based boundary grouping, surface filling-in, spatial attention, and object attention. The model shows how form boundaries can determine regions in which surface filling-in occurs; how surface filling-in interacts with spatial attention to generate a form-fitting distribution of spatial attention, or attentional shroud; how the strongest shroud can inhibit weaker shrouds; and how the winning shroud regulates learning of texture categories, and thus the allocation of object attention. The model can discriminate abutted textures with blurred boundaries and is sensitive to texture boundary attributes like discontinuities in orientation and texture flow curvature as well as to relative orientations of texture elements. The model quantitatively fits a large set of human psychophysical data on orientation-based textures. Object boundar output of the model is compared to computer vision algorithms using a set of human segmented photographic images. The model classifies textures and suppresses noise using a multiple scale oriented filterbank and a distributed Adaptive Resonance Theory (dART) classifier. The matched signal between the bottom-up texture inputs and top-down learned texture categories is utilized by oriented competitive and cooperative grouping processes to generate texture boundaries that control surface filling-in and spatial attention. Topdown modulatory attentional feedback from boundary and surface representations to early filtering stages results in enhanced texture boundaries and more efficient learning of texture within attended surface regions. Surface-based attention also provides a self-supervising training signal for learning new textures. Importance of the surface-based attentional feedback in texture learning and classification is tested using a set of textured images from the Brodatz micro-texture album. Benchmark studies vary from 95.1% to 98.6% with attention, and from 90.6% to 93.2% without attention.

Applications of statistical models in proportioning medium strength self-compacting concrete

Self-compacting concrete (SCC) is generally designed with a relatively higher content of finer, which includes cement, and dosage of superplasticizer than the conventional concrete. The design of the current SCC leads to high compressive strength, which is already used in special applications, where the high cost of materials can be tolerated. Using SCC, which eliminates the need for vibration, leads to increased speed of casting and thus reduces labour requirement, energy consumption, construction time, and cost of equipment. In order to obtain and gain maximum benefit from SCC it has to be used for wider applications. The cost of materials will be decreased by reducing the cement content and using a minimum amount of admixtures. This paper reviews statistical models obtained from a factorial design which was carried out to determine the influence of four key parameters on filling ability, passing ability, segregation and compressive strength. These parameters are important for the successful development of medium strength self-compacting concrete (MS-SCC). The parameters considered in the study were the contents of cement and pulverised fuel ash (PFA), water-to-powder ratio (W/P), and dosage of superplasticizer (SP). The responses of the derived statistical models are slump flow, fluidity loss, rheological parameters, Orimet time, V-funnel time, L-box, JRing combined to Orimet, JRing combined to cone, fresh segregation, and compressive strength at 7, 28 and 90 days. The models are valid for mixes made with 0.38 to 0.72 W/P ratio, 60 to 216 kg/m3 of cement content, 183 to 317 kg/m3 of PFA and 0 to 1% of SP, by mass of powder. The utility of such models to optimize concrete mixes to achieve good balance between filling ability, passing ability, segregation, compressive strength, and cost is discussed. Examples highlighting the usefulness of the models are presented using isoresponse surfaces to demonstrate single and coupled effects of mix parameters on slump flow, loss of fluidity, flow resistance, segregation, JRing combined to Orimet, and compressive strength at 7 and 28 days. Cost analysis is carried out to show trade-offs between cost of materials and specified consistency levels and compressive strength at 7 and 28 days that can be used to identify economic mixes. The paper establishes the usefulness of the mathematical models as a tool to facilitate the test protocol required to optimise medium strength SCC.