Transport across a junction of topological insulators and a superconductor

We study transport across a line junction lying between two orthogonal topological insulator surfaces and a superconductor which can have either s-wave (spin-singlet) or p-wave (spin-triplet) pairing symmetry. The junction can have three time-reversal invariant barriers on three sides. We compute the charge and the spin conductance across such a junction and study their behaviors as a function of the bias voltage applied across the junction and the three parameters used to characterize the barrier. We find that the presence of topological insulators and a superconductor leads to both Dirac- and Schrodinger-like features in charge and spin conductances. We discuss the effect of bound states on the superconducting side of the barrier on the conductance; in particular, we show that for triplet p-wave superconductors, such a junction may be used to determine the spin state of its Cooper pairs. Our study reveals that there is a nonzero spin conductance for some particular spin states of the triplet Cooper pairs; this is an effect of the topological insulators which break the spin rotation symmetry. Finally, we find an unusual satellite peak (in addition to the usual zero bias peak) in the spin conductance for p-wave symmetry of the superconductor order parameter.

Deciphering complex patterns of class-I HLA-peptide cross-reactivity via hierarchical grouping

T-cell responses in humans are initiated by the binding of a peptide antigen to a human leukocyte antigen (HLA) molecule. The peptide-HLA complex then recruits an appropriate T cell, leading to cell-mediated immunity. More than 2000 HLA class-I alleles are known in humans, and they vary only in their peptide-binding grooves. The polymorphism they exhibit enables them to bind a wide range of peptide antigens from diverse sources. HLA molecules and peptides present a complex molecular recognition pattern, as many peptides bind to a given allele and a given peptide can be recognized by many alleles. A powerful grouping scheme that not only provides an insightful classification, but is also capable of dissecting the physicochemical basis of recognition specificity is necessary to address this complexity. We present a hierarchical classification of 2010 class-I alleles by using a systematic divisive clustering method. All-pair distances of alleles were obtained by comparing binding pockets in the structural models. By varying the similarity thresholds, a multilevel classification was obtained, with 7 supergroups, each further subclassifying to yield 72 groups. An independent clustering performed based only on similarities in their epitope pools correlated highly with pocket-based clustering. Physicochemical feature combinations that best explain the basis of clustering are identified. Mutual information calculated for the set of peptide ligands enables identification of binding site residues contributing to peptide specificity. The grouping of HLA molecules achieved here will be useful for rational vaccine design, understanding disease susceptibilities and predicting risk of organ transplants.

Oil-Water Two-Phase Flow Inside T-Junction

The oil/water two-phase flow inside T-junctions was numerically simulated with a 3-D two-fluid model, and the turbulence was described using the mixture k - epsilon model. Some experiments of oil/water flow inside a single T-junction were conducted in the laboratory. The results show that the separating performance of T-junction largely depends oil the inlet volumetric fraction and flow patterns. A reasonable agreement is reached between the numerical simulation and the experiments for both the oil fraction distribution and the separation efficiency.

Grouping materials and processes for the designer: an application for cluster analysis.

In this paper, an analytical tool - cluster analysis - that is commonly used in biology, archaeology, linguistics and psychology is applied to materials and design. Here we use it to cluster materials and the processes that shape them, using their attributes as indicators of relationship. The attributes that are chosen are important to design and designers. The resulting clusters, and the classifications that can be developed from them, depend on the selected attributes and - to some extent - on the method of clustering. Alternative classifications for design that is focused on the technical or aesthetic attributes of materials and the materials and shapes allowed by processes are explored.

Quantum Efficiency And Temperature Coefficients Of Gainp/Gaas Dual-Junction Solar Cell

GaInP/GaAs dual-junction solar cell with a conversion efficiency of 25.2% has been fabricated using metalorganic chemical vapor deposition (MOCVD) technique. Quantum efficiencies of the solar cell were measured within a temperature range from 25 to 160A degrees C. The results indicate that the quantum efficiencies of the subcells increase slightly with the increasing temperature. And red-shift phenomena of absorption limit for all subcells are observed by increasing the cell's work temperature, which are consistent with the viewpoint of energy gap narrowing effect. The short-circuit current density temperature coefficients dJ (sc)/dT of GaInP subcell and GaAs subcell are determined to be 8.9 and 7.4 mu A/cm(2)/A degrees C from the quantum efficiency data, respectively. And the open-circuit cell voltage temperature coefficients dV (oc)/dT calculated based on a theoretical equation are -2.4 mV/A degrees C and -2.1 mV/A degrees C for GaInP subcell and GaAs subcell.