Dispersive diffusion controlled distance dependent recombination in amorphous semiconductors

The photoluminescence in amorphous semiconductors decays according to power law $t^{-delta}$ at long times. The photoluminescence is controlled by dispersive transport of electrons. The latter is usually characterized by the power $alpha$ of the transient current observed in the time-of-flight experiments. Geminate recombination occurs by radiative tunneling which has a distance dependence. In this paper, we formulate ways to calculate reaction rates and survival probabilities in the case carriers execute dispersive diffusion with long-range reactivity. The method is applied to obtain tunneling recombination rates under dispersive diffusion. The theoretical condition of observing the relation $delta = alpha/2 + 1$ is obtained and theoretical recombination rates are compared to the kinetics of observed photoluminescence decay in the whole time range measured.Comment: To appear in Journal of Chemical Physic

Dispersive photoluminescence decay by geminate recombination in amorphous semiconductors

The photoluminescence decay in amorphous semiconductors is described by power law $t^{-delta}$ at long times. The power-law decay of photoluminescence at long times is commonly observed but recent experiments have revealed that the exponent, $delta sim 1.2-1.3$, is smaller than the value 1.5 predicted from a geminate recombination model assuming normal diffusion. Transient currents observed in the time-of-flight experiments are highly dispersive characterized by the disorder parameter $alpha$ smaller than 1. Geminate recombination rate should be influenced by the dispersive transport of charge carriers. In this paper we derive the simple relation, $delta = 1+ alpha/2$. Not only the exponent but also the amplitude of the decay calculated in this study is consistent with measured photoluminescence in a-Si:H.Comment: 18pages. Submitted for the publication in Phys. Rev.

Structural Insights into Differences in Drug-binding Selectivity between Two Forms of Human α1-Acid Glycoprotein Genetic Variants, the A and F1*S Forms

Human α1-acid glycoprotein (hAGP) in serum functions as a carrier of basic drugs. In most individuals, hAGP exists as a mixture of two genetic variants, the F1*S and A variants, which bind drugs with different selectivities. We prepared a mutant of the A variant, C149R, and showed that its drug-binding properties were indistinguishable from those of the wild type. In this study, we determined the crystal structures of this mutant hAGP alone and complexed with disopyramide (DSP), amitriptyline (AMT), and the nonspecific drug chlorpromazine (CPZ). The crystal structures revealed that the drug-binding pocket on the A variant is located within an eight-stranded β-barrel, similar to that found in the F1*S variant and other lipocalin family proteins. However, the binding region of the A variant is narrower than that of the F1*S variant. In the crystal structures of complexes with DSP and AMT, the two aromatic rings of each drug interact with Phe-49 and Phe-112 at the bottom of the binding pocket. Although the structure of CPZ is similar to those of DSP and AMT, its fused aromatic ring system, which is extended in length by the addition of a chlorine atom, appears to dictate an alternative mode of binding, which explains its nonselective binding to the F1*S and A variant hAGPs. Modeling experiments based on the co-crystal structures suggest that, in complexes of DSP, AMT, or CPZ with the F1*S variant, Phe-114 sterically hinders interactions with DSP and AMT, but not CPZ. © 2011 by The American Society for Biochemistry and Molecular Biology, Inc

Robust multi-fidelity design of a micro re-entry unmanned space vehicle

This article addresses the preliminary robust design of a small-scale re-entry unmanned space vehicle by means of a hybrid optimization technique. The approach, developed in this article, closely couples an evolutionary multi-objective algorithm with a direct transcription method for optimal control problems. The evolutionary part handles the shape parameters of the vehicle and the uncertain objective functions, while the direct transcription method generates an optimal control profile for the re-entry trajectory. Uncertainties on the aerodynamic forces and characteristics of the thermal protection material are incorporated into the vehicle model, and a Monte-Carlo sampling procedure is used to compute relevant statistical characteristics of the maximum heat flux and internal temperature. Then, the hybrid algorithm searches for geometries that minimize the mean value of the maximum heat flux, the mean value of the maximum internal temperature, and the weighted sum of their variance: the evolutionary part handles the shape parameters of the vehicle and the uncertain functions, while the direct transcription method generates the optimal control profile for the re-entry trajectory of each individual of the population. During the optimization process, artificial neural networks are utilized to approximate the aerodynamic forces required by the optimal control solver. The artificial neural networks are trained and updated by means of a multi-fidelity approach: initially a low-fidelity analytical model, fitted on a waverider type of vehicle, is used to train the neural networks, and through the evolution a mix of analytical and computational fluid dynamic, high-fidelity computations are used to update it. The data obtained by the high-fidelity model progressively become the main source of updates for the neural networks till, near the end of the optimization process, the influence of the data obtained by the analytical model is practically nullified. On the basis of preliminary results, the adopted technique is able to predict achievable performance of the small spacecraft and the requirements in terms of thermal protection materials