Electrophoretic Deposition of Transparent ZnO Thin Films from Highly Stabilized Colloidal Suspensions

The parameters that control the stability of ZnO-nanoparticles suspensions and their deposition by electrophoretic deposition were studied, so as to organize the assembly and compaction of nanoparticles. The addition of cationic polyelectrolyte - Polyethylenimine (PEI) - with different molecular weights was investigated, in order to study their effectiveness and the influence of the molecular weight of the organic chain on suspensions dispersion. It was found that PEI with the highest molecular weight provided better dispersion conditions. Cathodic EPD was performed under previously optimized suspensions conditions and over electropolished stainless steel substrates. Experimental results showed that the EPD process in these conditions allows obtaining dense transparent ZnO thin films. Deposition times and intensities were optimized by analyzing the resulting thin films characteristics. Finally, the deposits were characterized by FE-SEM, AFM, and different spectroscopic techniques.

Development of Shape-Optimization Tools for the Aerodynamic Design of Turbomachinery Blades

The aim of this work is to develop an automated tool for the optimization of turbomachinery blades founded on an evolutionary strategy. This optimization scheme will serve to deal with supersonic blades cascades for application to Organic Rankine Cycle (ORC) turbines. The blade geometry is defined using parameterization techniques based on B-Splines curves, that allow to have a local control of the shape. The location in space of the control points of the B-Spline curve define the design variables of the optimization problem. In the present work, the performance of the blade shape is assessed by means of fully-turbulent flow simulations performed with a CFD package, in which a look-up table method is applied to ensure an accurate thermodynamic treatment. The solver is set along with the optimization tool to determine the optimal shape of the blade. As only blade-to-blade effects are of interest in this study, quasi-3D calculations are performed, and a single-objective evolutionary strategy is applied to the optimization. As a result, a non-intrusive tool, with no need for gradients definition, is developed. The computational cost is reduced by the use of surrogate models. A Gaussian interpolation scheme (Kriging model) is applied for the estimated n-dimensional function, and a surrogate-based local optimization strategy is proved to yield an accurate way for optimization. In particular, the present optimization scheme has been applied to the re-design of a supersonic stator cascade of an axial-flow turbine. In this design exercise very strong shock waves are generated in the rear blade suction side and shock-boundary layer interaction mechanisms occur. A significant efficiency improvement as a consequence of a more uniform flow at the blade outlet section of the stator is achieved. This is also expected to provide beneficial effects on the design of a subsequent downstream rotor. The method provides an improvement to gradient-based methods and an optimized blade geometry is easily achieved using the genetic algorithm.

Probabilistic reasoning with an enzyme-driven DNA device

We present a biomolecular probabilistic model driven by the action of a DNA toolbox made of a set of DNA templates and enzymes that is able to perform Bayesian inference. The model will take single-stranded DNA as input data, representing the presence or absence of a specific molecular signal (the evidence). The program logic uses different DNA templates and their relative concentration ratios to encode the prior probability of a disease and the conditional probability of a signal given the disease. When the input and program molecules interact, an enzyme-driven cascade of reactions (DNA polymerase extension, nicking and degradation) is triggered, producing a different pair of single-stranded DNA species. Once the system reaches equilibrium, the ratio between the output species will represent the application of Bayes? law: the conditional probability of the disease given the signal. In other words, a qualitative diagnosis plus a quantitative degree of belief in that diagno- sis. Thanks to the inherent amplification capability of this DNA toolbox, the resulting system will be able to to scale up (with longer cascades and thus more input signals) a Bayesian biosensor that we designed previously.

Influence of termal fluctuations in radiation damage cascade production and defect dynamics in tungsten

The detailed study of the deterioration suffered by the materials of the components of a nuclear facility, in particular those forming part of the reactor core, is a topic of great interest which importance derives in large technological and economic implications. Since changes in the atomic-structural properties of relevant components pose a risk to the smooth operation with clear consequences for security and life of the plant, controlling these factors is essential in any development of engineering design and implementation. In recent times, tungsten has been proposed as a structural material based on its good resistance to radiation, but still needs to be done an extensive study on the influence of temperature on the behavior of this material under radiation damage. This work aims to contribute in this regard. Molecular Dynamics (MD) simulations were carried out to determine the influence of temperature fluctuations on radiation damage production and evolution in Tungsten. We have particularly focused our study in the dynamics of defect creation, recombination, and diffusion properties. PKA energies were sampled in a range from 5 to 50 KeV. Three different temperature scenarios were analyzed, from very low temperatures (0-200K), up to high temperature conditions (300-500 K). We studied the creation of defects, vacancies and interstitials, recombination rates, diffusion properties, cluster formation, their size and evolution. Simulations were performed using Lammps and the Zhou EAM potential for W

Towards an Optimal Implementation of Cascade

Cascade is an information reconciliation protocol proposed in the context of secret key agreement in quantum cryptography. This protocol allows removing discrepancies in two partially correlated sequences that belong to distant parties, connected through a public noiseless channel. It is highly interactive, thus requiring a large number of channel communications between the parties to proceed and, although its efficiency is not optimal, it has become the de-facto standard for practical implementations of information reconciliation in quantum key distribution. The aim of this work is to analyze the performance of Cascade, to discuss its strengths, weaknesses and optimization possibilities, comparing with some of the modified versions that have been proposed in the literature. When looking at all design trade-offs, a new view emerges that allows to put forward a number of guidelines and propose near optimal parameters for the practical implementation of Cascade improving performance significantly in comparison with all previous proposals.

Towards an optimal implementation of cascade

The aim of this contribution is to study the modifications of Cascade, comparing them with the original protocol on the grounds of a full set of parameters, so that the effect of these modifications can be fairly assessed. A number of simulations were performed to study not only the efficiency but also other characteristics of the protocol that are important for its practical application, such as the number of communications and the failure probability. Note that, although it is generally believed that the only price to pay for an improved efficiency is an increased interactivity, when looking at all the significant magnitudes a different view emerges, showing that, for instance, the failure probability eliminate some the supposed advantages of these improvements.