Laser-assisted deposition of thin films from photoexcited vapour phases

Laser-assisted chemical vapour deposition (LCVD) has been extensively studied in the last two decades. A vast range of applications encompass various areas such as microelectronics, micromechanics, microelectromechanics and integrated optics, and a variety of metals, semiconductors and insulators have been grown by LCVD. In this article, we review briefly the LCVD process and present two case studies of thin film deposition related to laser thermal excitation (e.g., boron carbide) and non-thermal excitation (e.g., CrO(2)) of the gas phase.

The spin polarization of CrO2 revisited

Here, we use Andreev reflection spectroscopy to study the spin polarization of high quality CrO2 films. We study the spin polarization as a function of growth temperature, resulting in grain size and electrical resistivity. In these films low temperature growth appears to be a necessary but not sufficient condition to guarantee the observation of high spin polarization, and this is only observed in conjunction with suppressed superconducting gap values and anomalously low interface properties. We suggest that this combination of observations is a manifestation of the long range spin triplet proximity effect. (C) 2007 American Institute of Physics.

Magnetic properties of Co-doped TiO(2) anatase nanopowders

This letter reports on the magnetic properties of Ti(1-x)Co(x)O(2) anatase phase nanopowders with different Co contents. It is shown that oxygen vacancies play an important role in promoting long-range ferromagnetic order in the material studied in addition to the transition-metal doping. Furthermore, the results allow ruling out the premise of a strict connection between Co clustering and the ferromagnetism observed in the Co:TiO(2) anatase system.

Femtosecond laser ablation of dentin

The surface morphology, structure and composition of human dentin treated with a femtosecond infrared laser (pulse duration 500 fs, wavelength 1030 nm, fluences ranging from 1 to 3 J cm(-2)) was studied by scanning electron microscopy, x-ray diffraction, x-ray photoelectron spectroscopy and Fourier transform infrared spectroscopy. The average dentin ablation threshold under these conditions was 0.6 +/- 0.2 J cm(-2) and the ablation rate achieved in the range 1 to 2 mu m/pulse for an average fluence of 3 J cm(-2). The ablation surfaces present an irregular and rugged appearance, with no significant traces of melting, deformation, cracking or carbonization. The smear layer was entirely removed by the laser treatment. For fluences only slightly higher than the ablation threshold the morphology of the laser-treated surfaces was very similar to the dentin fracture surfaces and the dentinal tubules remained open. For higher fluences, the surface was more porous and the dentin structure was partially concealed by ablation debris and a few resolidified droplets. Independently on the laser processing parameters and laser processing method used no sub-superficial cracking was observed. The dentin constitution and chemical composition was not significantly modified by the laser treatment in the processing parameter range used. In particular, the organic matter is not preferentially removed from the surface and no traces of high temperature phosphates, such as the beta-tricalcium phosphate, were observed. The achieved results are compatible with an electrostatic ablation mechanism. In conclusion, the high beam quality and short pulse duration of the ultrafast laser used should allow the accurate preparation of cavities, with negligible damage of the underlying material.

New physics contributions to A(FB)(t(t)over-bar) at the Tevatron

The Tevatron has measured a discrepancy relative to the standard model prediction in the forward-backward asymmetry in top quark pair production. This asymmetry grows with the rapidity difference of the two top quarks. It also increases with the invariant mass of the t (t) over bar pair, reaching, for high invariant masses, 3.4 standard deviations above the next-to-leading order prediction for the charge asymmetry of QCD. However, perfect agreement between experiment and the standard model was found in both total and differential cross section of top quark pair production. As this result could be a sign of new physics we have parametrized this new physics in terms of a complete set of dimension six operators involving the top quark. We have then used a Markov chain Monte Carlo approach in order to find the best set of parameters that fits the data, using all available data regarding top quark pair production at the Tevatron. We have found that just a very small number of operators are able to fit the data better than the standard model.

Elastically restrained Bernoulli-Euler beams applied to rotary machinery modelling

Facing the lateral vibration problem of a machine rotor as a beam on elastic supports in bending, the authors deal with the free vibration of elastically restrained Bernoulli-Euler beams carrying a finite number of concentrated elements along their length. Based on Rayleigh's quotient, an iterative strategy is developed to find the approximated torsional stiffness coefficients, which allows the reconciliation between the theoretical model results and the experimental ones, obtained through impact tests. The mentioned algorithm treats the vibration of continuous beams under a determined set of boundary and continuity conditions, including different torsional stiffness coefficients and the effect of attached concentrated masses and rotational inertias, not only in the energetic terms of the Rayleigh's quotient but also on the mode shapes, considering the shape functions defined in branches. Several loading cases are examined and examples are given to illustrate the validity of the model and accuracy of the obtained natural frequencies.

Long-term price range forecast applied to risk management using regression models

Long-term contractual decisions are the basis of an efficient risk management. However those types of decisions have to be supported with a robust price forecast methodology. This paper reports a different approach for long-term price forecast which tries to give answers to that need. Making use of regression models, the proposed methodology has as main objective to find the maximum and a minimum Market Clearing Price (MCP) for a specific programming period, and with a desired confidence level α. Due to the problem complexity, the meta-heuristic Particle Swarm Optimization (PSO) was used to find the best regression parameters and the results compared with the obtained by using a Genetic Algorithm (GA). To validate these models, results from realistic data are presented and discussed in detail.

Photodiode with nanocrystalline Si/amorphous Si absorber bilayer

This letter reports a near-ultraviolet/visible/near-infrared n(+)-n-i-delta(i)-p photodiode with an absorber comprising a nanocrystalline silicon n layer and a hydrogenated amorphous silicon i layer. Device modeling reveals that the dominant source of reverse dark current is deep defect states in the n layer, and its magnitude is controlled by the i layer thickness. The photodiode with the 900/400 nm thick n-i layers exhibits a reverse dark current density of 3nA/cm(2) at -1V. Donor concentration and diffusion length of holes in the n layer are estimated from the capacitance-voltage characteristics and from the bias dependence of long-wavelength response, respectively. (C) 2011 American Institute of Physics. [doi: 10.1063/1.3660725]

A short-term risk management tool applied to OMEL electricity market using particle swarm optimization

Short-term risk management is highly dependent on long-term contractual decisions previously established; risk aversion factor of the agent and short-term price forecast accuracy. Trying to give answers to that problem, this paper provides a different approach for short-term risk management on electricity markets. Based on long-term contractual decisions and making use of a price range forecast method developed by the authors, the short-term risk management tool presented here has as main concern to find the optimal spot market strategies that a producer should have for a specific day in function of his risk aversion factor, with the objective to maximize the profits and simultaneously to practice the hedge against price market volatility. Due to the complexity of the optimization problem, the authors make use of Particle Swarm Optimization (PSO) to find the optimal solution. Results from realistic data, namely from OMEL electricity market, are presented and discussed in detail.

Genetic algorithm methodology applied to intelligent house control

In recent years the use of several new resources in power systems, such as distributed generation, demand response and more recently electric vehicles, has significantly increased. Power systems aim at lowering operational costs, requiring an adequate energy resources management. In this context, load consumption management plays an important role, being necessary to use optimization strategies to adjust the consumption to the supply profile. These optimization strategies can be integrated in demand response programs. The control of the energy consumption of an intelligent house has the objective of optimizing the load consumption. This paper presents a genetic algorithm approach to manage the consumption of a residential house making use of a SCADA system developed by the authors. Consumption management is done reducing or curtailing loads to keep the power consumption in, or below, a specified energy consumption limit. This limit is determined according to the consumer strategy and taking into account the renewable based micro generation, energy price, supplier solicitations, and consumers’ preferences. The proposed approach is compared with a mixed integer non-linear approach.

Particle swarm optimization applied to integrated demand response resources scheduling

The concept of demand response has a growing importance in the context of the future power systems. Demand response can be seen as a resource like distributed generation, storage, electric vehicles, etc. All these resources require the existence of an infrastructure able to give players the means to operate and use them in an efficient way. This infrastructure implements in practice the smart grid concept, and should accommodate a large number of diverse types of players in the context of a competitive business environment. In this paper, demand response is optimally scheduled jointly with other resources such as distributed generation units and the energy provided by the electricity market, minimizing the operation costs from the point of view of a virtual power player, who manages these resources and supplies the aggregated consumers. The optimal schedule is obtained using two approaches based on particle swarm optimization (with and without mutation) which are compared with a deterministic approach that is used as a reference methodology. A case study with two scenarios implemented in DemSi, a demand Response simulator developed by the authors, evidences the advantages of the use of the proposed particle swarm approaches.

A long-term swarm intelligence hedging tool applied to electricity markets

This paper proposes a swarm intelligence long-term hedging tool to support electricity producers in competitive electricity markets. This tool investigates the long-term hedging opportunities available to electric power producers through the use of contracts with physical (spot and forward) and financial (options) settlement. To find the optimal portfolio the producer risk preference is stated by a utility function (U) expressing the trade-off between the expectation and the variance of the return. Variance estimation and the expected return are based on a forecasted scenario interval determined by a long-term price range forecast model, developed by the authors, whose explanation is outside the scope of this paper. The proposed tool makes use of Particle Swarm Optimization (PSO) and its performance has been evaluated by comparing it with a Genetic Algorithm (GA) based approach. To validate the risk management tool a case study, using real price historical data for mainland Spanish market, is presented to demonstrate the effectiveness of the proposed methodology.

Multi-dimensional scaling applied to histogram-based DNA analysis

This paper aims to study the relationships between chromosomal DNA sequences of twenty species. We propose a methodology combining DNA-based word frequency histograms, correlation methods, and an MDS technique to visualize structural information underlying chromosomes (CRs) and species. Four statistical measures are tested (Minkowski, Cosine, Pearson product-moment, and Kendall τ rank correlations) to analyze the information content of 421 nuclear CRs from twenty species. The proposed methodology is built on mathematical tools and allows the analysis and visualization of very large amounts of stream data, like DNA sequences, with almost no assumptions other than the predefined DNA “word length.” This methodology is able to produce comprehensible three-dimensional visualizations of CR clustering and related spatial and structural patterns. The results of the four test correlation scenarios show that the high-level information clusterings produced by the MDS tool are qualitatively similar, with small variations due to each correlation method characteristics, and that the clusterings are a consequence of the input data and not method’s artifacts.

Comparison between deterministic and meta-heuristic methods applied to ancillary services dispatch

This paper proposes two meta-heuristics (Genetic Algorithm and Evolutionary Particle Swarm Optimization) for solving a 15 bid-based case of Ancillary Services Dispatch in an Electricity Market. A Linear Programming approach is also included for comparison purposes. A test case based on the dispatch of Regulation Down, Regulation Up, Spinning Reserve and Non-Spinning Reserve services is used to demonstrate that the use of meta-heuristics is suitable for solving this kind of optimization problem. Faster execution times and lower computational resources requirements are the most relevant advantages of the used meta-heuristics when compared with the Linear Programming approach.

Photodetector with integrated optical thin film filters

This paper reports on optical filters based on a-SiC:H tandem pi'n/pin heterostructures. The spectral sensitivity is analyzed. Steady state optical bias with different wavelengths are applied from each front and back sides and the photocurrent is measured. Results show that it is possible to control the sensitivity of the device and to tune a specific wavelength range by combining radiations with complementary light penetration depths. The transfer characteristics effects due to changes in the front and back optical bias wavelength are discussed. Depending on the wavelength of the external background and irradiation side, the device acts either as a short- or a long-pass band filter or as a band-stop filter. The output waveform presents a nonlinear amplitude-dependent response to the wavelengths of the input channels.