Generalized Relative Entropies as Contrast Functionals on Density Matrices

We use a class of generalized relative entropies on density matrices to obtain one-parameter families of torsion-free affine connections.     

Observation of a superdeformed band in <Superscript>190</Superscript>Pb

A superdeformed band has been observed in the N = 108 isotope ¹⁹⁰Pb. This is the most neutron-deficient Pb isotope in which superdeformed states have been observed. Several theoretical approaches have predicted that N = 108 will mark the limit of observable superdeformation in the Pb isotopes. The band, which consists of five (possibly six) transitions, is observed to feed at least one isomeric level in its decay to the ground state. This decay pattern supports a spin assignment of 10 for the lowest observed level.     

Trapped ion chain as a neural network: error resistant quantum computation

We demonstrate the possibility of realizing a neural network in a chain of trapped ions with induced long range interactions. Such models permit one to store information distributed over the whole system. The storage capacity of such a network, which depends on the phonon spectrum of the system, can be controlled by changing the external trapping potential. We analyze the implementation of error resistant universal quantum information processing in such systems.     

Conductance studies of NaBPh4, NBu4I, NaI, NaCl, NaBr, NaClO4 and the limiting ionic conductance in water + propan-1-ol mixtures at 298.15 K

The electrical conductivities of NaBPh₄, NBu₄I, NaI, NaCl, NaBr and NaClO₄ have been studied in the mixtures of propan-1-ol with water. The obtained results were analysed using the Fuoss–Justice equation. The individual limiting ionic conductivities of Na⁺, NBu₄⁺, BPh₄⁻, I⁻, Cl⁻, Br⁻, ClO₄⁻ ions have been determined using the Fuoss–Hirsch assumption. The dependencies of the limiting molar conductances Λ_o and Walden products Λ_oη versus mixed solvent composition have been discussed.     

Spatial and frequency domain effects of defects in 1D photonic crystal

The aim of this paper is to present the analysis of influence of defects in 1D photonic crystal (PC) on the density of states and simultaneously spontaneous emission, in both spatial and frequency domains. In our investigations we use an analytic model of 1D PC with defects. Our analysis reveals how presence of a defect causes a defect mode to appear. We show that a defect in 1D PC has local character, being negligible in regions of PC situated far from the defected elementary cell. We also analyze the effect of multiple defects, which lead to photonic band gap splitting.     

Integral and differential form of the protein folding problem

The availability of the complete genomic sequences of many species, including human, has raised enormous expectations in medicine, pharmacology, ecology, biotechnology and forensic sciences. However, knowledge is only a first step toward understanding, and we are only at the early stage of a scientific process that might lead us to satisfy all the expectations raised by the genomic projects. In this review I will discuss the present status of computational methods that attempt to infer the unique three-dimensional structure of proteins from their amino acid sequences. Although this problem has been defined as the “holy grail” of biology, it represents only one of the many hurdles in our path towards the understanding of life at a molecular level.     

Iron–cobalt nanocrystalline alloy supported on a cubic mesostructured silica matrix: FeCo/SBA-16 porous nanocomposites

A series of novel nanocomposites constituted of FeCo nanoparticles dispersed in an ordered cubic Im3m mesoporous silica matrix (SBA-16) have been successfully synthesized using the wet impregnation method. SBA-16, prepared using the non-ionic Pluronic 127 triblock copolymer as a structure-directing agent, is an excellent support for catalytic nanoparticles because of its peculiar three-dimensional cage-like structure, high surface area, thick walls, and high thermal stability. Low-angle X-ray diffraction, N₂ physisorption, and transmission electron microscopy analyses show that after metal loading, calcination at 500 °C, and reduction in H₂ flux at 800 °C, the nanocomposites retain the well-ordered structure of the matrix with cubic symmetry of pores. FeCo alloy nanoparticles with spherical shape and narrow size distribution (4–8 nm) are homogeneoulsy distributed throughout the matrix and they seem in a large extent to be allocated inside the pores.     

Nano organic carbon and soot in turbulent non-premixed ethylene flames

Spectral optical techniques are combined to characterise the distribution of large-molecule soot precursors, nanoparticles of organic carbon, and soot in two turbulent non-premixed ethylene flames with differing residence times. Laser-induced fluorescence, laser-induced incandescence and light scattering are used to define distributions across the particle size distribution. From the scattering and laser-induced emission measurements it appears that two classes of particles are formed. The first ones are preferentially formed in the fuel-rich region of the flame closer to the nozzle, have sizes of the order of few nanometers but are not fully solid particles, because the constituent molecules still maintain their individual identity exhibiting strong broadband fluorescence in the UV. The second class of particles constituted by solid particles, with sizes of the order of tens of nanometers are able to absorb a sufficient number of photons to be heated to incandescent temperatures. These larger particles are formed at larger residence times in the flame since they are the result of slow growth processes such as coagulation or carbonization. The flames are also modeled in order to produce mixture fraction maps. A new discovery is that nanoparticles of organic carbon concentration, unlike soot, does correlate well with mixture fraction, independent of position in the flame. This is likely to be a significant benefit to future modelling of soot inception processes in turbulent non-premixed flames.     

Numbers,scale and symbols: the public understanding of nanotechnology

Nanotechnology will be an increasing part of the everyday lives of most people in the world. There is a general recognition that few people understand the implications of the technology, the technology itself or even the definition of the word. This lack of understanding stems from a lack of knowledge about science in general but more specifically difficulty in grasping the size scale and symbolism of nanotechnology. A potential key to informing the general public is establishing the ability to comprehend the scale of nanotechnology. Transitioning from the macro to the nanoscale seems to require an ability to comprehend scales of one-billion. Scaling is a skill not common in most individuals and tests of their ability to extrapolate size based upon scaling a common object demonstrates that most individuals cannot scale to the extent needed to make the transition to nanoscale. Symbolism is another important vehicle to providing the general public with a basis to understand the concepts of nanotechnology. With increasing age, individuals are able to draw representations of atomic scale objects, but these tend to be iconic and the different representations not easily translated. Ball and stick models are most recognized by the public, which provides an opportunity to present not only useful symbolism but also a reference point for the atomic scale.     

Theoretical diagnostic and prediction of physical properties of quaternary InGaAsP compound using artificial neural networks optimized by the Levenberg Maquardt algorithm

The quaternaries \(In_{1 - x} Ga_{x} As_{y} P_{1 - y}\) are the main promising elements for the fabrication of optoelectronic devices. The adjustment of their physical parameters is assumed by the change of the molar fraction \(x\) and \(y\). These parameters can be affected by the variation of temperature and pressure. To make the theoretical diagnosis of these materials, it is fundamental to know the energy gap ‘\(\varvec{E}_{\varvec{g}}\)’ and the lattice parameter ‘\(a\)’, over a wide range of chemical compositions \(0 \le x \le 0.47\) and \(0 \le y \le 1\), at different temperatures and pressures. We show that by using the Artificial Neural Network method optimized by the Levenberg Maquardt algorithm ANN-LM, it is possible to obtain results very close to the experiment. The scatter plot and error calculation show that the ANN-LM model provides more accurate values of the lattice parameter than those calculated by Vegard’s law. On the other hand, the energy gap values \(Eg (x, y, T)\) estimated, using the ANN-LM model, proved to be close to the experimental values that those calculated by the empirical equations. In addition, the ANN-LM method allowed us to estimate with great accuracy the values of the energy gap at different temperatures and pressures \(Eg (P, T)\). Our work provides crucial information on the physical properties of the quaternary without the use of approximations, and without taking into account the hypothesis of a perfect agreement between \(InGaAsP\) and \(InP\) substrate.