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The design of optoelectronic devices made with ZnO superlattices requires the knowledge of the refractive index, which currently can be done only for films thicker than 30 nm. In this work, we present an effective medium approach to determine the refractive index of ZnO layers as thin as 2 nm. The approach was implemented by determining the refractive index of ZnO layers ranging from 2 nm to 20 nm using spectroscopic ellipsometry measurements in multilayers. For a precise control of morphology and thickness, the superlattices were fabricated with atomic layer deposition (ALD) with alternating layers of 2 nm thick Al2O3 and ZnO, labeled as N ZnO-Al2O3, where N = 10, 20, 30, 50, 75 and 100. The total thickness of all superlattices was kept at 100 nm. The approach was validated by applying it to similar superlattices reported in the literature and fitting the transmittance spectra of the superlattices.

Layer-by-layer (LBL) films of a semiconducting polymer (POMA) alternated with a polyelectrolyte (PVS), adsorbed onto silicon oxide, mica, ITO/glass, Au/Cr/glass, hydrophilic and hydrophobic glass were studied by atomic force microscopy (AFM). The samples were characterized LBL with the AFM operating in the contact, friction and tapping modes, which allowed us to determine their morphological surface properties such as roughness, mean grain size, grain boundaries and power spectrum density. Their film thickness was measured by AFM using the tip as a scraping tool. Surface roughness increases with the number of bilayers until a constant value is reached. This is in agreement with the observed increase in the adsorbed amount (per layer) of POMA as the number of bilayers is increased, which also saturates after several bilayers. It is shown that the 3D growth behaviour indicates a similar microscopic mechanism for all systems under study, pointing to a stochastic growth process of the Eden model type, but strongly influenced by initial roughness and water affinity of the virgin substrates. The crystalline or amorphous nature of the substrates does not seem to influence the growth process.

Poly(vinylidene fluoride/trifluoroethylene) copolymers of different molar ratios in the range between 60:40 and 80:20 have been studied by thermally stimulated processes, namely thermally stimulated depolarization current, thermally stimulated current and differential scanning calorimetry, and by dielectric constant measurements. The results obtained show unequivocally that a ferroelectric-to-ferroelectric phase transition exists for 70:30 and 75:25 copolymers, in addition to the well-known ferroelectric-to-paraelectric transition, which has already been observed in all copolymers.

The plate current for a negative corona in a corona triode is observed to become positive and to saturate at a value Is, when the grid is biased positively and the point-to-grid distance is less than 15 mm for corona currents of up to 60 mu A. The saturation current Is decreases exponentially with increasing point-to-grid distance and this strong dependence appears to indicate that the current is linked to those high-energy (hot) electrons which survive attachment, and are capable of ionizing molecules below and on their way to the grid. Simple calculations based on a theoretical model for hot electrons show that their energy is sufficient for causing such ionization. This inverted polarity current can also explain surface charge decay measurements presented in the literature for polymer samples treated in a corona triode when the grid was counterbiased with respect to the corona source.

Thermally stimulated current measurements on beta -PVDF samples submitted to relatively low applied fields (up to 100 kV cm-1) have been performed. The results are interpreted in terms of a novel space-charge effect in PVDF, whereby the space charges are not removed from the sample after the first heating process, but are trapped at the polymer surface.

The mechanisms responsible for containing activity in systems represented by networks are crucial in various phenomena, for example, in diseases such as epilepsy that affect the neuronal networks and for information dissemination in social networks. The first models to account for contained activity included triggering and inhibition processes, but they cannot be applied to social networks where inhibition is clearly absent. A recent model showed that contained activity can be achieved with no need of inhibition processes provided that the network is subdivided into modules (communities). In this paper, we introduce a new concept inspired in the Hebbian theory, through which containment of activity is achieved by incorporating a dynamics based on a decaying activity in a random walk mechanism preferential to the node activity. Upon selecting the decay coefficient within a proper range, we observed sustained activity in all the networks tested, namely, random, Barab?si?Albert and geographical networks. The generality of this finding was confirmed by showing that modularity is no longer needed if the dynamics based on the integrate-and-fire dynamics incorporated the decay factor. Taken together, these results provide a proof of principle that persistent, restrained network activation might occur in the absence of any particular topological structure. This may be the reason why neuronal activity does not spread out to the entire neuronal network, even when no special topological organization exists.

Many features of complex systems can now be unveiled by applying statistical physics methods to treat them as social networks. The power of the analysis may be limited, however, by the presence of ambiguity in names, e.g., caused by homonymy in collaborative networks. In this paper we show that the ability to distinguish between homonymous authors is enhanced when longer-distance connections are considered, rather than looking at only the immediate neighbors of a node in the collaborative network. Optimized results were obtained upon using the 3rd hierarchy in connections. Furthermore, reasonable distinction among authors could also be achieved upon using pattern recognition strategies for the data generated from the topology of the collaborative network. These results were obtained with a network from papers in the arXiv repository, into which homonymy was deliberately introduced to test the methods with a controlled, reliable dataset. In all cases, several methods of supervised and unsupervised machine learning were used, leading to the same overall results. The suitability of using deeper hierarchies and network topology was confirmed with a real database of movie actors, with the additional finding that the distinguishing ability can be further enhanced by combining topology features and long-range connections in the collaborative network.

We report on the use of dynamic scale theory and fractal analyses in a study of the growth stages of Langmuir?Blodgett (LB) films of polyaniline and a neutral biphosphinic ruthenium complex, namely mer-[RuCl3 (dppb)(py)] (dppb = 1,4-bis(diphenylphosphine)buthane, py = pyridine), Rupy. The LB films were deposited onto indium-tin-oxide substrates and characterized with atomic force microscopy. From the granular morphology exhibited by the films one could infer growth processes inside and outside the grains. Growth outside was found to follow the Kardar?Parisi?Zhang model, with fractal dimensions of about?2.7. As one would expect, inside the grains the morphology is close to a Euclidian surface with fractal dimension of about 2.