ZnO nanorod arrays fabrication via chemical bath deposition: Ligand concentration effect study

A new ligand, N, N, N', N'-tetramethylethylenediamine, has been used to grow ZnO nanorods on silicon substrates via a two steps approach. A preliminary seeding on silicon substrates has been combined with chemical bath deposition using a Zinc acetate - N, N, N', N'-tetramethylethylenediamine aqueous solution. The used diamino ligand has been selected as Zn(2+) complexing agent and the related hydrolysis generates the reacting ions (Zn(2-) and OH(-)) responsible for the ZnO growth. The seed layer has been annealed at low temperature (<200 degrees C) and the ZnO nanorods have been grown on this ZnO amorphous layer. There is experimental evidence that the ligand concentration (ranging from 5 to 50 mM) strongly affects the alignment of ZnO nanorods on the substrate, their lateral dimension and the related surface density. Length and diameter of ZnO nanorods increase upon increasing the ligand concentration, while the nanorod density decreases. Even more important, it has been demonstrated, as proof of concept, that chemical bath deposition can be usefully combined with colloidal lithography for selective ZnO nanorod deposition Thus, by patterning the ZnO seeded substrate with polystyrene microsphere colloidal lithography, regular Si hole arrays, spatially defined by hexagonal ZnO nanorods, have been successfully obtained. (C) 2010 Elsevier Ltd. All rights reserved

Effects of Metal-Organic Chemical Vapour Deposition grown seed layer on the fabrication of well aligned ZnO nanorods by Chemical Bath Deposition

Well aligned, long and uniform ZnO nanorods have been reproducibly fabricated adopting a two-steps Metal-Organic Chemical Vapour Deposition (MOCVD) and Chemical Bath Deposition (CBD) fabrication approaches. Thin (<100 nm) ZnO buffer layers have been seeded on silicon substrates by MOCVD and ZnO layers have been subsequently grown, in form of well textured nanorods, using CBD. It has been found that the structure and thickness of the seed layer strongly influence the final morphology and the crystal texturing of ZnO nanorods as well as the CBD growth rate. There is, in addition, a strong correlation between morphologies of CBD grown ZnO nanorods and those of the seed layer underneath. Thus, nanorods deposited over low temperature MOCVD buffer layers are less homogeneous in lateral dimensions and poorly vertically oriented. On the contrary, higher temperature nano-dimensional ZnO seeds favour the CBD growth of almost mono-dimensional homologue nanorods, with an adequate control of the lateral transport of matter. The nanorod aspect ratio values decrease upon increasing the deposition temperatures of the seed layers. Moreover, the nanorods length can be tailored either by adjusting the CBD growth time or by changing concentration of the N,N,N',N'-tetramethylethylenediamine ligand used in the CBD process. In particular, at high concentrations, the CBD process is faster with a greater global aspect ratio in agreement with a preferential one-dimensional growth of the ZnO nanostructures. Finally, these ZnO nanorod arrays possess good optical quality in accordance to the photoluminescence properties

A Game-theory Analysis of Charging Stations Selection by EV Drivers

We address the problem of Electric Vehicle (EV) drivers' assistance through Intelligent Transportation System (ITS). Drivers of EVs that are low in battery may ask a navigation service for advice on which charging station to use and which route to take. A rational driver will follow the received advice, provided there is no better choice i.e., in game-theory terms, if such advice corresponds to a Nash-equilibrium strategy. Thus, we model the problem as a game: first we propose a congestion game, then a game with congestion-averse utilities, both admitting at least one pure-strategy Nash equilibrium. The former represents a practical scenario with a high level of realism, although at a high computational price. The latter neglects some features of the real-world scenario but it exhibits very low complexity, and is shown to provide results that, on average, differ by 16% from those obtained with the former approach. Furthermore, when drivers value the trip time most, the average per-EV performance yielded by the Nash equilibria and the one attained by solving a centralized optimization problem that minimizes the EV trip time differ by 15% at most. This is an important result, as minimizing this quantity implies reduced road traffic congestion and energy consumption, as well as higher user satisfaction

Advertisement Delivery and Display in Vehicular Networks

The role of vehicles has been rapidly expanding to become a different kind of utility, no longer just vehicles but nodes of the future Internet. The car producers and the research community are investing considerable time and resources in the design of new protocols and applications that meet customer demand, or that foster new forms of interaction between the moving customers and the rest of the world. Among the variety of new applications and business models, the spreading of advertisements is expected to play a crucial role. Indeed, advertising is already a significant source of revenue and it is currently used over many communication channels, such as the Internet and television. In this paper, we address the targeting of advertisements in vehicular networks, where advertisements are broadcasted by Access Points and then displayed to interested users. In particular, we describe the advertisement dissemination process by means of an optimization model aiming at maximizing the number of advertisements that are displayed to users within the advertisement target area and target time period. We then solve the optimization problem on an urban area, using realistic vehicular traffic traces. Our results highlight the importance of predicting vehicles mobility and the impact of the user interest distribution on the revenue that can be obtained from the advertisement service

Context-aware and automatic configuration of mobile devices in cloud-enabled ubiquitous computing

This is the author's accepted manuscript. The final publication is available at Springer via http://dx.doi.org/10.1007/s00779-013-0698-3. Copyright @ Springer-Verlag London 2013.Context-sensitive (or aware) applications have, in recent years, moved from the realm of possibilities to that of ubiquity. One exciting research area that is still very much in the realm of possibilities is that of cloud computing, and in this paper, we present our work, which explores the overlap of these two research areas. Accordingly, this paper explores the notion of cross-source integration of cloud-based, context-aware information in ubiquitous computing through a developed prototypical solution. Moreover, the described solution incorporates remote and automatic configuration of Android smartphones and advances the research area of context-aware information by harvesting information from several sources to build a rich foundation on which algorithms for context-aware computation can be based. Evaluation results show the viability of integrating and tailoring contextual information to provide users with timely, relevant and adapted application behaviour and content

Colloidal lithography and Metal-Organic Chemical Vapor Deposition process integration to fabricate ZnO nanohole arrays

A complete set up of optimal process conditions for an effective colloidal lithography/catalyst assisted MOCVD process integration is presented. It mainly focuses on the determination of the deposition temperature threshold for ZnO Metal-Organic Chemical Vapour Deposition (MOCVD) as well as the concentration of metal-organic silver (Ag) catalyst. Indeed, the optimization of such process parameters allows to tailor the ZnO film morphology in order to make the colloidal lithography/catalyst assisted MOCVD approach a valuable bottom up method to fabricate bi-dimensional ordered ZnO nanohole arrays. (C) 2010 Elsevier B.V. All rights reserved

Dependable Distributed Training of Compressed Machine Learning Models

The existing work on the distributed training of machine learning (ML) models has consistently overlooked the distribution of the achieved learning quality, focusing instead on its average value. This leads to a poor dependability of the resulting ML models, whose performance may be much worse than expected. We fill this gap by proposing DepL, a framework for dependable learning orchestration, able to make high-quality, efficient decisions on (i) the data to leverage for learning, (ii) the models to use and when to switch among them, and (iii) the clusters of nodes, and the resources thereof, to exploit. For concreteness, we consider as possible available models a full DNN and its compressed versions. Unlike previous studies, DepL guarantees that a target learning quality is reached with a target probability, while keeping the training cost at a minimum. We prove that DepL has constant competitive ratio and polynomial complexity, and show that it outperforms the state-of-the-art by over 27% and closely matches the optimum