Two-dimensional simulation of nanoparticle deposition from high-density plasmas on microstructured surfaces

Selective and controlled deposition of plasma-grown nanoparticles is one of the pressing problems of plasma-aided nanofabrication. The results of advanced numerical simulations of motion of charge-variable nanoparticles in the plasma presheath and sheath areas and in localized microscopic electric fields created by surface microstructures are reported. Conditions for site-selective deposition of such nanoparticles onto individual microstructures and open surface areas within a periodic micropattern are formulated. The effects of plasma parameters, surface potential, and micropattern features on nanoparticle deposition are investigated and explained using particle charging and plasma force arguments. The results are generic and applicable to a broad range of nanoparticle-generating plasmas and practical problems ranging from management of nanoparticle contamination in microelectronics to site-selective nanoparticle deposition into specified device locations, and synthesis of advanced microporous materials and nanoparticle superlattices. © 2007 American Institute of Physics.

Ion-assisted functional monolayer coating of nanorod arrays in hydrogen plasmas

Uniformity of postprocessing of large-area, dense nanostructure arrays is currently one of the greatest challenges in nanoscience and nanofabrication. One of the major issues is to achieve a high level of control in specie fluxes to specific surface areas of the nanostructures. As suggested by the numerical experiments in this work, this goal can be achieved by manipulating microscopic ion fluxes by varying the plasma sheath and nanorod array parameters. The dynamics of ion-assisted deposition of functional monolayer coatings onto two-dimensional carbon nanorod arrays in a hydrogen plasma is simulated by using a multiscale hybrid numerical simulation. The numerical results show evidence of a strong correlation between the aspect ratios and nanopattern positioning of the nanorods, plasma sheath width, and densities and distributions of microscopic ion fluxes. When the spacing between the nanorods and/or their aspect ratios are larger, and/or the plasma sheath is wider, the density of microscopic ion current flowing to each of the individual nanorods increases, thus reducing the time required to apply a functional monolayer coating down to 11 s for a 7-μm-wide sheath, and to 5 s for a 50-μm-wide sheath. The computed monolayer coating development time is consistent with previous experimental reports on plasma-assisted functionalization of related carbon nanostructures [B. N. Khare et al., Appl. Phys. Lett. 81, 5237 (2002)]. The results are generic in that they can be applied to a broader range of plasma-based processes and nanostructures, and contribute to the development of deterministic strategies of postprocessing and functionalization of various nanoarrays for nanoelectronic, biomedical, and other emerging applications.

Templated i-PVD of metallic nanodot arrays

The results of numerical simulation of plasma-based, porous, template-assisted nanofabrication of Au nanodot arrays on highly-doped silicon taking into account typical electron density of low-temperature plasma of 1017-1018 m-3 and electron temperature of 2-5 eV are reported here. Three-dimensional microscopic topography of ion flux distribution over the outer and inner surfaces of the nanoporous template is obtained via numerical simulation of Au ion trajectories in the plasma sheath, in the close proximity of, and inside the nanopores. It is shown that, by manipulating the electron temperature, the cross-sheath potential drop, and by additionally altering the structure of the nanoporous template, one can control the ion fluxes within the nanopores, and eventually maximize the ion deposition onto the top surface of the developing crystalline Au nanodots (see top panel in the figure). In the same time, this procedure allows one to minimize amorphous deposits on the sidewalls that clutter and may eventually close the nanopores, thus disrupting the nanodot growth process, as it is shown in the bottom panel in the figure on the right.

Simulation of ion flux distribution in conductive and nonconductive nanotip patterns

The distribution of flux of carbon-bearing cations over nanopatterned surfaces with conductive nanotips and nonconductive nanoislands is simulated using the Monte-Carlo technique. It is shown that the ion current is focused to nanotip surfaces when the negative substrate bias is low and only slightly perturbed at higher substrate biases. In the low-bias case, the mean horizontal ion displacement caused by the nanotip electric field exceeds 10 nm. However, at higher substrate biases, this value reduces down to 2 nm. In the nonconductive nanopattern case, the ion current distribution is highly nonuniform, with distinctive zones of depleted current density around the nanoislands. The simulation results suggest the efficient means to control ion fluxes in plasma-aided nanofabrication of ordered nanopatterns, such as nanotip microemitter structures and quantum dot or nanoparticle arrays. © World Scientific Publishing Company.

Electrostatic nanoparticle filter for atomic scale fabrication in low-temperature plasmas

The means of reducing nanoparticle contamination in the synthesis of carbon nanostructures in reactive Ar + H2 + CH4 plasmas are studied. It is shown that by combining the electrostatic filtering and thermophoretic manipulation of nanoparticles, one can significantly improve the quality of carbon nanopatterns. By increasing the substrate heating power, one can increase the size of deposited nanoparticles and eventually achieve nanoparticle-free nanoassemblies. This approach is generic and is applicable to other reactive plasma-aided nanofabrication processes.

Tailoring of ion species composition in complex plasmas with charge exchange collisions

A generic approach towards tailoring of ion species composition in reactive plasmas used for nanofabrication of various functional nanofilms and nanoassemblies, based on a simplified model of a parallel-plate rf discharge, is proposed. The model includes an idealized reactive plasma containing two neutral and two ionic species interacting via charge exchange collisions in the presence of a microdispersed solid component. It is shown that the number densities of the desired ionic species can be efficiently managed by adjusting the dilution of the working gas in a buffer gas, rates of electron impact ionization, losses of plasma species on the discharge walls, and surfaces of fine particles, charge exchange rates, and efficiency of three-body recombination processes in the plasma bulk. The results are relevant to the plasma-aided nanomanufacturing of ordered patterns of carbon nanotip and nanopyramid microemitters.

Assembly and self-organization of nanomaterials

The increasing interest in nanoscience and nanotechnology has prompted intense investigations into appropriate fabrication techniques. Self-organized, bottom-up growth of nanomaterials using plasma nanofabrication techniques1–10 has proven to be one of the most promising approaches for the construction of precisely tailored nanostructures (i.e., quantum dots,11–13 nanotubes,14–17 nanowires,18–20 etc.) arrays. Thus the primary aim of this chapter is to show how plasmas may be used to achieve a high level of control during the self-organized growth of a range of nanomaterials, from zero-dimensional quantum dots (Section 15.2) to one- and two-dimensional nanomaterials (Section 15.3) to nanostructured films (Section 15.4)...

Plasma-enhanced chemical vapor deposition of graphene nanostructures

Graphene has received great interest from researchers all over the world owing to its unique properties. Much of the excitement surrounding graphene is due to its remarkable properties and inherent quantum effects. These effects and properties make it a desirable material for the fabrication of new devices. Graphene has a plethora of potential uses including gas and molecular sensors, electronics, spintronics and optics [1-7]. Interestingly, some of these properties have been known about since before the material was even isolated due to a considerable amount of theoretical work and simulations. The material was to some extent a condensed matter modelers "toy" as it was used as a benchmark 2D material Graphene had been used for a long time as the fundamental building block of many other carbon structures...

Optimizing I/O cost and managing memory for composition vector method based on correlation matrix calculation in bioinformatics

The generation of a correlation matrix for set of genomic sequences is a common requirement in many bioinformatics problems such as phylogenetic analysis. Each sequence may be millions of bases long and there may be thousands of such sequences which we wish to compare, so not all sequences may fit into main memory at the same time. Each sequence needs to be compared with every other sequence, so we will generally need to page some sequences in and out more than once. In order to minimize execution time we need to minimize this I/O. This paper develops an approach for faster and scalable computing of large-size correlation matrices through the maximal exploitation of available memory and reducing the number of I/O operations. The approach is scalable in the sense that the same algorithms can be executed on different computing platforms with different amounts of memory and can be applied to different bioinformatics problems with different correlation matrix sizes. The significant performance improvement of the approach over previous work is demonstrated through benchmark examples.

The role of autogenic halide gas in the production of metal borides

Synthesis of metal borides is typically undertaken at high temperature using direct combinations of elemental starting materials[1]. Techniques include carbothermal reduction using elemental carbon, metals, metal oxides and B2O3[2] or reaction between metal chlorides and boron sources[3]. These reactions generally require temperatures greater than 1200oC and are not readily suitable for an industrial setting nor scalable to bulk production.

Making optimal and justifiable asset maintenance decisions

Maintenance decisions for large-scale asset systems are often beyond an asset manager's capacity to handle. The presence of a number of possibly conflicting decision criteria, the large number of possible maintenance policies, and the reality of budget constraints often produce complex problems, where the underlying trade-offs are not apparent to the asset manager. This paper presents the decision support tool "JOB" (Justification and Optimisation of Budgets), which has been designed to help asset managers of large systems assess, select, interpret and optimise the effects of their maintenance policies in the presence of limited budgets. This decision support capability is realized through an efficient, scalable backtracking- based algorithm for the optimisation of maintenance policies, while enabling the user to view a number of solutions near this optimum and explore tradeoffs with other decision criteria. To assist the asset manager in selecting between various policies, JOB also provides the capability of Multiple Criteria Decision Making. In this paper, the JOB tool is presented and its applicability for the maintenance of a complex power plant system.

A two-stage item recommendation method using probabilistic ranking with reconstructed tensor model

In a tag-based recommender system, the multi-dimensional correlation should be modeled effectively for finding quality recommendations. Recently, few researchers have used tensor models in recommendation to represent and analyze latent relationships inherent in multi-dimensions data. A common approach is to build the tensor model, decompose it and, then, directly use the reconstructed tensor to generate the recommendation based on the maximum values of tensor elements. In order to improve the accuracy and scalability, we propose an implementation of the -mode block-striped (matrix) product for scalable tensor reconstruction and probabilistically ranking the candidate items generated from the reconstructed tensor. With testing on real-world datasets, we demonstrate that the proposed method outperforms the benchmarking methods in terms of recommendation accuracy and scalability.

A tag-based personalized item recommendation system using tensor modeling and topic model approaches

This research falls in the area of enhancing the quality of tag-based item recommendation systems. It aims to achieve this by employing a multi-dimensional user profile approach and by analyzing the semantic aspects of tags. Tag-based recommender systems have two characteristics that need to be carefully studied in order to build a reliable system. Firstly, the multi-dimensional correlation, called as tag assignment , should be appropriately modelled in order to create the user profiles [1]. Secondly, the semantics behind the tags should be considered properly as the flexibility with their design can cause semantic problems such as synonymy and polysemy [2]. This research proposes to address these two challenges for building a tag-based item recommendation system by employing tensor modeling as the multi-dimensional user profile approach, and the topic model as the semantic analysis approach. The first objective is to optimize the tensor model reconstruction and to improve the model performance in generating quality rec-ommendation. A novel Tensor-based Recommendation using Probabilistic Ranking (TRPR) method [3] has been developed. Results show this method to be scalable for large datasets and outperforming the benchmarking methods in terms of accuracy. The memory efficient loop implements the n-mode block-striped (matrix) product for tensor reconstruction as an approximation of the initial tensor. The probabilistic ranking calculates the probabil-ity of users to select candidate items using their tag preference list based on the entries generated from the reconstructed tensor. The second objective is to analyse the tag semantics and utilize the outcome in building the tensor model. This research proposes to investigate the problem using topic model approach to keep the tags nature as the “social vocabulary” [4]. For the tag assignment data, topics can be generated from the occurrences of tags given for an item. However there is only limited amount of tags availa-ble to represent items as collection of topics, since an item might have only been tagged by using several tags. Consequently, the generated topics might not able to represent the items appropriately. Furthermore, given that each tag can belong to any topics with various probability scores, the occurrence of tags cannot simply be mapped by the topics to build the tensor model. A standard weighting technique will not appropriately calculate the value of tagging activity since it will define the context of an item using a tag instead of a topic.

Profiling : an application assignment approach for green data centers

In the past few years, there has been a steady increase in the attention, importance and focus of green initiatives related to data centers. While various energy aware measures have been developed for data centers, the requirement of improving the performance efficiency of application assignment at the same time has yet to be fulfilled. For instance, many energy aware measures applied to data centers maintain a trade-off between energy consumption and Quality of Service (QoS). To address this problem, this paper presents a novel concept of profiling to facilitate offline optimization for a deterministic application assignment to virtual machines. Then, a profile-based model is established for obtaining near-optimal allocations of applications to virtual machines with consideration of three major objectives: energy cost, CPU utilization efficiency and application completion time. From this model, a profile-based and scalable matching algorithm is developed to solve the profile-based model. The assignment efficiency of our algorithm is then compared with that of the Hungarian algorithm, which does not scale well though giving the optimal solution.

Decision support for the efficient annotation of bioacoustic events

Acoustic sensors allow scientists to scale environmental monitoring over large spatiotemporal scales. The faunal vocalisations captured by these sensors can answer ecological questions, however, identifying these vocalisations within recorded audio is difficult: automatic recognition is currently intractable and manual recognition is slow and error prone. In this paper, a semi-automated approach to call recognition is presented. An automated decision support tool is tested that assists users in the manual annotation process. The respective strengths of human and computer analysis are used to complement one another. The tool recommends the species of an unknown vocalisation and thereby minimises the need for the memorization of a large corpus of vocalisations. In the case of a folksonomic tagging system, recommending species tags also minimises the proliferation of redundant tag categories. We describe two algorithms: (1) a “naïve” decision support tool (16%–64% sensitivity) with efficiency of O(n) but which becomes unscalable as more data is added and (2) a scalable alternative with 48% sensitivity and an efficiency ofO(log n). The improved algorithm was also tested in a HTML-based annotation prototype. The result of this work is a decision support tool for annotating faunal acoustic events that may be utilised by other bioacoustics projects.