Effect of elemental composition and size on electron confinement in self-assembled SiC quantum dots : a combinatorial approach

A high level of control over quantum dot (QD) properties such as size and composition during fabrication is required to precisely tune the eventual electronic properties of the QD. Nanoscale synthesis efforts and theoretical studies of electronic properties are traditionally treated quite separately. In this paper, a combinatorial approach has been taken to relate the process synthesis parameters and the electron confinement properties of the QDs. First, hybrid numerical calculations with different influx parameters for Si1-x Cx QDs were carried out to simulate the changes in carbon content x and size. Second, the ionization energy theory was applied to understand the electronic properties of Si1-x Cx QDs. Third, stoichiometric (x=0.5) silicon carbide QDs were grown by means of inductively coupled plasma-assisted rf magnetron sputtering. Finally, the effect of QD size and elemental composition were then incorporated in the ionization energy theory to explain the evolution of the Si1-x Cx photoluminescence spectra. These results are important for the development of deterministic synthesis approaches of self-assembled nanoscale quantum confinement structures.

High-rate, low-temperature synthesis of composition controlled hydrogenated amorphous silicon carbide films in low-frequency inductively coupled plasmas

It is commonly believed that in order to synthesize high-quality hydrogenated amorphous silicon carbide (a-Si1-xCx : H) films at competitive deposition rates it is necessary to operate plasma discharges at high power regimes and with heavy hydrogen dilution. Here we report on the fabrication of hydrogenated amorphous silicon carbide films with different carbon contents x (ranging from 0.09 to 0.71) at high deposition rates using inductively coupled plasma (ICP) chemical vapour deposition with no hydrogen dilution and at relatively low power densities (∼0.025 W cm -3) as compared with existing reports. The film growth rate R d peaks at x = 0.09 and x = 0.71, and equals 18 nm min-1 and 17 nm min-1, respectively, which is higher than other existing reports on the fabrication of a-Si1-xCx : H films. The extra carbon atoms for carbon-rich a-Si1-xCx : H samples are incorporated via diamond-like sp3 C-C bonding as deduced by Fourier transform infrared absorption and Raman spectroscopy analyses. The specimens feature a large optical band gap, with the maximum of 3.74 eV obtained at x = 0.71. All the a-Si1-xCx : H samples exhibit low-temperature (77 K) photoluminescence (PL), whereas only the carbon-rich a-Si1-xCx : H samples (x ≥ 0.55) exhibit room-temperature (300 K) PL. Such behaviour is explained by the static disorder model. High film quality in our work can be attributed to the high efficiency of the custom-designed ICP reactor to create reactive radical species required for the film growth. This technique can be used for a broader range of material systems where precise compositional control is required. © 2008 IOP Publishing Ltd.

Tailoring the composition of self-assembled Si1−xCx quantum dots : simulation of plasma/ion-related controls

Precise control of composition and internal structure is essential for a variety of novel technological applications which require highly tailored binary quantum dots (QDs) with predictable optoelectronic and mechanical properties. The delicate balancing act between incoming flux and substrate temperature required for the growth of compositionally graded (Si1-xC x; x varies throughout the internal structure), core-multishell (discrete shells of Si and C or combinations thereof) and selected composition (x set) QDs on low-temperature plasma/ion-flux-exposed Si(100) surfaces is investigated via a hybrid numerical simulation. Incident Si and C ions lead to localized substrate heating and a reduction in surface diffusion activation energy. It is shown that by incorporating ions in the influx, a steady-state composition is reached more quickly (for selected composition QDs) and the composition gradient of a Si1-xCx QD may be fine tuned; additionally (with other deposition conditions remaining the same), larger QDs are obtained on average. It is suggested that ionizing a portion of the influx is another way to control the average size of the QDs, and ultimately, their internal structure. Advantages that can be gained by utilizing plasma/ion-related controls to facilitate the growth of highly tailored, compositionally controlled quantum dots are discussed as well.

Plasma-controlled nanocrystallinity and phase composition of TiO2 : a smart way to enhance biomimetic response

This contribution sheds light on the role of crystal size and phase composition in inducing biomimetic apatite growth on the surface of nanostructured titania films synthesized by reactive magnetron sputtering of Ti targets in Ar+O2 plasmas. Unlike most existing techniques, this method enables one to deposit highly crystalline titania films with a wide range of phase composition and nanocrystal size, without any substrate heating or postannealing. Moreover, by using this dry plasma-based method one can avoid surface hydroxylation at the deposition stage, almost inevitable in wet chemical processes. Results of this work show that high phase purity and optimum crystal size appear to be the essential requirement for efficient apatite formation on magnetron plasma-fabricated bioactive titania coatings. © 2006 Wiley Periodicals, Inc.

Participatory Data Analytics: Collaborative interfaces for data composition and visualisation

This research proposes the development of interfaces to support collaborative, community-driven inquiry into data, which we refer to as Participatory Data Analytics. Since the investigation is led by local communities, it is not possible to anticipate which data will be relevant and what questions are going to be asked. Therefore, users have to be able to construct and tailor visualisations to their own needs. The poster presents early work towards defining a suitable compositional model, which will allow users to mix, match, and manipulate data sets to obtain visual representations with little-to-no programming knowledge. Following a user-centred design process, we are subsequently planning to identify appropriate interaction techniques and metaphors for generating such visual specifications on wall-sized, multi-touch displays.

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.

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.

Assessment of body composition in Indian adults: comparison between dual-energy X-ray absorptiometry and isotope dilution technique

Dual-energy X-ray absorptiometry (DXA) and isotope dilution technique have been used as reference methods to validate the estimates of body composition by simple field techniques; however, very few studies have compared these two methods. We compared the estimates of body composition by DXA and isotope dilution (18O) technique in apparently healthy Indian men and women (aged 19–70 years, n 152, 48 % men) with a wide range of BMI (14–40 kg/m2). Isotopic enrichment was assessed by isotope ratio mass spectroscopy. The agreement between the estimates of body composition measured by the two techniques was assessed by the Bland–Altman method. The mean age and BMI were 37 (SD 15) years and 23·3 (SD 5·1) kg/m2, respectively, for men and 37 (SD 14) years and 24·1 (SD 5·8) kg/m2, respectively, for women. The estimates of fat-free mass were higher by about 7 (95 % CI 6, 9) %, those of fat mass were lower by about 21 (95 % CI 218,223) %, and those of body fat percentage (BF%) were lower by about 7·4 (95 % CI 28·2, 26·6) % as obtained by DXA compared with the isotope dilution technique. The Bland–Altman analysis showed wide limits of agreement that indicated poor agreement between the methods. The bias in the estimates of BF% was higher at the lower values of BF%. Thus, the two commonly used reference methods showed substantial differences in the estimates of body composition with wide limits of agreement. As the estimates of body composition are method-dependent, the two methods cannot be used interchangeably

BDNF SNPs are implicated in comorbid alcohol dependence in schizophrenia but not in alcohol-dependent patients without schizophrenia

Aims The functional BDNF single nucleotide polymorphism (SNP) rs6265 has been associated with many disorders including schizophrenia and alcohol dependence. However, studies have been inconsistent, reporting both positive and negative associations. Comorbid alcohol dependence has a high prevalence in schizophrenia so we investigated the role of rs6265 in alcohol dependence in Australian populations of schizophrenia and alcohol dependent patients. Methods Two BDNF SNPs rs6265 and a nearby SNP rs7103411 were genotyped in a total of 848 individuals. These included a schizophrenia group (n = 157) and a second schizophrenia replication group (n = 235), an alcohol dependent group (n = 231) that had no schizophrenia diagnosis and a group of healthy controls (n = 225). Results Allelic association between rs7103411 and comorbid alcohol dependence was identified (P = 0.044) in the primary schizophrenia sample. In the replication study, we were able to detect allelic associations between both BDNF SNPs and comorbid alcohol dependence (rs6265, P = 0.006; rs7103411, P = 0.014). Moreover, we detected association between both SNPs and risk-taking behaviour after drinking (rs6265, P = 0.005; rs7103411, P = 0.009) and we detected strong association between both SNPs and alcohol dependence in males (rs6265, P = 0.009; rs7103411, P = 0.013) while females showed association with multiple behavioural measures reflecting repetitive alcohol consumption. Haplotype analysis revealed the rs6265-rs7103411 A/C haplotype is associated with comorbid alcohol dependence (P = 0.002). When these SNPs were tested in the non-schizophrenia alcohol dependent group we were unable to detect association. Conclusion We conclude that these BDNF SNPs play a role in development of comorbid alcohol dependence in schizophrenia while our data does not indicate that they play a role in alcohol dependent patients who do not have schizophrenia.

Decomposition of the environmental Kuznets curve : scale, technique, and composition effects

This study decomposed the determinants of environmental quality into scale, technique, and composition effects. We applied a semiparametric method of generalized additive models, which enabled us to use flexible functional forms and include several independent variables in the model. The differences in the technique effect were found to play a crucial role in reducing pollution. We found that the technique effect was sufficient to reduce sulfur dioxide emissions. On the other hand, its effect was not enough to reduce carbon dioxide (CO2) emissions and energy use, except for the case of CO2 emissions in high-income countries.

An investigation of the impact of various geographical scales for the specification of spatial dependence

Ecological studies are based on characteristics of groups of individuals, which are common in various disciplines including epidemiology. It is of great interest for epidemiologists to study the geographical variation of a disease by accounting for the positive spatial dependence between neighbouring areas. However, the choice of scale of the spatial correlation requires much attention. In view of a lack of studies in this area, this study aims to investigate the impact of differing definitions of geographical scales using a multilevel model. We propose a new approach -- the grid-based partitions and compare it with the popular census region approach. Unexplained geographical variation is accounted for via area-specific unstructured random effects and spatially structured random effects specified as an intrinsic conditional autoregressive process. Using grid-based modelling of random effects in contrast to the census region approach, we illustrate conditions where improvements are observed in the estimation of the linear predictor, random effects, parameters, and the identification of the distribution of residual risk and the aggregate risk in a study region. The study has found that grid-based modelling is a valuable approach for spatially sparse data while the SLA-based and grid-based approaches perform equally well for spatially dense data.