Fracture behavior of a Zr-metallic glass under dynamic compression

Dynamic planar compressive experiments on a typical tough Zr-BMG (Bulk Metallic Glass) were carried out under impact velocity of 500-600 m/sec and strain rate of 10(6)/s. The fracture surface of samples exhibits different fracture patterns at different parts of the sample. At a corner close to the front loading boundary, fracture patterns from the free edge toward the centre changed from equiaxial veins in microscale to periodic corrugations in nanoscale; in the middle of the sample, the fracture surface contains glazed zones laid out orderly along the same boundary. FEM simulation was performed to investigate the stress distributions in the impacted sample under a short duration impact loading. It has revealed that the fracture patterns changing from the free edge toward the centre were resulted from the fracture modes' changing from the tensile dominant fracture to the shear dominant fracture. Whereas at the middle part of the sample, fracture initiated from several parallel shear bands propagating close to the same boundary is due to a large strain or much higher shear stress in this area.

Quark distributions in nuclei and nuclear effects on nucleon structure functions

Extended quark distribution functions are presented obtained by fitting a large amount of experimental data of the l-A DIS process on the basis of an improved nuclear density model. The experimental data of l-A DIS processes with A >= 3 in the region 0.0010 <= x <= 0.9500 axe quite satisfactorily described by using the extended formulae. Our knowledge of the influence of nuclear matter on the quark distributions is deepened.

Single-walled carbon nanohorn as new solid-phase extraction adsorbent for determination of 4-nitrophenol in water sample

Single-walled carbon nanohorn (SWCNH) was developed as new adsorbent for solid-phase extraction using 4-nitrophenol as representative. The unique exoteric structures and high surface area of SWCNH allow extracting a large amount of 4-nitrophenol over a short time. Highly sensitive determination of 4-nitrophenol was achieved by linear sweep voltammetry after only 120 s extraction. The calibration plot for 4-nitrophenol determination is linear in the range of 5.0 x 10(-8) M-1.0 x 10(-5) M under optimum conditions. The detection limit is 1.1 x 10(-8) M. The proposed method was successfully employed to determine 4-nitrophenol in lake water samples, and the recoveries of the spiked 4-nitrophenol were excellent (92-106%).

Clay mineral distributions in the southern Yellow Sea and their significance

To better understand the characteristics of the clay minerals in the southern Yellow Sea, the X-ray quantitative determinations have been carried out for the surface samples obtained from the Yellow Sea. With newly compiled clay mineral synoptic maps, the depositional processes were described for four main clay minerals (illite, chlorite, kaolinite and smectite). The analysis shows that most clay minerals are of terrigenous source with the Huanghe River acting as the major sediment supplier. Besides, the source of muddy sediments in the Yellow Sea was also discussed. As for the central Yellow Sea mud (CYSM), the sediments in its northern part mainly come from the Huanghe River, and those in the rest are of multi-origin. Very similarly, a large amount of sediments in the northern part of the southeastern Yellow Sea Mud (SEYSM) derive from the Keum River and Yeong-san River, while those in the southern part are of multi-origin.

Size dependence of biomass spectra and abundance spectra: the optimal distributions

Based on the hypothesis of self-optimization, we derive four models of biomass spectra and abundance spectra in communities with size-dependent metabolic rates. In Models 1 and 2, the maximum diversity of population abundance in different size classes subject to the constraints of constant mean body mass and constant mean respiration rate is assumed to be the strategy for ecosystems to organize their size structure. In Models 3 and 4, the organizing strategy is defined as the maximum diversity of biomass in different size classes without constraints on mean body mass and subject to the constant mean specific respiration rate of all individuals, i.e. the average specific respiration rate over all individuals of a community or group, which characterizes the mean rate of energy consumption in a community. Models 1 and 2 generate peaked distributions of biomass spectral density whereas Model 3 generates a fiat distribution. In Model 4, the distributions of biomass spectral density and of abundance spectral density depend on the Lagrangian multipler (lambda (2)). When lambda (2) tends to zero or equals zero, the distributions of biomass spectral density and of abundance spectral density correspond to those from Model 3. When lambda (2) has a large negative value, the biomass spectrum is similar to the empirical fiat biomass spectrum organized in logarithmic size intervals. When lambda (2) > 0, the biomass spectral density increases with body mass and the distribution of abundance spectral density is an unimodal curve. (C) 2001 Elsevier Science B.V. All rights reserved.

Acid catalyzed synthesis of ordered bifunctionalized mesoporous organosilicas with large pore

Thiol-functionalized mesoporous ethane-silicas with large pore were synthesized by co-condensation of 1,2-bis(trimethoxy-sily)ethane (BTME) with 3-mercaptopropyltrimethoxysilane (MPTMS) using nonionic oligomeric polymer C1H (OCH(2)-CH(2))(10)OH (Brij-76) or poly(alkylene oxide) block copolymer (P123) as surfactant in acidic medium. The results of powder X-ray diffraction (XRD), nitrogen gas adsorption, and transmission electron microscopy (TEM) show that the resultant materials have well-ordered hexagonal mesoscopic structure with uniform pore size distributions. (29)Si MAS NNR, (13)C CP-MAS NMR. FT-IR, and UV Raman spectroscopies confirm the attachment of thiol functionalities in the mesoporous ethane-sificas. The maximum content of the attached thiol group (-SH) in the mesoporous framework is 2.48mmol/g. The ordered mesoporous materials are efficient Hg(2+) adsorbents with almost every -SH site accessible to Hg(2+). In the presence of various kinds of heavy metal ions such as Hg(2+), Cd(2+), Zn(2+), Cu(2+) and Cr(3+), the materials synthesized using poly(alkylene oxide) block cooollxmier (Pluronic 123) g(2+), as surfactant show almost similar affinity to Hg(2+), Cd(2+), and Cr(3+), while the materials synthesized using ofigomeric polymer C(18)H(37)(OCH(2)CH(2))(10)OH (Brij-76) as surfactant exhibit high selectivity to Hg(2+). (C) 2004 Elsevier Inc. All rights reserved.

Post-grafting preparation of large-pore mesoporous materials with localized high content titanium doping

A new post-grafting process, consisting of two steps of substrate preparation and sol - gel post-grafting, has been developed to prepare titanium-doped mesoporous SBA-15 material with a double-layered structure and locally concentrated titanium content at the inner pore surface. With this novel technique, the single phased and originally ordered mesostructures can be well conserved; in the conventional direct synthesis they can be partially damaged when the frameworks are doped with high content heteroatoms. Titanium species exist in an isolated, tetrahedral structure and are localized at the pore surface; this is beneficial to both reactant access and product release. Characterization with XRD, N-2 adsorption/desorption isotherms, HREM/ EDS, ICP, UV - Vis, and the newly developed UV - Raman spectroscopy confirm these results. Preliminary catalytic tests with the selective epoxidation of cyclohexene show good catalytic activity. Among them, sample TiSBA-15-10 with a Si : Ti molar ratio of 10 shows a TON value of 75 and a highest product ( epoxide) yield of 55%.

On the Performance of Polynomial-time CLIQUE Approximation Algorithms on Very Large Graphs

The performance of a randomized version of the subgraph-exclusion algorithm (called Ramsey) for CLIQUE by Boppana and Halldorsson is studied on very large graphs. We compare the performance of this algorithm with the performance of two common heuristic algorithms, the greedy heuristic and a version of simulated annealing. These algorithms are tested on graphs with up to 10,000 vertices on a workstation and graphs as large as 70,000 vertices on a Connection Machine. Our implementations establish the ability to run clique approximation algorithms on very large graphs. We test our implementations on a variety of different graphs. Our conclusions indicate that on randomly generated graphs minor changes to the distribution can cause dramatic changes in the performance of the heuristic algorithms. The Ramsey algorithm, while not as good as the others for the most common distributions, seems more robust and provides a more even overall performance. In general, and especially on deterministically generated graphs, a combination of simulated annealing with either the Ramsey algorithm or the greedy heuristic seems to perform best. This combined algorithm works particularly well on large Keller and Hamming graphs and has a competitive overall performance on the DIMACS benchmark graphs.

Mining Anomalies Using Traffic Feature Distributions

The increasing practicality of large-scale flow capture makes it possible to conceive of traffic analysis methods that detect and identify a large and diverse set of anomalies. However the challenge of effectively analyzing this massive data source for anomaly diagnosis is as yet unmet. We argue that the distributions of packet features (IP addresses and ports) observed in flow traces reveals both the presence and the structure of a wide range of anomalies. Using entropy as a summarization tool, we show that the analysis of feature distributions leads to significant advances on two fronts: (1) it enables highly sensitive detection of a wide range of anomalies, augmenting detections by volume-based methods, and (2) it enables automatic classification of anomalies via unsupervised learning. We show that using feature distributions, anomalies naturally fall into distinct and meaningful clusters. These clusters can be used to automatically classify anomalies and to uncover new anomaly types. We validate our claims on data from two backbone networks (Abilene and Geant) and conclude that feature distributions show promise as a key element of a fairly general network anomaly diagnosis framework.

Molecular composition of biogenic secondary organic aerosols using ultrahigh resolution mass spectrometry: comparing laboratory and field studies

Numerous laboratory experiments have been performed in an attempt to mimic atmospheric secondary organic aerosol (SOA) formation. However, it is still unclear how close the aerosol particles generated in laboratory experiments resemble atmospheric SOA with respect to their detailed chemical composition. In this study, we generated SOA in a simulation chamber from the ozonolysis of α-pinene and a biogenic volatile organic compound (BVOC) mixture containing α- and β-pinene, Δ3-carene, and isoprene. The detailed molecular composition of laboratory-generated SOA was compared with that of background ambient aerosol collected at a boreal forest site (Hyytiälä, Finland) and an urban location (Cork, Ireland) using direct infusion nanoelectrospray ultrahigh resolution mass spectrometry. Kendrick Mass Defect and Van Krevelen approaches were used to identify and compare compound classes and distributions of the detected species. The laboratory-generated SOA contained a distinguishable group of dimers that was not observed in the ambient samples. The presence of dimers was found to be less pronounced in the SOA from the VOC mixtures when compared to the one component precursor system. The elemental composition of the compounds identified in the monomeric region from the ozonolysis of both α-pinene and VOC mixtures represented the ambient organic composition of particles collected at the boreal forest site reasonably well, with about 70% of common molecular formulae. In contrast, large differences were found between the laboratory-generated BVOC samples and the ambient urban sample. To our knowledge this is the first direct comparison of molecular composition of laboratory-generated SOA from BVOC mixtures and ambient samples.

Analysing energy detector diversity receivers for spectrum sensing

The analysis of energy detector systems is a well studied topic in the literature: numerous models have been derived describing the behaviour of single and multiple antenna architectures operating in a variety of radio environments. However, in many cases of interest, these models are not in a closed form and so their evaluation requires the use of numerical methods. In general, these are computationally expensive, which can cause difficulties in certain scenarios, such as in the optimisation of device parameters on low cost hardware. The problem becomes acute in situations where the signal to noise ratio is small and reliable detection is to be ensured or where the number of samples of the received signal is large. Furthermore, due to the analytic complexity of the models, further insight into the behaviour of various system parameters of interest is not readily apparent. In this thesis, an approximation based approach is taken towards the analysis of such systems. By focusing on the situations where exact analyses become complicated, and making a small number of astute simplifications to the underlying mathematical models, it is possible to derive novel, accurate and compact descriptions of system behaviour. Approximations are derived for the analysis of energy detectors with single and multiple antennae operating on additive white Gaussian noise (AWGN) and independent and identically distributed Rayleigh, Nakagami-m and Rice channels; in the multiple antenna case, approximations are derived for systems with maximal ratio combiner (MRC), equal gain combiner (EGC) and square law combiner (SLC) diversity. In each case, error bounds are derived describing the maximum error resulting from the use of the approximations. In addition, it is demonstrated that the derived approximations require fewer computations of simple functions than any of the exact models available in the literature. Consequently, the regions of applicability of the approximations directly complement the regions of applicability of the available exact models. Further novel approximations for other system parameters of interest, such as sample complexity, minimum detectable signal to noise ratio and diversity gain, are also derived. In the course of the analysis, a novel theorem describing the convergence of the chi square, noncentral chi square and gamma distributions towards the normal distribution is derived. The theorem describes a tight upper bound on the error resulting from the application of the central limit theorem to random variables of the aforementioned distributions and gives a much better description of the resulting error than existing Berry-Esseen type bounds. A second novel theorem, providing an upper bound on the maximum error resulting from the use of the central limit theorem to approximate the noncentral chi square distribution where the noncentrality parameter is a multiple of the number of degrees of freedom, is also derived.

First principles simulation of amorphous silicon bulk, interfaces, and nanowires for photovoltaics

Amorphous silicon has become the material of choice for many technologies, with major applications in large area electronics: displays, image sensing and thin film photovoltaic cells. This technology development has occurred because amorphous silicon is a thin film semiconductor that can be deposited on large, low cost substrates using low temperature. In this thesis, classical molecular dynamics and first principles DFT calculations have been performed to generate structural models of amorphous and hydrogenated amorphous silicon and interfaces of amorphous and crystalline silicon, with the ultimate aim of understanding the photovoltaic properties of core-shell crystalline amorphous Si nanowire structures. We have shown, unexpectedly, from the simulations, that our understanding of hydrogenated bulk a-Si needs to be revisited, with our robust finding that when fully saturated with hydrogen, bulk a-Si exhibits a constant optical energy gap, irrespective of the hydrogen concentration in the sample. Unsaturated a-Si:H, with a lower than optimum hydrogen content, shows a smaller optical gap, that increases with hydrogen content until saturation is reached. The mobility gaps obtained from an analysis of the electronic states show similar behavior. We also obtained that the optical and mobility gaps show a volcano curve as the H content is varied from 7% (undersaturation) to 18% (mild oversaturation). In the case of mild over saturation, the mid-gap states arise exclusively from an increase in the density of strained Si-Si bonds. Analysis of our structures shows the extra H atoms in this case form a bridge between neighboring silicon atoms which increases the corresponding Si-Si distance and promotes bond length disorder in the sample. That has the potential to enhance the Staebler-Wronski effect. Planar interface models of amorphous-crystalline silicon have been generated in Si (100), (110) and (111) surfaces. The interface models are characterized by structure, RDF, electronic density of states and optical absorption spectrum. We find that the least stable (100) surface will result in the formation of the thickest amorphous silicon layer, while the most stable (110) surface forms the smallest amorphous region. We calculated for the first time band offsets of a-Si:H/c-Si heterojunctions from first principles and examined the influence of different surface orientations and amorphous layer thickness on the offsets and implications for device performance. The band offsets depend on the amorphous layer thickness and increase with thickness. By controlling the amorphous layer thickness we can potentially optimise the solar cell parameters. Finally, we have successfully generated different amorphous layer thickness of the a-Si/c-Si and a-Si:H/c-Si 5 nm nanowires from heat and quench. We perform structural analysis of the a-Si-/c-Si nanowires. The RDF, Si-Si bond length distributions, and the coordination number distributions of amorphous regions of the nanowires reproduce similar behaviour compared to bulk amorphous silicon. In the final part of this thesis we examine different surface terminating chemical groups, -H, - OH and –NH2 in (001) GeNW. Our work shows that the diameter of Ge nanowires and the nature of surface terminating groups both play a significant role in both the magnitude and the nature of the nanowire band gaps, allowing tuning of the band gap by up to 1.1 eV. We also show for the first time how the nanowire diameter and surface termination shifts the absorption edge in the Ge nanowires to longer wavelengths. Thus, the combination of nanowire diameter and surface chemistry can be effectively utilised to tune the band gaps and thus light absorption properties of small diameter Ge nanowires.

Finite mixture distributions, sequential likelihood and the EM algorithm

A popular way to account for unobserved heterogeneity is to assume that the data are drawn from a finite mixture distribution. A barrier to using finite mixture models is that parameters that could previously be estimated in stages must now be estimated jointly: using mixture distributions destroys any additive separability of the log-likelihood function. We show, however, that an extension of the EM algorithm reintroduces additive separability, thus allowing one to estimate parameters sequentially during each maximization step. In establishing this result, we develop a broad class of estimators for mixture models. Returning to the likelihood problem, we show that, relative to full information maximum likelihood, our sequential estimator can generate large computational savings with little loss of efficiency.

Potential consequences of climate change for primary production and fish production in large marine ecosystems

Existing methods to predict the effects of climate change on the biomass and production of marine communities are predicated on modelling the interactions and dynamics of individual species, a very challenging approach when interactions and distributions are changing and little is known about the ecological mechanisms driving the responses of many species. An informative parallel approach is to develop size-based methods. These capture the properties of food webs that describe energy flux and production at a particular size, independent of species' ecology. We couple a physical-biogeochemical model with a dynamic, size-based food web model to predict the future effects of climate change on fish biomass and production in 11 large regional shelf seas, with and without fishing effects. Changes in potential fish production are shown to most strongly mirror changes in phytoplankton production. We project declines of 30-60% in potential fish production across some important areas of tropical shelf and upwelling seas, most notably in the eastern Indo-Pacific, the northern Humboldt and the North Canary Current. Conversely, in some areas of the high latitude shelf seas, the production of pelagic predators was projected to increase by 28-89%.

Flexible C : N ratio enhances metabolism of large phytoplankton when resource supply is intermittent

Phytoplankton cell size influences particle sinking rate, food web interactions and biogeographical distributions. We present a model in which the uptake, storage and assimilation of nitrogen and carbon are explicitly resolved in different-sized phytoplankton cells. In the model, metabolism and cellular C :N ratio are influenced by the accumulation of carbon polymers such as carbohydrate and lipid, which is greatest when cells are nutrient starved, or exposed to high light. Allometric relations and empirical data sets are used to constrain the range of possible C : N, and indicate that larger cells can accumulate significantly more carbon storage compounds than smaller cells. When forced with extended periods of darkness combined with brief exposure to saturating irradiance, the model predicts organisms large enough to accumulate significant carbon reserves may on average synthesize protein and other functional apparatus up to five times faster than smaller organisms. The advantage of storage in terms of average daily protein synthesis rate is greatest when modeled organisms were previously nutrient starved, and carbon storage reservoirs saturated. Small organisms may therefore be at a disadvantage in terms of average daily growth rate in environments that involve prolonged periods of darkness and intermittent nutrient limitation. We suggest this mechanism is a significant constraint on phytoplankton C :N variability and cell size distribution in different oceanic regimes.