Initial Cluster Formation Research Articles

Comparative study of flame-based SiO2 nanoparticle synthesis from TMS and HMDSO: SiO-LIF concentration measurement and detailed simulation

Depending on the chemical nature of precursor species, the flame-based synthesis of silicon dioxide nanoparticles in lean hydrogen/oxygen flames proceeds via different chemical routes, which affects the generated particle characteristics. Modeling the flame chemistry and particle formation therefore can provide valuable understanding of the underlying gas-phase and particle-formation pathways. In the present study we compare experimentally obtained temperature and semi-quantified SiO-concentration profiles in low-pressure (3 kPa), lean (ϕ < 0.6), inert-gas diluted H2/O2/Ar burner-stabilized flat flames doped with 200–4000 ppm hexamethyldisiloxane (HMDSO) or tetramethylsilane (TMS) with results from kinetics modeling. Temperature fields in the flames were determined via multi-line laser-induced fluorescence (LIF) imaging using both added NO and native SiO as target species. Gas-phase silicon monoxide (SiO) was detected via LIF by exciting the rovibrational Q(42) transition in the A1Π–X1Σ+ (1,0) vibronic band system at 230.998 nm that provides a weak temperature dependence when analyzing relative SiO mole fractions. Semi-quantitative SiO mole-fraction profiles as a function of height-above-burner (HAB) were obtained for all flames from the measured SiO-LIF intensities corrected for variations of the temperature-dependent ground-state population and the collisional quenching using measured temperatures and effective fluorescence lifetimes, respectively. The experimental data were compared with results of appropriate chemical kinetics mechanisms from the literature with suitable modifications to best reproduce measured SiO mole-fraction profiles. Modeling initial cluster formation is important in this study to unravel the observed ‘double-peak’-structure of the SiO concentration profiles assumed to originate from resublimed SiO from early-formed SiO2 nanoparticles in the rising temperature gradient during initial particle nucleation, and which may be altered by the availability of oxygen in the precursor species.

Providing Healthcare-as-a-Service Using Fuzzy Rule Based Big Data Analytics in Cloud Computing

With advancements in information and communication technology, there is a steep increase in the remote healthcare applications in which patients can get treatment from the remote places also. The data collected about the patients by remote healthcare applications constitute big data because it varies with volume, velocity, variety, veracity, and value. To process such a large collection of heterogeneous data is one of the biggest challenges which requires a specialized approach. To address this challenge, a new fuzzy rule based classifier is presented in this paper with an aim to provide Healthcare-as-a-Service. The proposed scheme is based upon the initial cluster formation, retrieval, and processing of the big data in cloud environment. Then, a fuzzy rule based classifier is designed for efficient decision making for data classification in the proposed scheme. To perform inferencing from the collected data, membership functions are designed for fuzzification and defuzzification processes. The proposed scheme is evaluated on various evaluation metrics, such as average response time, accuracy, computation cost, classification time, and false positive ratio. The results obtained confirm the effectiveness of the proposed scheme with respect to various performance evaluation metrics in cloud computing environment.

Heparin-dependent aggregation of hen egg white lysozyme reveals two distinct mechanisms of amyloid fibrillation

Heparin, a biopolymer possessing high negative charge density, is known to accelerate amyloid fibrillation by various proteins. Using hen egg white lysozyme, we studied the effects of heparin on protein aggregation at low pH, raised temperature, and applied ultrasonic irradiation, conditions under which amyloid fibrillation was promoted. Heparin exhibited complex bimodal concentration-dependent effects, either accelerating or inhibiting fibrillation at pH 2.0 and 60 °C. At concentrations lower than 20 μg/ml, heparin accelerated fibrillation through transient formation of hetero-oligomeric aggregates. Between 0.1 and 10 mg/ml, heparin rapidly induced amorphous heteroaggregation with little to no accompanying fibril formation. Above 10 mg/ml, heparin again induced fibrillation after a long lag time preceded by oligomeric aggregate formation. Compared with studies performed using monovalent and divalent anions, the results suggest two distinct mechanisms of heparin-induced fibrillation. At low heparin concentrations, initial hen egg white lysozyme cluster formation and subsequent fibrillation is promoted by counter ion binding and screening of repulsive charges. At high heparin concentrations, fibrillation is caused by a combination of salting out and macromolecular crowding effects probably independent of protein net charge. Both fibrillation mechanisms compete against amorphous aggregation, producing a complex heparin concentration-dependent phase diagram. Moreover, the results suggest an active role for amorphous oligomeric aggregates in triggering fibrillation, whereby breakdown of supersaturation takes place through heterogeneous nucleation of amyloid on amorphous aggregates.

Multi-cell nuclei segmentation in cervical cancer images by integrated feature vectors

Automated analysis of cervical cancer images is considered as an attractive research in the biological fields. Due to the intensive advances in the digital technology and the light microscopy, the cellular imaging requires the continuous growing importance. Sophisticated methods are adopted to isolate the nuclei from the cytoplasm based on the boundary estimation and the analysis of intensity of blue cells to improve the abnormality prediction. The cell-based segmentation evolved in research studies assures the automatic assistance with an assumption of a single cell. Previous work [21] concentrates on the segmentation of abnormal region on single-cell images. This paper extends that work into the multi-cell images with the addition of geometrical features. The complex cell structure, poor contrast, and overlapping affect the cell segmentation performance. This paper enhances the performance of single-cell segmentation with the integrated feature vectors of geometrical (area, cell size, cell intensity and the maximum intensity) and Gray level Co-occurrence Matrix (GLCM) to improve the abnormality level prediction.. Initially, the Neighborhood Concentric Filtering (NCF) is applied on the input slides to remove the noise present in the image and enhance the intensity level. Then, the initial level cluster formation and masking are performed on the noise-free image. The Optimal Weight Updating with the Multi-Level set (OWU-ML) estimates the Region of Interest (ROI) and segments the blue cell and cytoplasm. The clear analysis of blue cell indicates the exact classification of abnormal levels in the images. The combination of geometrical and GLCM extracts the texture pattern features of the blue cell, cytoplasm and the nucleus portions in the form of angle variations. Finally, the Neural Network-based RVM classifier predicts the classes of (normal and abnormal) cervical images. The integration of novel methods such as OWU-ML segmentation, GLCM + geometrical feature extraction and NN-RVM classification improves the abnormal prediction performance and assures the suitability in multi-cell cervical image handling in biological applications.

Evolution of defects in nanoporous gold with annealing temperature using low-energy positron beam experiments

Defects in nanoporous gold (np-Au) samples, prepared by selective dissolution of silver from a binary Ag75Au25 alloy, with annealing temperature have been investigated using the positron beam Doppler line shape S-parameter. It is shown that there is an initial formation of vacancy clusters arising due to the aggregation of thermally migrating vacancies up to 473 K, followed by the breakup of these clusters at higher temperatures, coincident with the rapid growth of ligaments. In addition to the defect-specific S-parameter, the low-energy positron beam experiments have also been used to evaluate the variation of the effective positron diffusion length in np-Au with annealing temperature. The variation of effective positron diffusion length with the ligament diameter, d, scales as which points to the diffusion of positrons along the ligament length, that is also substantiated through measurement of the positronium fraction.

Collapse Dynamics of Core–Shell Nanogels

Stimuli-responsive core–shell nanogels display collapse properties which are determined by both the core and the shell. We examine the equilibrium properties and the detailed structural changes during a collapse transition of polymer core–shell nanoparticles using Brownian dynamics simulations. Gel particles with randomly distributed cross-linking nodes are created. The influence of the cross-linking degree, core/shell mass ratio, and the strength of the interparticle attractive interaction on the collapse behavior is investigated. Both collapsed core and collapsed shell structures are considered and compared with collapsed homopolymer networks. The transition time was found to be reduced with increasing cross-linking degree and inversely related to the depth of the Lennard-Jones potential. Similar to the kinetics of single chain collapse, there is an initial formation of clusters, in this case near cross-linking nodes, and a subsequent coarsening to form a compact globule. Where the nanogels were collapsed into a compact core, the deformation was found to be essentially symmetric, with a significantly slower relaxation time for the shell units compared to the core collapse transition time. Reducing the core size, the shell units were less affected by the presence of a collapsing core. For the system with a collapsing shell, our simulations reveal in some cases an inversion, with the shell compressing and eventually squeezing out the core units. This effect was more pronounced with decreasing cross-linking degree and core/shell mass ratio.

Templated green synthesis of plasmonic silver nanoparticles in onion epidermal cells suitable for surface-enhanced Raman and hyper-Raman scattering.

We report fast and simple green synthesis of plasmonic silver nanoparticles in the epidermal cells of onions after incubation with AgNO3 solution. The biological environment supports the generation of silver nanostructures in two ways. The plant tissue delivers reducing chemicals for the initial formation of small silver clusters and their following conversion to plasmonic particles. Additionally, the natural morphological structures of the onion layers, in particular the extracellular matrix provides a biological template for the growth of plasmonic nanostructures. This is indicated by red glowing images of extracellular spaces in dark field microscopy of onion layers a few hours after AgNO3 exposure due to the formation of silver nanoparticles. Silver nanostructures generated in the extracellular space of onion layers and within the epidermal cell walls can serve as enhancing plasmonic structures for one- and two-photon-excited spectroscopy such as surface enhanced Raman scattering (SERS) and surface enhanced hyper-Raman scattering (SEHRS). Our studies demonstrate a templated green preparation of enhancing plasmonic nanoparticles and suggest a new route to deliver silver nanoparticles as basic building blocks of plasmonic nanosensors to plants by the uptake of solutions of metal salts.

Effect of Cu and Ge on solute clustering in Al–Mg–Si alloys

The clustering kinetics after quenching and during natural ageing in pure or Cu/Ge-containing Al–Mg–Si alloys was studied by positron annihilation lifetime spectroscopy and hardness measurements. The evolution of positron lifetime follows qualitatively the same pattern for all the alloys investigated, but also differs in some details: adding Cu to both Al–Mg–Si and Al–Mg–Ge alloys slows down the initial formation of clusters in all alloys. However, in Al–Mg–Si alloys, it promotes their growth in later stages. For Ge-containing Al–Mg–Si alloys with equal Mg contents, Ge notably retards the ageing kinetics compared to Si. Possible reasons of these effects are related to the interactions between vacancies and solutes or solute clusters and to the diffusion kinetics of various solute-vacancy complexes.

Study of Self-Induced Growth of AlGaAs Nanoneedles on Silicon Substrates Using Metal Organic Chemical Vapor Deposition Technique.

A simple catalyst free growth method was used for the growth of single crystalline AlGaAs nanoneedles on Si substrate by metal organic chemical vapor deposition (MOCVD) technique. The growth mechanism of catalyst free growth of nanoneedles was investigated. The effect of growth rate, growth temperature and V/III ratio was studied in detail. The growth of nanoneedles required a careful optimization of the growth conditions. The formation of well-faceted nanoneedles with hexagonal cross-section was found to be influenced by the growth parameters. Based on these studies, the growth mechanism has been explained using nucleation theory. The growth of nanoneedles was believed to proceed via Vapor-Solid (VS) growth mechanism after the initial AlGaAs cluster formation depending on the growth conditions.

Dynamics of ultrathin gold layers on vitreous silica probed by density functional theory.

The structure and properties of Au ultrathin films on hydroxyl-free and hydroxylated silica glass surfaces are investigated using ab initio molecular dynamics simulations. Substantial surface structure dependence of Au agglomeration behavior (solid-state dewetting) is found. On hydroxyl-free surfaces, the Au film virtually undergoes instantaneous agglomeration accompanied by the formation of voids exposing a bare silica glass surface. In contrast, simulated annealing of the Au film on hydroxylated surface models leaves its structure unchanged within the simulation time. This points to a key role of reactive defect sites in the kinetics of solid-state dewetting processes of metals deposited on the glass surface. Such sites are important for initial void nucleation and formation of metal clusters. In addition, our calculations demonstrate the crucial role of the appropriate inclusion of dispersion interactions in density functional theory simulations of metals deposited on glass surfaces. For defective, hydroxyl-free glass surfaces the dispersion correction accounts for 35% of the total adhesion energy. The effect is even more dramatic for hydroxylated glass surfaces, where adhesion energies are almost entirely due to dispersion interactions. The Au adhesion energies of 200 and 160 kJ (mol nm(2))(-1) calculated for hydroxylated glass surfaces are in good agreement with the experimental data.

New particle formation and growth from methanesulfonic acid, trimethylamine and water

New particle formation from gas-to-particle conversion represents a dominant source of atmospheric particles and affects radiative forcing, climate and human health. The species involved in new particle formation and the underlying mechanisms remain uncertain. Although sulfuric acid is commonly recognized as driving new particle formation, increasing evidence suggests the involvement of other species. Here we study particle formation and growth from methanesulfonic acid, trimethylamine and water at reaction times from 2.3 to 32 s where particles are 2-10 nm in diameter using a newly designed and tested flow system. The flow system has multiple inlets to facilitate changing the mixing sequence of gaseous precursors. The relative humidity and precursor concentrations, as well as the mixing sequence, are varied to explore their effects on particle formation and growth in order to provide insight into the important mechanistic steps. We show that water is involved in the formation of initial clusters, greatly enhancing their formation as well as growth into detectable size ranges. A kinetics box model is developed that quantitatively reproduces the experimental data under various conditions. Although the proposed scheme is not definitive, it suggests that incorporating such mechanisms into atmospheric models may be feasible in the near future.

Ageing Characteristics of Al-Mg-(Ge,Si)-Cu Alloys

In order to elucidate some of the differences between Al-Mg-Si and Al-Mg-Ge alloys and the role of Cu, a series of Al-Mg-Ge, Al-Mg-Si and Al-Mg-Ge-Si alloys, some of them containing Cu, are investigated by positron annihilation lifetime spectroscopy during natural ageing. Al-Mg-Ge alloys show qualitatively the same evolution of positron lifetime τ1C with time as Al-Mg-Si alloys, namely an initial decrease, followed by a re-increase, after which τ1C drops to an equilibrium value. However, for alloys with equal Mg contents, Ge gives rise to a notably slower ageing kinetics than Si, pointing at effects of atomic size or solute-vacancy binding energies. Adding Cu to both Al-Mg-Ge and Al-Mg-Si alloys slows down the initial formation of clusters but promotes their further growth.

Probing the local atomic structure of Sr-modified Al–Si alloys

Extended X-ray absorption fine structure (EXAFS) spectroscopy and atom probe tomography (APT) measurements were jointly used for the first time to probe the local structure around Sr atoms in Al–10%Sr master alloy and in Al–3%Si–0.04%Sr and Al–12.5%Si–0.04%Sr eutectic alloys in order to study the impact of trace levels of Sr on the morphological changes occurring in Al–Si binary eutectic alloy. EXAFS analysis shows the oxidation states of Sr is close to Sr0 in all the alloys studied. In the Al–10%Sr master alloy, Sr atoms tend to form the intermetallic compound Al4Sr. On the other hand, in Al–Si alloys, Sr atoms segregate towards Si-rich regions, preferentially bonding to Si atoms to form Al2Si2Sr-like clusters with a coordination environment consistent with bulk Al2Si2Sr. APT study reveals the presence of nanometer-sized Al-rich and Si-rich regions and support the EXAFS results. We speculate that addition of trace levels of Sr to Al–Si binary alloy system results in the initial formation of Al2Si2Sr-like clusters and the presence of such intermetallic clusters could play an important role such as poisoning of nucleation sites, thereby delaying the nucleation of eutectic phases and causing the morphological changes of the eutectic phases in the Al–Si alloys.

Electrochemical Reduction of CO2 Using Supported Cu2O Nanoparticles

We have studied the electrochemical reduction of CO2 using Cu2O catalysts prepared by nanoparticle synthesis (i.e., chemical reduction) and by electrochemical deposition. Both preparation methods give catalysts that exhibit high formation rates for CO and C2H4 relative to CH4, compared to Cu foil electrodes. The main difference is a higher relative rate of CO formation using the nanoparticle catalysts. Highly loaded nanoparticle catalysts give the highest overall rates for CO2 product formation, most likely because of their greater surface area. Lower loaded catalysts show evidence that product selectivity is affected by the competing H2 reaction on the carbon fiber support. Characterization using SEM and XRD together with transient current behavior suggests that Cu2O is reduced in two stages, with the initial formation of dispersed Cu clusters occurring rapidly on the oxide surface, followed by a much slower reduction of the interior of the oxide.

Sunfleck effects on physiology, growth, and local demography of American ginseng (Panax quinquefolius L.)

The light environment of the forest understory is heterogeneous, characterized by low levels of diffuse light punctuated by short periods of direct solar radiation known as sunflecks. Less well understood is the degree to which sunflecks are responsible for spatially varying performance of understory plants. We studied natural variation in the light environment, as well as physiology, growth, and microsite demographics of American ginseng (Panax quinquefolius L.) to test whether clustering of plants within a population was related to sunfleck variation. HOBO dataloggers were positioned in 10 clusters within a single ginseng population to measure the local understory light environment during mid-season (June 27 to August 9 2011). Photosynthesis and transpiration were measured with a Li-Cor 6400 portable photosynthesis system. Population census data were used to obtain measurements of the relative growth rate (2011–2012), seed production efficiency (2011) and seed germination (2009–2012) of plants in the same subpopulations. All measured dependent variables were regressed against the percentage of mean photosynthetic photon flux density contributed from sunflecks and mean length of the longest sunfleck. Light saturated photosynthetic rates of ginseng showed a positive linear relationship to both sunfleck characteristics, while transpiration was unaffected. The relative growth rate of established ginseng plants also had a positive linear relationship with the length of the longest sunfleck. However, germination rate was negatively affected by an increasing percentage of photosynthetic photon flux density from sunflecks. Seed production efficiency was not influenced by either sunfleck characteristic. Due to negative effects on germination, initial ginseng cluster formation is likely not associated with sunflecks, however photosynthetic and growth responses suggest that fitness effects are positive for established individuals. A spatially and temporally varying light environment is beneficial for ginseng population growth.

Formation Mechanism of Silver Nanoparticles Stabilized in Glassy Matrices

In any given matrix control over the final particle size distribution requires a constitutive understanding of the mechanisms and kinetics of the particle evolution. In this contribution we report on the formation mechanism of silver nanoparticles embedded in a soda-lime silicate glass matrix. For the silver ion-exchanged glass it is shown that at temperatures below 410 °C only molecular clusters (diameter <1 nm) are forming which are most likely silver dimers. These clusters grow to nanoparticles (diameter >1 nm) by annealing above this threshold temperature of 410 °C. It is evidenced that the growth and thus the final silver nanoparticle size are determined by matrix-assisted reduction mechanisms. As a consequence, particle growth proceeds after the initial formation of stable clusters by addition of silver monomers which diffuse from the glass matrix. This is in contrast to the widely accepted concept of particle growth in metal-glass systems, in which it is assumed that the nanoparticle formation is predominantly governed by Ostwald ripening processes.

A Secure Cluster Formation Scheme in Wireless Sensor Networks

In wireless sensor networks, clustering expedites many desirable functions such as load balancing, energy savings, and distributed key management. For secure clustering, it is very important to find compromised nodes and remove them during the initial cluster formation process. If some nodes are compromised and survive the censorship process, they can make some nodes have a different cluster view and can split a cluster into multiple clusters to deteriorate cluster quality as a whole. To resolve these problems, we propose a robust scheme against such attacks in this paper. First, our scheme generates large-sized clusters where any two nodes are at most two hops away from each other to raise the quality of clusters. Second, our scheme employs the verification of two-hop distant nodes to preserve the quality of the large-sized clusters and refrains from splitting the clusters. Last, our scheme prefers broadcast transmissions to save the energy of nodes. Security analysis proves that our scheme can identify compromised nodes and preserves the cluster membership agreement against the compromised nodes. In addition, simulation results prove that our scheme generates fewer clusters and is more secure and energy efficient than the scheme producing only small-sized clusters.

A Spatial Model for Integrin Clustering as a Result of Feedback between Integrin Activation and Integrin Binding

Integrins are transmembrane adhesion receptors that bind extracellular matrix (ECM) proteins and signal bidirectionally to regulate cell adhesion and migration. In many cell types, integrins cluster at cell-ECM contacts to create the foundation for adhesion complexes that transfer force between the cell and the ECM. Even though the temporal and spatial regulation of these integrin clusters is essential for cell migration, how cells regulate their formation is currently unknown. It has been shown that integrin cluster formation is independent of actin stress fiber formation, but requires active (high-affinity) integrins, phosphoinositol-4,5-bisphosphate (PIP2), talin, and immobile ECM ligand. Based on these observations, we propose a minimal model for initial formation of integrin clusters, facilitated by localized activation and binding of integrins to ECM ligands as a result of biochemical feedback between integrin binding and integrin activation. By employing a diffusion-reaction framework for modeling these reactions, we show how spatial organization of bound integrins into clusters may be achieved by a local source of active integrins, namely protein complexes formed on the cytoplasmic tails of bound integrins. Further, we show how such a mechanism can turn small local increases in the concentration of active talin or active integrin into integrin clusters via positive feedback. Our results suggest that the formation of integrin clusters by the proposed mechanism depends on the relationships between production and diffusion of integrin-activating species, and that changes to the relative rates of these processes may affect the resulting properties of integrin clusters.

Dynamic Document Clustering Using Singular Value Decomposition

Incremental document clustering is important in many applications, but particularly so in healthcare contexts where text data is found in abundance, ranging from published research in journals to day-to-day healthcare data such as discharge summaries and nursing notes. In such dynamic environments new documents are constantly added to the set of documents that have been used in the initial cluster formation. Hence it is important to be able to incrementally update the clusters at a low computational cost as new documents are added. In this paper the authors describe a novel, low cost approach for incremental document clustering. Their method is based on conducting singular value decomposition (SVD) incrementally. They dynamically fold in new documents into the existing term-document space and dynamically assign these new documents into pre-defined clusters based on intra-cluster similarity. This saves the cost of re-computing SVD on the entire document set every time updates occur. The authors also provide a way to retrieve documents based on different window sizes with high scalability and good clustering accuracy. They have tested their proposed method experimentally with 960 medical abstracts retrieved from the PubMed medical library. The authors’ incremental method is compared with the default situation where complete re-computation of SVD is done when new documents are added to the initial set of documents. The results show minor decreases in the quality of the cluster formation but much larger gains in computational throughput.

A Scalable Clustering Algorithm in Dense Mobile Sensor Networks

Clustering offers a kind of hierarchical organization to provide scalability and basic performance guarantee by partitioning the network into disjoint groups of nodes. In this paper a scalable and energy efficient clustering algorithm is proposed under dense mobile sensor networks scenario. In the initial cluster formation phase, our proposed scheme features a simple execution process with polynomial time complexity, and eliminates the “frozen time” requirement by introducing some GPS-capable mobile nodes to act as cluster heads. In the following cluster maintenance stage, the maintenance of clusters is asynchronously and event driven so as to thoroughly eliminate the “ripple effect” brought by node mobility. As a result local changes in a cluster need not be seen and updated by the entire network, thus bringing greatly reduced communication overheads and being well suitable for the high mobility environment. Extensive simulations have been conducted and the simulation results reveal that our proposed algorithm successfully achieves its target at incurring much less clustering overheads as well as maintaining much more stable cluster structure, as compared to HCC(High Connectivity Clustering) algorithm