Random Aggregates Research Articles

Prediction on damage evolution of recycled crumb rubber concrete using quantitative cloud imagine correlation

Abstract To account for the improvement of mechanical properties for recycled crumb rubber concrete (RCRC) under uniaxial compression and flexural toughness test in detail, three groups of RCRC specimen covering contents of rubber aggregate from 10 percent to 30 percent and one group of normal concrete (NC) specimen were prepared. Experimental results present distinguished influence of rubber particle content on uniaxial compressive strength and failure mode. Then, improvement in toughness properties was displayed inherently through change of the index named ratio of flexural strength to compressive one ( R f - c ). Simultaneously, a mesoscopic numerical model on random aggregate of RCRC was built up and validated by comparison of results from damage simulating to in-door testing. Furthermore, the mechanism of ductility enhancement for RCRC is unveiled visually and the prediction on damage evolution is revealed in quantitative way by a novel methodology of quantitative cloud imagine correlation (QCIC) based on 2D analysis. Subsequently, the similar law is established by a 3D Extended QCIC named volumetric damage accumulation method (VDAM). Therefore, it put forward a step further from state-of-the-art research about mechanical properties of RCRC, which may promote the utility of optimal design for this ecological material in civil engineering.

Rational design of a sandwiched structure Ni(OH)2 nanohybrid sustained by amino-functionalized graphene quantum dots for outstanding capacitance

The rational structure design of nanohybrids plays a vital role in the supercapacitor performance of electrode materials. Herein, a novel sandwiched structure based on Ni(OH)2 and amino-functionalized graphene quantum dots (Ni(OH)2/af-GQDs) is prepared via a facile green strategy. The layered Ni(OH)2 nanosheets are spaced by amino groups from af-GQDs to afford a sandwiched configuration with an enlarged pore diameter of 1.8 nm, which leads to the decrease of random aggregation of Ni(OH)2 nanosheets and dramatically facilitates the ion transport, thus contributing to the increased surface area and improved electrochemical capacitance. Besides, the introduction of af-GQDs leads to enhanced electrical conductivity and better cycling stability as well as contributing additional electrochemical double-layer capacitance, resulting in an outstanding specific capacitance of 2653 F g−1 at 1 A g−1. An asymmetric supercapacitor (ASC) based on Ni(OH)2/af-GQDs and electrochemical-exfoliated graphene (EG) is assembled, exhibiting a wide potential window range, a high energy density, and an excellent cycling stability with 90.15% capacitance retention. These results definitely demonstrate the great potential of Ni(OH)2-based hybrid materials in the development of high-performance supercapacitors.

Kruijenite, Ca4Al4(SO4)F2(OH)16·2H2O, a new mineral with microporous structure from the Eifel paleovolcanic region, Germany

The new mineral kruijenite, ideally Ca4Al4(SO4)F2(OH)16·2H2O, was found in a calcic xenolith from tephra of the Feuerberg paleovolcano situated near Daun, Eifel paleovolcanic region, Rhineland-Palatinate, Germany. It is associated with fluorite, calcite, aragonite, cuspidine, magnesioferrite, hematite, sharyginite, harmunite, and an insufficiently investigated hydrous Ca-Mg-Al silicate. Kruijenite occurs as pale greenish-yellow to colourless long prismatic tetragonal crystals up to 0.1 mm × 1 mm in cavities typically combined in radiating or random aggregates. The mineral is brittle, with Mohs’ hardness of 3; Dcalc is 2.573 g/cm3. The IR spectrum is given. Kruijenite is optically uniaxial (−), ω = 1.576(3), e = 1.561(3). The chemical composition (wavelength dispersive spectrometer Oxford INCA Wave 700 EPMA analyser, H2O calculated from structural data) is: CaO 32.38 wt%, Al2O3 27.75 wt%, Cr2O3 1.45 wt%, SO3 8.09 wt%, F 5.84 wt%, H2O 25.64 wt%, –O=F –2.46 wt%, total 98.69 wt%. The empirical formula is Ca4.00(Al3.77Cr0.13)Σ3.90(SO4)0.70F2.13(OH)16.17·1.79H2O (Z = 2). Kruijenite is tetragonal, space group P4/ncc, a = 12.9299(4) A, c = 5.2791(3) A, V = 882.57(6) A3, Z = 2. The crystal structure was solved and refined to R = 0.121. Kruijenite represents a novel structure type. Its structure is based on the microporous pseudo-framework built by Al(OH)6 octahedra and CaF2(OH)6 polyhedra. The strongest reflections of the powder X-ray diffraction pattern [d (I) (hkl)] are: 9.12 A (77%) (110), 4.565 A (100%) (220), 4.084 A (50%) (310), 2.964 A (74%) (321), 2.694 A (27%) (411), 2.321 A (24%) (431), 2.284 A (29%) (511), 2.217 A (22%) (321, 530), 1.971 A (40%) (611). The mineral is named in honor of the Dutch collector of Eifel minerals Fred Kruijen (born in 1956).

Gluten aggregation behavior in gluten and gluten-starch doughs after wheat bran dietary fiber addition

The influences of wheat bran dietary fiber (WBDF) on the structural changes and aggregation behavior of gluten in both gluten (G) and gluten-starch (G + S) systems were investigated. Six WBDF levels were provided in the G and G + S system to reveal the correlations between WBDF level and gluten depolymerization behavior. Results showed that WBDF caused a significant reduction in SS content and impaired water holding capacity, implying possible disaggregation behavior and less compact gluten network structure. The size exclusion chromatography profile also exhibited the loss of high molecular weight gluten aggregates in both G and G + S system. The Fourier transform-infrared spectroscopy results reveled an increase in β-sheet (∼55%) conformation at the cost of the β-turn structure, whereas random coils and aggregates tended to decrease. It may be speculated that the transformation of β-turn into β-sheet structure be responsible for the disaggregation behavior of gluten aggregates and the more rigid gluten network.

AIE active fluorescent organic nanoaggregates for selective detection of phenolic-nitroaromatic explosives and cell imaging

Development of organic nanoparticles with high fluorescence, good biocompatibility along with strong resistance to photobleaching through simple synthetic routes is important for diverse applications such as sensing and bioimaging. Herein, we present the development of a pyrene excimer nanoaggregate which shows aggregation induced emission (AIE) effect in a solvent mixture of THF and water. The pyrene based fluorescent probe, dimethyl-5-(pyren-1-ylmethyleneamino)isophthalate (5-DP) was synthesized through a simple single step condensation reaction from inexpensive reagents. The photophysical studies of nanoaggregated system further corroborates the AIE active behavior of 5-DP probe at different water fractions (ƒw = 0% to 90%), where the hydrogen bonding interaction between imine and water molecules led to suppression of photoinduced electron transfer (PET) inducing significant enhancement in fluorescence. The highly photostable nanoaggregates were explored as a selective fluorescence “turn off” sensor for phenolic nitroaromatics and the chemo-selectivity was highly pronounced for 2,4,6-trinitrophenol (picric acid), that showed efficient quenching in aqueous as well as solid phase, with a detection limit of 10 nM in aqueous medium. The quenching efficiency of the nanoaggregates can be ascribed to a combination of factors including efficient fluorescence resonance energy transfer, inner filter effect and coulombic interaction between picric acid and the aggregated probe molecules. Further, random aggregation of the pyrene derivative could be controlled for the formation of fluorescent spherical nanoparticles using Pluoronics P-123 block copolymers as encapsulating agents. The resulting composite could be used as a neoteric cell imaging probe with significantly less cytotoxicity, thus showing their potential biological applications.

Mesoscopic Numerical Simulation of Fracture Process and Failure Mechanism of Concrete Based on Convex Aggregate Model

To investigate the fracture process and failure mechanism of concrete subjected to uniaxial compressive loading, a new finite element method—the base force element method (BFEM)—was adopted in the modeling of numerical simulation. At mesoscale, concrete is considered as a three-phase heterogeneous material composed of aggregate particles, cement mortar, and the interfacial transition zones between the two phases. A two-dimensional random convex aggregate model was established using the principle of the area equivalence method. A multistage linear damage constitutive model that can describe nonlinear behavior of concrete under mechanical stress was proposed. The mechanical properties of concrete mesoscopic components are determined. The numerical simulation results indicate that the base force element method can be applied to predict the failure pattern of concrete under compressive loading, which have a good accordance with the available experiment data. The stress contour plots were given and used to analyze the failure mechanism of concrete. The effects of specimen size on the strength of concrete material were studied. It is found that compressive strength of concrete decreases as the specimen size increases. In addition, the influences of aggregate distribution, coarse aggregate content, and end friction on concrete performance are explored.

Numerical study of ITZ contribution on diffusion of chloride and induced rebar corrosion: A discussion of three-dimensional multiscale approach

Modeling approach for mesoscopic model of concrete depicting mass transportation and physicochemical reaction is important since there is growing demand for accuracy and computational efficiency of numerical simulation. Mesoscopic numerical simulation considering binder, aggregate and interfacial transition zone (ITZ) generally produces huge number of DOFs, which is inapplicable for full structure. In this paper, a three-dimensional multiscale approach describing three-phase structure of concrete was discussed numerically. An effective approach generating random aggregate in polygon based on checking centroid distance and intersection of line segment was introduced. Moreover, ITZ elements were built by parallel expanding the surface of aggregates on inner side. By combining mesoscopic model including full-graded aggregate and macroscopic model, cases related to diffusivity and thickness of ITZ, volume fraction and grade of aggregate were studied regarding the consideration of multiscale compensation. Result clearly showed that larger analysis model in multiscale model expanded the diffusion space of chloride ion and decreased chloride content in front of rebar. Finally, this paper addressed some worth-noting conclusions about the chloride distribution and rebar corrosion regarding the configuration of, rebar diameter, concrete cover and exposure period.

Natural rubber-SiO2 nanohybrids: interface structures and dynamics.

Homogeneous dispersion of silica nanoparticles (SiO2 NPs) in natural rubber (NR) is a key challenge for engineering high-performance nanocomposites and elucidation of their structure on a molecular basis. Towards this, the present work devised a novel route for obtaining 3D self-assembled SiO2 NP-NR nanocomposites under aqueous conditions and in the presence of Mg2+, by establishing a molecular bridge that clamped the negatively charged NR and SiO2 colloidal particles with a favoured NR-SiO2 NP hetero-aggregation. The characteristic NR-SiO2 NP hetero-aggregates displayed a decreased heat capacity with increase in the SiO2 mass-fraction, implying a restricted NR chain mobility. Such changes in the interfacial layers were tapped by 29Si NMR, DFT calculations and molecular dynamics simulations towards a mechanistic understanding of the structure and dynamics of the NR/SiO2 NP hybrid. Simple models were used to illustrate basic ideas; specific electrostatic interactions such as ion-dipole and H-bonding interactions proved to be the driving forces for the organized assembly leading to the NR-SiO2 hetero-aggregate over the NR-NR or SiO2 NP-SiO2 NP homo-aggregate. Molecular dynamics simulation of the aqueous canonical ensemble of the hybrid showed the stable molecular conformation to reveal a SiO2 NP spherical core encapsulated by a hydrophobically interconnected NR polymer layer as the outer shell, as a unique structural model. Specifically, the lipid end of the NR was involved electrostatically while the lysine end (the protein part of NR) H-bonded to the core silica cluster thereby restricting random aggregation. The calculated negative free energy changes for the hetero-aggregate composites via their vibrational and rotational spectra proved the spontaneity of composite formation.

Numerical Study on Diffusion of Chloride and Induced Rebar Corrosion by Two-Dimensional Multiscale Approach

Modeling approach for mesoscopic model of concrete depicting mass transportation and physicochemical reaction is important since there is growing demand for accuracy and computational efficiency of numerical simulation. Mesoscopic numerical simulation considering binder, aggregate, and interfacial transition zone (ITZ) generally produces huge number of DOFs, which is inapplicable for full structure. In this paper, a two-dimensional multiscale approach describing three-phase structure of concrete was discussed numerically. An effective approach generating random aggregate in polygon based on checking centroid distance and intersection of line segment was introduced. Moreover, ITZ elements were built by parallel expanding the edge of aggregates on inner side. By combining mesoscopic model including full-graded aggregate and macroscopic model, cases related to diffusivity and width of ITZ, volume fraction, and grade of aggregate were studied regarding the consideration of multiscale compensation. Result clearly showed that larger analysis model in multiscale model expanded the diffusion space of chloride ion and decreased chloride content in front of rebar. Finally, this paper addressed some noteworthy conclusions about the chloride distribution and rebar corrosion regarding the configuration of rebar diameter, concrete cover, and exposure period.

ClaMPP: a cloud-based multi-party privacy preserving classification scheme for distributed applications

Privacy preservation in a distributed environment is a challenging task as it requires efficient control strategies for authentication and integrity preservation of various users and applications. In a distributed environment, most of the data items are distributed among various parties which may be located across different geographical locations. In the literature, most of the techniques proposed for privacy preserving consider only two parties collaboration for data items sharing using data perturbation and homomorphic encryption technique. Since data perturbation is less accurate and homomorphic encryption incurs high computation cost, so to fill these gaps, this paper proposes a Cloud-based Multi-party Privacy Preserving Classification Scheme (ClaMPP), which uses Naive Bayesian Classifier using multi-party random masking and polynomial aggregation technique for privacy preservation among different parties. The proposed ClaMPP scheme consists of four protocols which are described as follows. Protocols I and II are used to calculate conditional probabilities between the item vectors in a secure manner by using Privacy Preserving Conditional Probability Protocol. In the protocol III, the prior probabilities between the item vectors are computed by using additive property of Paillier homomorphic encryption technique. The naive Bayesian classification model constructed from the calculated values from protocols I, II and III is used in protocol IV to generate the predictions for items based on user’s interest. Three datasets MovieLens100K, MovieLens20M and Jester are used in ClaMPP to analyse the privacy, accuracy and overhead costs incurred. It has been experimentally demonstrated that the proposed scheme is secure, and the privacy preservation does not affect the accuracy of the prediction. Comparative analysis of ClaMPP has been done with other related schemes based on off-line model computation cost. It has been demonstrated from the results obtained that computation cost decreases significantly in comparison with the other state-of-art techniques.

Uncertainty in the effects of the modifiable areal unit problem under different levels of spatial autocorrelation: a simulation study

ABSTRACTThe objective of this paper is to investigate uncertainties surrounding relationships between spatial autocorrelation (SA) and the modifiable areal unit problem (MAUP) with an extensive simulation experiment. Especially, this paper aims to explore how differently the MAUP behaves for the level of SA focusing on how the initial level of SA at the finest spatial scale makes a significant difference to the MAUP effects on the sample statistics such as means, variances, and Moran coefficients (MCs). The simulation experiment utilizes a random spatial aggregation (RSA) procedure and adopts Moran spatial eigenvectors to simulate different SA levels. The main findings are as follows. First, there are no substantive MAUP effects for means. However, the initial level of SA plays a role for the zoning effect, especially when extreme positive SA is present. Second, there is a clear and strong scale effect for the variances. However, the initial SA level plays a non-negligible role in how this scale effect deploys. Third, the initial SA level plays a crucial role in the nature and extent of the MAUP effects on MCs. A regression analysis confirms that the initial SA level makes a substantial difference to the variability of the MAUP effects.

Physico-chemical chlorophyll-a species in aqueous alcohol solutions determine the rate of its discoloration under UV light.

Chlorophyll-a (Chl-a) discolors when it is exposed to light, and such discoloration decreases food quality. To elucidate the discoloration mechanism of Chl-a, we determined discoloration rate in different Chl-a chemical species and assessed the size of Chl-a aggregates in mixed aqueous solutions of methanol and ethanol. Chl-a existed as monomer, J-aggregate, and random aggregate in solutions with different alcohol concentrations. The predominant species depended on the alcohol concentration. Monomeric Chl-a and J-aggregates discolored quickly, whereas random aggregates discolored slowly. Particle sizes of J-aggregates were 319 and 2305 nm in diameter in aqueous solutions of methanol and ethanol, respectively. The sizes of random aggregates were 51 and 79 nm in 10% (v/v) aqueous solutions of methanol and ethanol, respectively. The size of Chl-a aggregates positively correlated with the rate of Chl-a discoloration under UV light. Based on the results obtained, we propose a mechanism of Chl-a discoloration.

Discrimination and conversion prediction of mild cognitive impairment using convolutional neural networks.

Recently, studies have demonstrated that machine learning techniques, particularly cutting-edge deep learning technology, have achieved significant progression on the classification of Alzheimer's disease (AD) and its prodromal phase, mild cognitive impairment (MCI). Moreover, accurate prediction of the progress and the conversion risk from MCI to probable AD has been of great importance in clinical application. In this study, the baseline MR images and follow-up information during 3 years of 150 normal controls (NC), 150 patients with stable MCI (sMCI) and 157 converted MCI (cMCI) were collected from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The deep convolutional neural networks (CNNs) were adopted to distinguish different stages of MCI from the NC group, and predict the conversion time from MCI to AD. Two CNN architectures including GoogleNet and CaffeNet were explored and evaluated in multiple classifications and estimations of conversion risk using transfer learning from pre-trained ImageNet (via fine-tuning) and five-fold cross-validation. A novel data augmentation approach using random views aggregation was applied to generate abundant image patches from the original MR scans. The GoogleNet acquired accuracies with 97.58%, 67.33% and 84.71% in three-way discrimination among the NC, sMCI and cMCI groups respectively, whereas the CaffeNet obtained promising accuracies of 98.71%, 72.04% and 92.35% in the NC, sMCI and cMCI classifications. Furthermore, the accuracy measures of conversion risk of patients with cMCI ranged from 71.25% to 83.25% in different time points using GoogleNet, whereas the CaffeNet achieved remarkable accuracy measures from 95.42% to 97.01% in conversion risk prediction. The experimental results demonstrated that the proposed methods had prominent capability in classification among the 3 groups such as sMCI, cMCI and NC, and exhibited significant ability in conversion risk prediction of patients with MCI.

Nanoassembly of Dipolar Imidazoanthraquinone Derivatives Leading to Enhanced Hole Mobility

Imidazoanthraquinone-based high dipolar molecules (AQ01 and AQ02) were synthesized and characterized. Photophysical properties in various solvents suggested the polar nature of the ground state for these materials. In addition, fluorescence quenching experiments with the commonly used electron donor poly-3-hexylthiophene (P3HT) in bulk heterojunction (BHJ) solar cells ascertained the electron acceptor properties of these molecules. Theoretical calculations based on density functional theory (DFT) gave insight on the dipolar nature, H-bonding, and π–π interaction in different types of supramolecular assemblies of AQ01 and AQ02. Calculations predicted that the π–π interaction via antiparallel orientation and H-bonding with the OH···OH interaction is favored energetically in AQ01. Morphological studies on thermally evaporated thin films indicated interconnected nanoassemblies in AQ01 while random aggregates in AQ02. Charge transport properties of these molecules were estimated for AQ01/AQ02 and their blends ...

Jamming and tiling in aggregation of rectangles

We study a random aggregation process involving rectangular clusters. In each aggregation event, two rectangles are chosen at random and if they have a compatible side, either vertical or horizontal, they merge along that side to form a larger rectangle. Starting with N identical squares, this elementary event is repeated until the system reaches a jammed state where each rectangle has two unique sides. The average number of frozen rectangles scales as in the large-N limit. The growth exponent characterizes statistical properties of the jammed state and the time-dependent evolution. We also study an aggregation process where rectangles are embedded in a plane and interact only with nearest neighbors. In the jammed state, neighboring rectangles are incompatible, and these frozen rectangles form a tiling of the two-dimensional domain. In this case, the final number of rectangles scales linearly with system size.

Bayesian Modeling of Motion Perception Using Dynamical Stochastic Textures.

A common practice to account for psychophysical biases in vision is to frame them as consequences of a dynamic process relying on optimal inference with respect to a generative model. The study presented here details the complete formulation of such a generative model intended to probe visual motion perception with a dynamic texture model. It is derived in a set of axiomatic steps constrained by biological plausibility. We extend previous contributions by detailing three equivalent formulations of this texture model. First, the composite dynamic textures are constructed by the random aggregation of warped patterns, which can be viewed as three-dimensional gaussian fields. Second, these textures are cast as solutions to a stochastic partial differential equation (sPDE). This essential step enables real-time, on-the-fly texture synthesis using time-discretized autoregressive processes. It also allows for the derivation of a local motion-energy model, which corresponds to the log likelihood of the probability density. The log likelihoods are essential for the construction of a Bayesian inference framework. We use the dynamic texture model to psychophysically probe speed perception in humans using zoom-like changes in the spatial frequency content of the stimulus. The human data replicate previous findings showing perceived speed to be positively biased by spatial frequency increments. A Bayesian observer who combines a gaussian likelihood centered at the true speed and a spatial frequency dependent width with a "slow-speed prior" successfully accounts for the perceptual bias. More precisely, the bias arises from a decrease in the observer's likelihood width estimated from the experiments as the spatial frequency increases. Such a trend is compatible with the trend of the dynamic texture likelihood width.

One-step chromatographic method to purify α-lactalbumin from whey for nanotube synthesis purposes

A one-step anion-exchange chromatography method (NaCl gradient elution on a DEAE Sepharose™ Fast Flow gel column) was developed to purify α-lactalbumin (α-LA) from whey protein isolate. α-LA nearly 100% pure (based on the total protein content) was obtained with a yield of about 39%. Besides pure α-LA, which was the main objective of this work, highly pure β-lactoglobulin was also obtained with a yield of about 59%. The high purity of the obtained α-LA samples allowed its use to synthesise protein nanotubes with excellent gelation properties for their use as food thickeners and bioactive carriers. The samples’ purity degree obtained (based on the total protein content) was critical in the formation of proper nanotubes instead of random aggregates, which produced opaque and weak gels, less useful for food applications.

A novel numerical scheme for random parameterized convex aggregate models with a high-volume fraction of aggregates in concrete-like granular materials

Concrete at a mesoscopic scale is normally regarded as a three-phase composite consisting of cement paste, aggregates and their surrounding interfacial transition zones (ITZs). Establishing mesostructure model close to realistic concrete is very crucial to precisely evaluate its mechanical properties. The preponderance of previous investigations has focused on aggregates as circles, ellipses or polygons constructed by the assembly of sides-sides with a low packing density, and little is known about precise mathematical characterizations for polygonal aggregates with a high packing density more than 60% and their surrounding ITZs. In this work, a novel numerical framework that adopts the deformation of a rhombus to mathematically provide a parametric equation of convex polygon characterizing the geometrical morphology of aggregates, is proposed to generate random polygonal aggregate models (RPAMs) with a high packing density. In this framework, a fast-random packing algorithm (FRPA) is developed to generate a high packing density of aggregates of 70%. Based on the parametric equation of polygonal aggregates, the geometrical topology of ITZs is mathematically realized with a convenient manner, rather than those cumbersome approximate operations reported in the literature. Moreover, the present numerical framework can be extended to the three-dimensional case.

Structure of sticky-hard-sphere random aggregates: The viewpoint of contact coordination and tetrahedra.

We study more than 10^{4} random aggregates of 10^{6} monodisperse sticky hard spheres each, generated by various static algorithms. Their packing fraction varies from 0.370 up to 0.593. These aggregates are shown to be based on two types of disordered structures: random regular polytetrahedra and random aggregates, the former giving rise to δ peaks on pair distribution functions. Distortion of structural (Delaunay) tetrahedra is studied by two parameters, which show some similarities and some differences in terms of overall tendencies. Isotropy of aggregates is characterized by the nematic order parameter. The overall structure is then studied by distinguishing spheres in function of their contact coordination number (CCN). Distributions of average CCN around spheres of a given CCN value show trends that depend on packing fraction and building algorithms. The radial dependence of the average CCN turns out to be dependent upon the CCN of the central sphere and shows discontinuities that resemble those of the pair distribution function. Moreover, it is shown that structural details appear when the CCN is used as pseudochemical parameter, such as various angular distribution of bond angles, partial pair distribution functions, Ashcroft-Langreth and Bhatia-Thornton partial structure factors. These allow distinguishing aggregates with the same values of packing fraction or average tetrahedral distortion or even similar global pair distribution function, indicative of the great interest of paying attention to contact coordination numbers to study more precisely the structure of random aggregates.

Ring-Like Assembly of Silica Nanospheres in the Presence of Amphiphilic Block Copolymer: Effects of Particle Size.

Block copolymer-mediated self-assembly of colloidal nanoparticles has attracted great attention for the fabrication of a wide variety of nanoparticle arrays. We have previously shown that silica nanospheres (SNSs) 15 nm in diameter assemble into ring-like nanostructures in the presence of amphiphilic block copolymers poly[(2-ethoxyethyl vinyl ether)- block-(2-methoxyethyl vinyl ether)] (EOVE-MOVE) in an aqueous phase. Here, the effects of particle size of SNSs on this polymer-mediated self-assembly are studied systematically using scanning electron microscopy to observe SNSs of seven different sizes between 13 to 42 nm. SNSs of 13, 16, 19, and 21 nm in diameter assemble into nanorings in the presence of EOVE-MOVE. In contrast, larger SNSs of 26, 34, and 42 nm aggregate heavily, form chain-like networks, and remain dispersed, respectively, instead of forming ring-like nanostructures. The assembly trend for 26-42 nm-SNSs agrees with that expected from the increased colloidal stability for larger particles. Time-course observation for the assembled morphology of 16 nm-SNSs reveals that the nanorings, once formed, assemble further into network-like structures, as if the nanorings behave as building units for higher-order assembly. This indicates that the ring-like assembly is a fast process that can proceed onto random colloidal aggregation. Detailed analysis of nanoring structures revealed that the average number of SNSs comprising one ring decreased from 5.0 to 3.1 with increasing the SNS size from 13 to 21 nm. A change in the number of ring members was also observed when the length of EOVE-MOVE varied while the size of SNSs was fixed. Dynamic light scattering measurements and atomic force microscopy confirmed the SNSs/polymer composite structures. We hypothesize that a stable composite morphology may exist that is influenced by both the size of SNSs and the polymer molecular structures.