Binary Mixtures Of Nanoparticles Research Articles

Developmental effects on Daphnia magna induced by titanium dioxide and iron oxide mixtures

Developmental effects of a binary mixture of nanoparticles (NPs) composed of Titanium dioxide (TiO2) and iron oxide (Fe2O3) were identified in Daphnia magna using a chronic toxicity test. Survival, growth, reproduction, and age at first brood were measured throughout 21 days of exposure. Results from this study demonstrated that mixture exposure to NPs (TiO2 and Fe2O3) significantly affects the development of D. magna to maturation and disturbs the reproductive performance in a dose-dependent manner and beginning from the lower dose. Mortality recorded in control group (1 ± 0.365) was significantly lower than in experimental groups (6.94 ± 0.193, 7.19 ± 0.188, 7.62 ± 0.125, 7.62 ± 0.155 individual, in C1, C2, C3, and C4, respectively). Moreover, swimming performances (F4, 35 = 112.9, df = 4, p < 0.0001) and heart rate (F4, 25 = 19.37, df = 4, p < 0.0001) were also affected. In conclusion, the interaction of binary mixtures can exacerbate toxicological effects that have significant implications for appropriately assessing the ecotoxicological effects of emerging pollutants. Further investigation and the results reported in the present study will be useful in environmental policies.

Entropy-driven formation of binary superlattices assembled from polymer-tethered nanorods and nanospheres.

Integrating binary mixtures of nanoparticles (NPs) into well-defined superstructures gives rise to novel collective properties depending on the shapes and individual properties of both species. In this paper, we studied the entropy-driven formation of binary superlattices assembled from polymer-tethered nanorods and nanospheres. The results indicated that the conformational entropy of the polymer chains and the mixing entropy of the nanorods and nanospheres are two parameters that determine the formation of binary superlattices.

Computational Reverse-Engineering Analysis for Scattering Experiments (CREASE) with Machine Learning Enhancement to Determine Structure of Nanoparticle Mixtures and Solutions.

We present a new open-source, machine learning (ML) enhanced computational method for experimentalists to quickly analyze high-throughput small-angle scattering results from multicomponent nanoparticle mixtures and solutions at varying compositions and concentrations to obtain reconstructed 3D structures of the sample. This new method is an improvement over our original computational reverse-engineering analysis for scattering experiments (CREASE) method (ACS Materials Au2021, 1 (2 (2), ), 140−156), which takes as input the experimental scattering profiles and outputs a 3D visualization and structural characterization (e.g., real space pair-correlation functions, domain sizes, and extent of mixing in binary nanoparticle mixtures) of the nanoparticle mixtures. The new gene-based CREASE method reduces the computational running time by >95% as compared to the original CREASE and performs better in scenarios where the original CREASE method performed poorly. Furthermore, the ML model linking features of nanoparticle solutions (e.g., concentration, nanoparticles’ tendency to aggregate) to a computed scattering profile is generic enough to analyze scattering profiles for nanoparticle solutions at conditions (nanoparticle chemistry and size) beyond those that were used for the ML training. Finally, we demonstrate application of this new gene-based CREASE method for analysis of small-angle X-ray scattering results from a nanoparticle solution with unknown nanoparticle aggregation and small-angle neutron scattering results from a binary nanoparticle assembly with unknown mixing/segregation among the nanoparticles.

Experimental Testing of an Approach to Establishing Combined Toxicity of Ternary Nanoparticle Mixtures.

Our studies of exposure to binary mixtures of nanoparticles (TiO2 + SiO2; TiO2 + Al2O3 and SiO2 + Al2O3) based on mathematical modelling show that their combined subchronic toxicity can either be of an additive type or deviate from it depending on the outcome, dose ratio, and levels of effect. To characterize the type of toxicity of ternary mixtures of nanoparticles, we successfully tested a previously developed approach for assessing the combined toxicity of metal ions. In this approach, the effects are classified by a null, positive, or negative change in the toxicity of binary nanoparticle mixtures when modeled against the toxicity of the third agent added.

Computational Reverse-Engineering Analysis for Scattering Experiments of Assembled Binary Mixture of Nanoparticles.

In this paper, we describe a computational method for analyzing results from scattering experiments on dilute solutions of supraparticles, where each supraparticle is created by the assembly of nanoparticle mixtures. Taking scattering intensity profiles and nanoparticle mixture composition and size distributions in each supraparticle as input, this computational approach called computational reverse engineering analysis for scattering experiments (CREASE) uses a genetic algorithm to output information about the structure of the assembled nanoparticles (e.g., real space pair correlation function, extent of nanoparticle mixing/segregation, sizes of domains) within a supraparticle. We validate this method by taking as input in silico scattering intensity profiles from coarse-grained molecular simulations of a binary mixture of nanoparticles, forming a close-packed structure and testing if our computational method can correctly reproduce the nanoparticle structure observed in those simulations. We test the strengths and limitations of our method using a variety of in silico scattering intensity profiles obtained from simulations of a spherical or a cubic supraparticle comprising binary nanoparticle mixtures with varying chemistries, with and without dispersity in sizes, that exhibit well-mixed to strongly segregated structures. The strengths of the presented method include its capability to analyze scattering intensity profiles even when the wavevector q range is limited, to handily provide all of the pairwise radial distribution functions, and to correctly determine the extent of segregation/mixing of the nanoparticles assembled in complex geometries.

Quantitative 3D real-space analysis of Laves phase supraparticles

Assembling binary mixtures of nanoparticles into crystals, gives rise to collective properties depending on the crystal structure and the individual properties of both species. However, quantitative 3D real-space analysis of binary colloidal crystals with a thickness of more than 10 layers of particles has rarely been performed. Here we demonstrate that an excess of one species in the binary nanoparticle mixture suppresses the formation of icosahedral order in the self-assembly in droplets, allowing the study of bulk-like binary crystal structures with a spherical morphology also called supraparticles. As example of the approach, we show single-particle level analysis of over 50 layers of Laves phase binary crystals of hard-sphere-like nanoparticles using electron tomography. We observe a crystalline lattice composed of a random mixture of the Laves phases. The number ratio of the binary species in the crystal lattice matches that of a perfect Laves crystal. Our methodology can be applied to study the structure of a broad range of binary crystals, giving insights into the structure formation mechanisms and structure-property relations of nanomaterials.

Binary mixture of nanoparticles in sewage sludge: Impact on spinach growth

Present study carried out pot experiments and evaluated effects of single and binary mixture of nanoparticles (exposed via sludge as soil conditioner) on spinach plant. Exposure of Ag2O nanoparticles (NPs) (1 and 10 mg/kg soil-sludge) did not show significant reduction in plant as compared to control. On the other hand, TiO2 NPs (exposed as single and in binary mixture) resulted in significant increase in root length (29% and 37%) and fresh weight (60% and 48%) at highest exposure concentration. Total chlorophyll content decreased for Ag2O and binary mixture (7% and 4%, respectively) and increased for TiO2 (5%) at 10 mg/kg soil-sludge. The toxic interaction between Ag2O and TiO2 NPs was additive at both exposure concentrations. Ag2O NPs had higher tendency of root surface adsorption than TiO2 NPs. Metal content in spinach leaves at highest exposure concentration was Ag: 2.6 ± 0.55 mg/g plant biomass(for Ag2O NPs) and 1.02 ± 0.32 mg/g plant biomass (for Ag2O + TiO2 NPs) and for Ti: 1.12 ± 0.78 (for TiO2 NPs) mg/g plant biomass and 0.58 ± 0.41 mg/g (for Ag2O + TiO2 NPs). The inadvertent ingestion of NPs- contaminated spinach resulted in projected daily intake (DI) of Ag and Ti for different age-mass classes (child to adult) exceeding the oral reference dose for toxicity during oral ingestion. In conclusion, we report no acute toxicity of single and binary mixture of NPs to spinach but significant accumulation of Ag and Ti metals in spinach leaves. There are high chances that ingestion of spinach grown in such environment might lead to human health risks.

Surface composition and ordering of binary nanoparticle mixtures in spherical confinement

We perform coarse-grained Langevin dynamics simulations in shrinking spherical confinement to unravel the design parameters controlling the surface composition and near-surface structure during the emulsion assembly of binary nanoparticle mixtures.

Hierarchical complex self-assembly in binary nanoparticle mixtures

Hierarchical self-assembly of soft matter provides a powerful route to create complex materials with enhanced physical properties. The understanding of the fundamental processes leading to such organization can provide design rules to create new functional materials. In this work, we use a simple model of polymer-grafted nanoparticles to explore the self-assembly of binary mixtures. By using Monte Carlo simulations we study the interplay of composition, density and particle sizes on the self-organization of such nanoparticle systems. It is found that complex hierarchical organization can take place for conditions where one-component systems form simple lattices. In particular, a mixture where one component forms a structure with 18-fold symmetry in a sea of an apparent disordered phase of the second component is observed to emerge for certain parameter combinations.

Thermomechanical behavior and thermal stability of polyurethane rigid nanocomposite foams containing binary nanoparticle mixtures

Polyurethane (PU) rigid nanocomposite foams containing binary nanoparticle mixtures were prepared via reactive foaming process by using the synergy of mixing based on H-bonding interactions between nanofillers and polymer matrix. Using a very high surface area hydrophilic fumed nanosilica with a primary particle size of 7 nm and a concentration of less than 0.2 wt% in addition to carboxylic acid functionalized multiwalled carbon nanotubes (MWCNTs-COOH) in PU rigid foams leads to improved thermal stability, thermomechanical and mechanical properties. Particularly, the system containing both 0.4 wt % CNT and 0.1 wt% nanosilica has a much higher value in terms of T g , storage modulus value at T g , reduced compressive strength, thermal stability and cell density compared to the sample containing only 0.5 wt% CNT. Based on FT-IR results, favorable and enhanced H-bonding interactions of N–H and carbonyl functional groups of the urethane linkage in PU chains with surface silanols of hydrophilic nanosilica particles and carboxylic acid functional groups of MWCNT-COOH were shown to be the main reasons for the reinforcement effect in PU rigid foams. • H-bonding interactions induced synergy of mixing between nanoparticles and PU rigid foam. • The addition of 0.4 wt % CNT and 0.1 wt % nanosilica maximized H-bonding. • The addition of 0.4 wt % CNT and 0.1 wt % nanosilica improved thermomechanical properties. • The addition of 0.4 wt % CNT and 0.1 wt % nanosilica improved reduced compressive strength, thermal stability. • The addition of CNT and more than 0.2 wt % nanosilica decreased thermomechanical properties, thermal stability.

Many-body effects in a binary nano-particle mixture dispersed in ideal polymer solutions.

A new mean-field theory is developed to treat a binary mixture of nanoparticles imbedded in a polydisperse polymer solution. The theory is based on a many-body polymer-mediated potential of mean force (PMF) between the particles and remains accurate even in the protein regime, where the particles' diameters cannot necessarily be considered large compared to the polymer radius of gyration. As implemented here, the theory is strictly valid for dilute to semi-dilute polymer solutions near the theta temperature (the so-called theta regime) or when the range of the PMF is strongly affected by the polymer size. For non-adsorbing particles, this is the same regime where the celebrated Asakura-Oosawa (AO) model is often used. Unlike the traditional AO model, however, our approach includes polymer flexibility and is accurate in the protein regime. We use the theory to calculate phase diagrams for a binary mixture of unequal-sized particles, both adsorbing and non-adsorbing. To test the theory, we carry out comparisons with simulations and obtained good quantitative agreement, which gives support to its accuracy. On the other hand, the oft-used approach assuming pairwise-additive potentials of mean force produce quantitatively (and sometime qualitatively) different phase diagrams.

Assembly of Zeolitic Crystals From a Model of Mesogenic Patchy Nanoparticles

Porous zeolitic crystals made of nanoparticles could have interesting applications in membranes and photonics. However, there is not yet a synthetic pathway to realize these complex architectures using nanoparticles. The challenge arises from the difficulty to control independently the different length scales intrinsic to the ordering of zeolitic crystals: the short-range tiling of the typically tetravalent sites that form the crystal, and the medium- and long-range order and connectivity of the pores. Here we propose a strategy to nucleate and grow zeolitic crystals from a binary mixture of nanoparticles in which one component favors tetravalent coordination and the two types of particles interact through frustrated attraction that promotes the formation of mesophases. We use molecular simulations to demonstrate that this approach produces zeolitic crystals in which the ordering of the pores is controlled by the structure of the mesophase, while the tiling of the network-former particles is controlled b...

Cluster-mediated assembly enables step-growth copolymerization from binary nanoparticle mixtures with rationally designed architectures.

Directed co-assembly of binary nanoparticles (NPs) into one-dimensional copolymer-like chains is fascinating but challenging in the realm of material science. While many strategies have been developed to induce the polymerization of NPs, it remains a grand challenge to produce colloidal copolymers with widely tailored compositions and precisely controlled architectures. Herein we report a robust colloidal polymerization strategy, which enables the growth of sophisticated NP chains with elaborately designed structures. By quantifying NP assembly statistics and kinetics, we establish that the linear assembly of colloidal NPs, with the assistance of PbSO4 clusters, follows a step-growth polymerization mechanism, and on the basis of this, we design and fabricate NP chains structurally analogous to random, block, and alternating copolymers, respectively. Our studies offer mechanistic insights into cluster-mediated colloidal polymerization, paving the way toward the rational synthesis of colloidal copolymers with quantitatively predicted architectures and functionalities.

Impact of Irrigation Using Water Containing CuO and ZnO Nanoparticles on Spinach oleracea Grown in Soil Media.

Wastewater reuse is an important adaptation option for mitigating water stress in rapidly growing urban centers. Reuse potential of nanoparticles (NPs) contaminated wastewater for irrigation of Spinacia oleracea grown in soil media were assessed in this study. Irrigation of plant were done with water containing CuO and ZnO NPs as single compound and in binary mixture (10, 100, 1000mg/L) till 11weeks. At 1000mg/L, reduction in root length: 16%, 12% and 18%, shoot length: 22%, 16% and 27%, total weight 37%, 27% and 45%, chlorophyll: 18%, 7% and 29% and carotenoids: 46%, 33% and 54% were found for CuO NPs, ZnO NPs and binary mixture of NPs respectively. Uptake values were found to be 5.65±0.8 Zn(2+) and 3.48±0.75 Cu(2+) mg/g for the case of ZnO and CuO NPs respectively (at 1000mg/L). For mixture of NPs, uptake of 3.18±1.05mg/g of Cu(2+) and 3.18±1.05mg/g of Zn(2+) ions were found. The results shows that water containing low concentration of NPs (10mg/L) can be used for irrigating spinach grown in soil media as no significant toxic effect on growth and uptake of metal ion were found as compared to control. The results of this study evaluated the suitability of reusing water contaminated with NPs in agriculture. Further studies are however required to understand the toxic mode of action of mixture of NPs on growth and uptake mechanisms.

Fluidization behavior of binary iron-containing nanoparticle mixtures in a vibro-fluidized bed

The present work is focused on the fluidization behavior of different mixed binary nanoparticles (Fe2O3+SiO2, Fe2O3+TiO2, and Fe2O3+ZnO) under the influence of a vibrated field. The results reveal that the iron-containing nanoparticle mixtures with different mass fractions can achieve uniform fluidization in the initial bed height of 40mm, amplitude of 3.0mm, vibration frequency of 40Hz, and binary mixtures containing nano-SiO2 and nano-Fe2O3 have commendable fluidization behavior. Richardson–Zaki equation index indicates that the particulate dispersion degree of the different proportion mixtures increases as the mass fraction of APF (agglomerate particulate fluidization) nanoparticle increases. For a binary nanoparticle mixture containing different proportions of high content nano-SiO2 and nano-Fe2O3, n/4.65 is closer to 1 and the relative deviation is smaller than that containing a high content TiO2 and ZnO, indicating that the system has a good dispersibility.

Pressure-controlled formation of crystalline, Janus, and core-shell supraparticles.

Binary mixtures of nanoparticles self-assemble in the confinement of evaporating oil droplets and form regular supraparticles. We demonstrate that moderate pressure differences on the order of 100 kPa change the particles' self-assembly behavior. Crystalline superlattices, Janus particles, and core-shell particle arrangements form in the same dispersions when changing the working pressure or the surfactant that sets the Laplace pressure inside the droplets. Molecular dynamics simulations confirm that pressure-dependent interparticle potentials affect the self-assembly route of the confined particles. Optical spectrometry, small-angle X-ray scattering and electron microscopy are used to compare experiments and simulations and confirm that the onset of self-assembly depends on particle size and pressure. The overall formation mechanism reminds of the demixing of binary alloys with different phase diagrams.

Linking experiment and theory for three-dimensional networked binary metal nanoparticle–triblock terpolymer superstructures

Controlling superstructure of binary nanoparticle mixtures in three dimensions from self-assembly opens enormous opportunities for the design of materials with unique properties. Here we report on how the intimate coupling of synthesis, in-depth electron tomographic characterization and theory enables exquisite control of superstructure in highly ordered porous three-dimensional continuous networks from single and binary mixtures of metal nanoparticles with a triblock terpolymer. Poly(isoprene-block-styrene-block-(N,N-dimethylamino)ethyl methacrylate) is synthesized and used as structure-directing agent for ligand-stabilized platinum and gold nanoparticles. Quantitative analysis provides insights into short- and long-range nanoparticle-nanoparticle correlations, and local and global contributions to structural chirality in the networks. Results provide synthesis criteria for next-generation mesoporous network superstructures from binary nanoparticle mixtures for potential applications in areas including catalysis.

Agglomerate Sizes of Binary Nanoparticle Mixtures in a Vibro‐Fluidized Bed

AbstractAgglomerate size is one of the key factors influencing the fluidization behavior of nanoparticles. The effect of fluidization time, superficial gas velocity, and vibration frequency on agglomerate sizes for different binary mixtures of nanoparticles at the top of the bed was investigated in a vibrated fluidized bed (VFB). The agglomerate sizes decreased and fluidization quality was significantly enhanced owing to introduction of vibration energy. The Richardson‐Zaki equation combined with Stokes' law permitted the prediction of mean agglomerate sizes. Experimental and estimated results indicated that vibration led to a smaller agglomerate size. The mean predicted agglomerate sizes were in agreement with those determined experimentally in the VFB.

Fluidization behavior of binary mixtures of nanoparticles in vibro-fluidized bed

The fluidization behavior of different mixed SiO2, TiO2 and/or ZnO nanoparticles under the application of vibrated fields of constant vibrated frequencies (40Hz) and amplitude (3.0mm) is studied. The single nanoparticles experiments show that SiO2 nanoparticles have a better fluidization quality than TiO2 and ZnO nanoparticles. For binary mixtures of the nanoparticles, the amount of SiO2 nanoparticles generally has a beneficial effect on the fluidization quality of the binary mixtures. Using the linear regression, the Richardson–Zaki exponents of three kinds single and their binary mixture of nanoparticles are calculated. The Richardson–Zaki analyses indicate that the particulate fluidization degree of mixed nanoparticles can be greatly improved in agglomerate particulate fluidization (APF) behavior.

Random Lasing in Self‐Assembled Dye‐Doped Latex Nanoparticles: Packing Density Effects

AbstractEfficient random lasing (RL) from self‐assembled dye‐doped latex nanoparticles (d = 50–380 nm) presenting size polydispersity is reported. This new system exhibits a very good chemical compatibility between dye (Rhodamine 6G) and polymer as well as a high refractive index contrast between nanoparticle and surroundings (air), in such a way that its emission properties surpass the ones previously reported in similar systems. Furthermore, this system allows analyzing in detail the effects of the nanoparticle size polydispersity and the packing density on the RL emission properties. It is shown that size polydispersity gives rise to non‐uniformities in the filling fraction along the sample that lead to fluctuations on the scattering length across the sample and thus to a variation on the emission properties. Finally, it is observed that the increase of the filling fraction, enabled by the use of binary mixtures of nanoparticles with different sizes, results in remarkable improvements in the RL emission properties.