Structural identification of percolation of nanoparticles

Musino, Dafne; Genix, Anne-Caroline; Chauveau, Edouard; Bizien, Thomas; Oberdisse, Julian

doi:10.1039/c9nr09395h

Cited by 27 publications

(39 citation statements)

References 60 publications

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“…To mark the ϕ D→A , we quantify the extent of nanorod aggregation by calculating the weight-averaged aggregation number ⟨N agg 2 ⟩/⟨N agg ⟩. 18,57 In Figure 5a−5c, the plots of log(⟨N agg 2 ⟩/⟨N agg ⟩) as a function of ϕ ranging from 0.10 to 0.20 show the dispersion to aggregation transition as a function of ϕ for all roughnesses. We mark ϕ D→A by the value of ϕ at which the log(⟨N agg 2 ⟩/⟨N agg ⟩) increases sharply (e.g., for N = 80 and roughness 4, the ϕ D→A = 0.17).…”

Section: T H Imentioning

confidence: 99%

“…The results presented so far describing the effect of varying nanorod roughness on positional and orientational order with increasing ϕ lead us to hypothesize that for each nanorod roughness the PNC undergoes a dispersed to aggregated phase transition with increasing ϕ and that this dispersion–aggregation transition volume fraction, ϕ D→A , increases with nanorod roughness. To mark the ϕ D→A , we quantify the extent of nanorod aggregation by calculating the weight-averaged aggregation number ⟨ N agg 2 ⟩/⟨ N agg ⟩. , In Figure a–c, the plots of log(⟨ N agg 2 ⟩/⟨ N agg ⟩) as a function of ϕ ranging from 0.10 to 0.20 show the dispersion to aggregation transition as a function of ϕ for all roughnesses. We mark ϕ D→A by the value of ϕ at which the log(⟨ N agg 2 ⟩/⟨ N agg ⟩) increases sharply (e.g., for N = 80 and roughness 4, the ϕ D→A = 0.17).…”

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confidence: 99%

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Effect of Nanorod Physical Roughness on the Aggregation and Percolation of Nanorods in Polymer Nanocomposites

Jayaraman

2021

ACS Macro Lett.

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Using molecular dynamics simulations, we elucidate the effect of nanorod roughness on nanorod aggregation, dispersion, and percolation in polymer nanocomposites (PNCs). By choosing coarse-grained models that enable systematic variation of the nanorod roughness and by selecting purely repulsive pairwise interactions for nanorods and polymer chains, we show how nanorod roughness affects the entropic driving forces for various PNC morphologies. At this entropically driven limit, we find that increasing nanorod roughness hinders nanorod aggregation and promotes nanorod percolation in the polymer melt. As nanorod roughness increases, the nanorod volume fraction needed to induce nanorod aggregation also increases. Increasing nanorod roughness increases the configurational entropy of the polymer chains and lowers the entropically induced depletion attraction between nanorods.

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Section: T H Imentioning

confidence: 99%

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confidence: 99%

Effect of Nanorod Physical Roughness on the Aggregation and Percolation of Nanorods in Polymer Nanocomposites

Jayaraman

2021

ACS Macro Lett.

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“…To quantify these aspects, the structure of polymer nanocomposites is usually characterized by transmission electron microscopy (TEM), [13][14][15][16] and in particular small-angle X-ray or neutron scattering (SAXS or SANS), 17 combined with appropriate modelling approaches, [18][19][20][21] possibly including simulations. [22][23] The dynamical properties of PNCs may be studied by broadband dielectric spectroscopy (BDS) [24][25][26][27] or NMR. [28][29] These techniques average over the macroscopic response of the samples to external fields, without spatial resolution.…”

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confidence: 99%

Resolving Segmental Polymer Dynamics in Nanocomposites by Incoherent Neutron Spin–Echo Spectroscopy

et al. 2020

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The segmental dynamics of styrene-butadiene nanocomposites with embedded silica nanoparticles (NPs, ca. 20%v) has been studied by broadband dielectric (BDS) and neutron spin-echo spectroscopy (NSE). It is shown by BDS that overlapping contributions only allow to conclude on a range of distributions of relaxation times in simplified industrial nanocomposites formed with highly polydisperse NPs. For comparison, structurally similar but less aggregated colloidal nanocomposites have a well-defined distribution of relaxation times due to the reduced influence of interfacial polarization processes. This distribution is widened with respect to the neat polymer, without change in the position of the maximum, and at most a small slowing down visible in the average time. We then demonstrate that incoherent NSE can be used to resolve small modifications of segmental dynamics of the industrial samples. By carefully choosing the q-vector of the measurement, experiments with fully hydrogenated polymer give access to the self-dynamics of the polymer in the presence of silica on the scale of approximately 1 nm. Our high resolution measurements show that the segmental motion is slightly but systematically slowed down also by the presence of the industrial filler NPs.

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“…This can guide us to determine the critical distance . Musino et al 41 investigated closest distance (range is from 0.4 to 2.0 radius of the fillers) between fillers to define as an aggregate. None of the studies discussed how critical distance depends on the material.…”

Section: Resultsmentioning

confidence: 99%

“…37 Recent developments in small-angle X-ray scattering technology make it possible to predict fillers' size, shape, and distribution in soft polymeric materials. Musino et al 41 correlate rheological measurements to SAXS analysis to determine at which concentration percolation threshold is achieved. Based on the calculated percolation threshold, they define aggregates in the system.…”

Section: Definition Of An Agglomeratementioning

confidence: 99%

A predictive model towards understanding the effect of reinforcement agglomeration on the stiffness of nanocomposites

Demir

Benkaddour

Aldrich

et al. 2022

Journal of Composite Materials

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Nanocomposite technologies can be significantly enhanced through a careful exploration of the effects of agglomerates on mechanical properties. Existing models are either overly simplified (e.g., neglect agglomeration effects) or often require a significant amount of computational resources. In this study, a novel continuum-based model with a statistical approach was developed. The model is based on a modified three-phase Mori–Tanaka model, which accounts for the filler, agglomerate, and matrix regions. Fillers are randomly dispersed in a defined space to predict agglomeration tendency. The proposed model demonstrates good agreement with the experimentally measured elastic moduli of spin-coated cellulose nanocrystal reinforced polyamide-6 films. The techniques and methodologies presented in the study are sufficiently general in that they can be extended to the analyses of various types of polymeric nanocomposite systems.

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Structural identification of percolation of nanoparticles

Abstract: Percolation is identified with a static scattering method on the scale of 1/qmin (here micron size), in agreement with rheological measurements of the storage modulus, and supported by electron microscopy.

Cited by 27 publications

References 60 publications

Effect of Nanorod Physical Roughness on the Aggregation and Percolation of Nanorods in Polymer Nanocomposites

Effect of Nanorod Physical Roughness on the Aggregation and Percolation of Nanorods in Polymer Nanocomposites

Resolving Segmental Polymer Dynamics in Nanocomposites by Incoherent Neutron Spin–Echo Spectroscopy

A predictive model towards understanding the effect of reinforcement agglomeration on the stiffness of nanocomposites

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