Ref HAL: hal-03358744_v1
Ref Arxiv: 2106.12669
DOI: 10.1021/acs.macromol.1c01394
WoS: 000703552500031
Ref. & Cit.: NASA ADS
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21 Citations
Résumé:

Using molecular dynamics simulations, we study the static and dynamic properties of spherical nanoparticles (NPs) embedded in a disordered and polydisperse polymer network. Purely repulsive and weakly attractive polymer–NP interactions are considered. It is found that for both types of particles, the NP dynamics at intermediate and long times is controlled by the confinement parameter C = σN/λ, where σN is the NP diameter and λ is the dynamic localization length of the cross-links. Three dynamical regimes are identified: (i) for weak confinement (C ≲ 1), the NPs can freely diffuse through the mesh; (ii) for strong confinement (1 ≲ C ≲ 3), NPs proceed by means of activated hopping; (iii) for extreme confinement (C ≳ 3), the mean-squared displacement shows on intermediate time scales a quasi-plateau because the NPs are trapped by the mesh for very long times. Escaping from this local cage is a process that depends strongly on the local environment, thus giving rise to an extremely heterogeneous relaxation dynamics. The simulation data are compared with the two main theories for the diffusion process of NPs in gels. Both theories give a very good description of the C dependence of the NP diffusion constant but fail to reproduce the heterogeneous dynamics at intermediate time scales.

Ref HAL: hal-03234741_v1
DOI: 10.1021/acs.macromol.1c00176
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Résumé:

Due to their unique structural and mechanical properties, randomly cross-linked polymer networks play an important role in many different fields, ranging from cellular biology to industrial processes. In order to elucidate how these properties are controlled by the physical details of the network (e.g., chain-length and end-to-end distributions), we generate disordered phantom networks with different cross-linker concentrations C and initial densities ρ init and evaluate their elastic properties. We find that the shear modulus computed at the same strand concentration for networks with the same C, which determines the number of chains and the chain-length distribution, depends strongly on the preparation protocol of the network, here controlled by ρ init. We rationalize this dependence by employing a generic stress−strain relation for polymer networks that does not rely on the specific form of the polymer end-to-end distance distribution. We find that the shear modulus of the networks is a nonmonotonic function of the density of elastically active strands, and that this behavior has a purely entropic origin. Our results show that if short chains are abundant, as it is always the case for randomly cross-linked polymer networks, the knowledge of the exact chain conformation distribution is essential for correctly predicting the elastic properties. Finally, we apply our theoretical approach to literature experimental data, qualitatively confirming our interpretations.

Ref HAL: hal-03184640_v1
Ref Arxiv: 2102.13214
DOI: 10.1016/j.coastaleng.2021.103976
WoS: WOS:000702874300002
Ref. & Cit.: NASA ADS
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Résumé:

The Miles theory of wave amplification by wind is extended to the case of finite depth h and a shear flow with (constant) vorticity {\Omega}. Vorticity is characterised through the non-dimensional parameter {\nu} = {\Omega} U_1 /g, where g the gravitational acceleration, U_1 a characteristic wind velocity and k the wavenumber. The notion of 'wave age' is generalised to account for the effect of vorticity. Several widely used growth rates are derived analytically from the dispersion relation of the wind/water interface, and their dependence on both water depth and vorticity is derived and discussed. Vorticity is seen to shift the maximum wave age, similar to what was previously known to be the effect of water depth. At the same time, a novel effect arises and the growth coefficients, at identical wave age and depth, are shown to experience a net increase or decrease according to the shear gradient in the water flow.

Ref HAL: hal-03184622_v1
Ref Arxiv: 2102.02486
Ref. & Cit.: NASA ADS
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Résumé:

We extend the paradigmatic and versatile TASEP (Totally Asymmetric Simple Exclusion Process) for stochastic 1d transport to allow for two different particle species, each having specific entry and exit rates. We offer a complete mean-field analysis, including a phase diagram, by mapping this model onto an effective one-species TASEP. Stochastic simulations confirm the results, but indicate deviations when the particle species have very different exit rates. We illustrate that this is due to a phenomenon of intermittency, and formulate a refined 'intermittent' mean-field (iMF) theory for this regime. We discuss how non-stationary effects may further enrich the phenomenology.

Ref HAL: hal-03125706_v1
DOI: 10.1016/j.ecss.2021.107229
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Wetlands conservation and resilience capacities are key issues in many places over the globe. Understanding these issues will benefit from a precise knowledge of seagrass species occupancy and coverage over time and over space. Such information can be obtained from remote sensing images and their classification thanks to a vegetation index, to be used in a complementary manner to field work inventories. Sentinel-2 data, which are available with a frequent revisit time (<5 days) and a high spatial resolution (10m pixel size) can be used to map grassbeds at the surface or slightly below the surface of permanent lagoons, hence enabling the characterization of its seasonal dynamics, which was not possible with previous remote-sensing tools. We have proved the feasibility of such a method in the natural reserve of the Bagnas (Herault, France) where Stuckenia pectinata coverage can be tracked over a full year thanks to Sentinel-2 images and field work. Inter-annual dynamics (seasonal growth and senescence) can be mapped over time with 10m resolution and will be extended to pluriannual studies thanks to the long-term objective of the Sentinel-2 mission. This opens the way to a concerted management of natural reserves based on data analysis and field knowledge, a better understanding of seagrass coverage with fluctuating environmental conditions, and predictive mechanistic and/or stochastic models of future qualitative trends.