Influence of boundary conditions on yielding in a soft glassy material

The yielding behavior of a sheared Laponite suspension is investigated within a 1 mm gap under two different boundary conditions. No-slip conditions, ensured by using rough walls, lead to shear localization as already reported in various soft glassy materials. When apparent wall slip is allowed using a smooth geometry, the sample is shown to break up into macroscopic solid pieces that get slowly eroded by the surrounding fluidized material up to the point where the whole sample is fluid. Such a drastic effect of boundary conditions on yielding suggests the existence of some macroscopic characteristic length that could be connected to cooperativity effects in jammed materials under shear.Comment: 4 pages, 5 figure

Shear-induced fragmentation of Laponite suspensions

Simultaneous rheological and velocity profile measurements are performed in a smooth Couette geometry on Laponite suspensions seeded with glass microspheres and undergoing the shear-induced solid-to-fluid (or yielding) transition. Under these slippery boundary conditions, a rich temporal behaviour is uncovered, in which shear localization is observed at short times, that rapidly gives way to a highly heterogeneous flow characterized by intermittent switching from plug-like flow to linear velocity profiles. Such a temporal behaviour is linked to the fragmentation of the initially solid sample into blocks separated by fluidized regions. These solid pieces get progressively eroded over time scales ranging from a few minutes to several hours depending on the applied shear rate $\dot{\gamma}$. The steady-state is characterized by a homogeneous flow with almost negligible wall slip. The characteristic time scale for erosion is shown to diverge below some critical shear rate $\dot{\gamma}^\star$ and to scale as $(\dot{\gamma}-\dot{\gamma}^\star)^{-n}$ with $n\simeq 2$ above $\dot{\gamma}^\star$. A tentative model for erosion is discussed together with open questions raised by the present results.Comment: 19 pages, 13 figures, submitted to Soft Matte

Stress overshoot in a simple yield stress fluid: an extensive study combining rheology and velocimetry

We report a large amount of experimental data on the stress overshoot phenomenon which takes place during start-up shear flows in a simple yield stress fluid, namely a carbopol microgel. A combination of classical rheological measurements and ultrasonic velocimetry makes it possible to get physical insights on the transient dynamics of both the stress $\sigma(t)$ and the velocity field across the gap of a rough cylindrical Couette cell during the start-up of shear under an applied shear rate $\dot\gamma$. (i) At small strains ($\gamma <1$), $\sigma(t)$ increases linearly and the microgel undergoes homogeneous deformation. (ii) At a time $t_m$, the stress reaches a maximum value $\sigma_m$ which corresponds to the failure of the microgel and to the nucleation of a thin lubrication layer at the moving wall. (iii) The microgel then experiences a strong elastic recoil and enters a regime of total wall slip while the stress slowly decreases. (iv) Total wall slip gives way to a transient shear-banding phenomenon, which occurs on timescales much longer than that of the stress overshoot and has been described elsewhere [Divoux \textit{et al., Phys. Rev. Lett.}, 2010, \textbf{104}, 208301]. This whole sequence is very robust to concentration changes in the explored range ($0.5 \le C \le 3$ w/w). We further demonstrate that the maximum stress $\sigma_m$ and the corresponding strain $\gamma_m=\dot\gamma t_m$ both depend on the applied shear rate $\dot \gamma$ and on the waiting time $t_w$ between preshear and shear start-up: they remain roughly constant as long as $\dot\gamma$ is smaller than some critical shear rate $\dot\gamma_w\sim 1/t_w$ and they increase as weak power laws of $\dot \gamma$ for $\dot\gamma> \dot\gamma_w$ [...].Comment: 18 pages, 14 figures, accepted for publication in Soft Matte

Extension de la régression linéaire généralisée sur composantes supervisées à la modélisation jointe des réponses

National audienceDans ce travail, nous proposons d'étendre la méthode SCGLR, pour la rendre capable d'identifier des groupes de réponses expliquées par des composantes communes. À l'origine, SCGLR vise la construction de composantes explicatives dans un grand nombre de covariables, éventuellement fortement redondantes. Ces composantes sont supervisées conjointement par l'ensemble des réponses. Désormais, nous cherchons à identifier des groupes de réponses partageant les mêmes dimensions explicatives. Dans un cadre écologique par exemple, des communautés d'espèces devraient pouvoir être modélisées par des composantes propres à chaque communauté. Un algorithme est proposé afin d'estimer le modèle

Direct synthesis of mesoporous hybrid organic-inorganic silica powders and thin films for potential non linear optic applications

Powders of mesoporous organosilica functionalized with a Non Linear Optical chromophore in the channel pore (azobenzene diethylphosphonate) were achieved in one step and were synthesized by templated-directed co-condensation of tetraethylorthosilicate (TEOS) and the functional organotriethoxysilanes. The materials were characterized by 13C, 31P and 29Si NMR experiments, nitrogen gas adsorption, powder X-ray diffractrion (XRD). Optically transparent and highly ordered multifunctional mesostructured films obtained by evaporation induced self-assembly (EISA) approach were deposited on glass or silicon substrates by dip-coating. Thin films were monofunctionalized in the channel pores or bifunctionalized (channel pore/framework) and allow us to evidence the salt effect induced by an organometallic complex on the structure of the mesostructured film. They were characterized by Grazing Incidence Small Angle X-ray Scattering (GISAXS) and X-ray reflectivit

Response mixture models based on supervised components: Clustering floristic taxa

International audienceIn this paper, we propose to cluster responses in order to identify groups predicted by specific explanatory components. A response matrix is assumed to depend on a set of explanatory variables, and a set of additional covariates. Explanatory variables are supposed many and redundant, which implies some dimension reduction and regularization. By contrast, additional covariates contain few selected variables which are forced into the regression model, as they demand no regularization. The response matrix is assumed partitioned into several unknown groups of responses. We suppose that the responses in each group are predictable from an appropriate number of specific orthogonal supervised components of explanatory variables. The classification is based on a mixture model of the responses. To estimate the model, we propose a criterion extending that of Supervised Component-based Generalized Linear Regression, a Partial Least Squares-type method, and develop an algorithm combining component-based model and Expectation Maximization estimation. This new methodology is tested on simulated data and then applied to a floristic ecology dataset

Response mixture models based on supervised components: clustering floristic taxa

In this paper, we propose to cluster outcomes in order to identify groups predicted by specific explanatory components. A response matrix Y is assumed to depend on a set X of explanatory variables, and a set A of additional covariates. Variables in X are supposed many and redundant, which implies some dimension reduction and regularization. By contrast, A contains few selected variables which are forced into the regression model, as they demand no regularization. The matrix Y is assumed partitioned into G unknown groups of responses. We suppose that the outcomes in each group are predictable from an appropriate number of specific orthogonal supervised components of X. The classification is based on a mixture model of the responses. To estimate the model, we propose a criterion extending that of Supervised Component-based Generalized Linear Regression (SCGLR), a PLS-type method, and develop an algorithm combining those of SCGLR and EM estimation: response mixture SCGLR (rmSCGLR). This new methodology is tested on simulated data and then applied to a floristic ecology dataset