13 research outputs found
CORRELATIONS BETWEEN RELATIVES WITH RESPECT TO DERMATOGLYPHIC PATTERNS ON SPECIFIC FINGERTIPS .2. SIB-SIB CORRELATIONS
CORRELATIONS BETWEEN RELATIVES WITH RESPECT TO DERMATOGLYPHIC PATTERNS ON SPECIFIC FINGERTIPS .1. PARENT-CHILD CORRELATIONS
Voltammetric characteristics of miconazole and its cathodic stripping voltammetric determination
Effects of microbial floras on the distributions of five domestic Drosophila species across fruit resources
The challenges for molecular nutrition research 2: quantification of the nutritional phenotype
Nonlinear mechanics of colloidal gels: creep, fatigue, and shear-induced yielding
International audienceColloidal gels are formed through the aggregation of attractive particles, whose size ranges from 10 nm to a few micrometers, suspended in a liquid. Such gels are ubiquitous in everyday life applications, from food products to paints or construction materials, in particular thanks to their ability to easily “yield”, i.e., to turn from a solid to a liquid under the application of a weak external load. Understanding and controlling the mechanical response of colloidal gels is therefore of prime importance. Depending on the details of the system, however, the resulting gel networks present different microstructural organizations that may lead to widely different mechanical responses. This raises important challenges in fully characterizing yielding and in uncovering the mechanisms of nonlinear response in colloidal gels. In this paper, we distinguish between two classes of colloidal gels showing respectively reversible yielding, where the gel network reforms upon load release, and irreversible yielding, where the network is fully destroyed through fractures and phase separation. This broad, empirical distinction is achieved through rheology and local experiments at a mesoscopic scale, intermediate between the network characteristic size and the sample size. We further discuss how the observables derived from creep and fatigue experiments may be modeled to predict yielding and highlight open questions and future research directions in the domain