International audienceMechanical characteristics (i.e., stiffness, internal friction angle, peak strength) and crushability of a soft granular material were evaluated by performing a comprehensive series of laboratory tests using the following devices: standard and non-standard triaxial apparatus, direct and annular shear box, oedometer and hydrostatic devices. The initial tested specimens differ by initial void ratio, grading characteristics and particle hardness. The air-dried specimen of soft particles were then subjected to monotonic loadings for various stress paths (direct and annular shear stress paths, oedometer stress paths until different upper normal pressures, triaxial stress paths including different confining pressures). After each homogeneous test, sieving has been performed in order to characterize the evolution of grading characteristics of the granular packing. Experimental results on mechanical properties show that maximum internal friction angle is rather independent of the particle stiffness even though small differences may exist before peak stress-state. As highlighted by recent studies (Arslan in Granul Matter 11(2): 87-97, 2009), the volumetric response of the specimen indicates that classical critical state is no more a relevant framework when particle crushability is too high compared with the applied stress-state. Crushability related to loading paths has been evaluated through the relative breakage ratio (Br). The first results pointed out the effects of initial geometrical configuration (i.e., void ratio, grading) and particle stiffness. Analysis of the stress paths effects on the amount of breakage revealed that stress-state is not sufficient to describe properly breakage undergone by the material which is confirmed by an obvious link between volumetric strain and total breakage. Finally, the present study showed that the percentage of fine particles content during breakage may be seen as a function of the "level" of deviatoric loading paths
High-throughput data analysis challenges in laboratory automation and lab-on-a-chip devices’ applications are continuously increasing. In cell culture monitoring, specifically, the electrical cell-substrate impedance sensing technique (ECIS), has been extensively used for a wide variety of applications. One of the main drawbacks of ECIS is the need for implementing complex electrical models to decode the electrical performance of the full system composed by the electrodes, medium, and cells. In this work we present a new approach for the analysis of data and the prediction of a specific biological parameter, the fill-factor of a cell culture, based on a polynomial regression, data-analytic model. The method was successfully applied to a specific ECIS circuit and two different cell cultures, N2A (a mouse neuroblastoma cell line) and myoblasts. The data-analytic modeling approach can be used in the decoding of electrical impedance measurements of different cell lines, provided a representative volume of data from the cell culture growth is available, sorting out the difficulties traditionally found in the implementation of electrical models. This can be of particular importance for the design of control algorithms for cell cultures in tissue engineering protocols, and labs-on-a-chip and wearable devices applications.
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