Hydrodynamic pairing of soft particles in a confined flow

The mechanism of hydrodynamics-induced pairing of soft particles, namely closed bilayer membranes (vesicles, a model system for red blood cells) and drops, is studied numerically with a special attention paid to the role of the confinement (the particles are within two rigid walls). This study unveils the complexity of the pairing mechanism due to hydrodynamic interactions. We find both for vesicles and for drops that two particles attract each other and form a stable pair at weak confinement if their initial separation is below a certain value. If the initial separation is beyond that distance, the particles repel each other and adopt a longer stable interdistance. This means that for the same confinement we have (at least) two stable branches. To which branch a pair of particles relaxes with time depends only on the initial configuration. An unstable branch is found between these two stable branches. At a critical confinement the stable branch corresponding to the shortest interdistance merges with the unstable branch in the form of a saddle-node bifurcation. At this critical confinement we have a finite jump from a solution corresponding to the continuation of the unbounded case to a solution which is induced by the presence of walls. The results are summarized in a phase diagram, which proves to be of a complex nature. The fact that both vesicles and drops have the same qualitative phase diagram points to the existence of a universal behavior, highlighting the fact that with regard to pairing the details of mechanical properties of the deformable particles are unimportant. This offers an interesting perspective for simple analytical modeling

Spectral Classification of a Set of Hyperspectral Images using the Convolutional Neural Network, in a Single Training

International audienceHyperspectral imagery has seen a great evolution in recent years. Consequently, several fields (medical, agriculture, geosciences) need to make the automatic classification of these hyperspectral images with a high rate and in an acceptable time. The state-of-the-art presents several classification algorithms based on the Convolutional Neural Network (CNN) and each algorithm is training on a part of an image and then performs the prediction on the rest. This article proposes a new Fast Spectral classification algorithm based on CNN, and which allows to build a composite image from multiple hyperspectral images, then trains the model only once on the composite image. After training, the model can predict each image separately. To test the validity of the proposed algorithm, two free hyperspectral images are taken, and the training time obtained by the proposed model on the composite image is better than the time obtained from the model of the state-of-the-art

An easy route to synthesis black phosphorus at low pressure and soft conditions

Black phosphorus a promising candidate for large application, due to his variety of structural and physical properties, can be prepared by a very low-coast reaction route with high purity and crystallinity. Black phosphorus is prepared from red phosphorus at 873K under reduced pressure using a simple and low cost catalytic system. The quality of crystal with lattice parameters a=3.31Å, b=10.48Å, c=4.37Å can be approved by a series of characterizations like scanning microscopy electron (SEM), energy dispersive spectrometry (EDX), Raman spectroscopy and powder X-rays. The new preparation method of black phosphorus represents an easy, effective and low cost approach to avoid complicated preparative setups, toxic catalysts, or “dirty” flux methods and is of general interest in elemental chemistry