How to teach neural networks to mesh: Application on 2-D simplicial contours.

A machine learning meshing scheme for the generation of 2-D simplicial meshes is proposed based on the predictions of neural networks. The data extracted from meshed contours are utilized to train neural networks which are used to approximate the number of vertices to be inserted inside the contour cavity, their location, and connectivity. The accuracy of the scheme is evaluated by comparing the quality of the mesh generated by the neural networks with that generated by a reference mesher.

Microfluidic preparation and self diffusion PFG-NMR analysis of monodisperse water-in-oil-in-water double emulsions.

Monodisperse water-in-oil-in-water (WOW) double emulsions have been prepared using microfluidic glass devices designed and built primarily from off the shelf components. The systems were easy to assemble and use. They were capable of producing double emulsions with an outer droplet size from 100 to 40 μm.

Structure, diffusion, and permeability of protein-stabilized monodispersed oil in water emulsions and their gels: a self-diffusion NMR study.

Self-diffusion NMR is used to investigate monodispersed oil in water emulsions and the subsequent gel formed by removing the water through evaporation. The radius of the oil droplets in the emulsions is measured using a number of diffusion methods based on the measurement of the mean squared displacement of the oil, water, and tracer molecules. The results are consistent with the known size of the emulsions.