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. Based on an element quality metric, after conducting tests on contours for a various number of edges, the results show a maximum average deviation of 15.2% on the mean quality and 27.3% on the minimum quality between the elements of the meshes generated by the scheme and the ones generated from the reference mesher; the scheme is able to produce good quality meshes that are suitable for meshing purposes. The meshing scheme is also applied to generate larger scale meshes with a recursive implementation. The findings encourage the adaption of the scheme for 3-D mesh generation.

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. Depending on how the devices were operated, double emulsions containing either single or multiple water droplets could be produced. Pulsed-field gradient self-diffusion NMR experiments have been performed on the monodisperse water-in-oil-in-water double emulsions to obtain information on the inner water droplet diameter and the distribution of the water in the different phases of the double emulsion. This has been achieved by applying regularization methods to the self-diffusion data. Using these methods the stability of the double emulsions to osmotic pressure imbalance has been followed by observing the change in the size of the inner water droplets over time.

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. Bragg-like reflections due to the restricted diffusion of the water around the oil droplets are observed due to the low polydispersity of the emulsions and the dense packing. The resulting data are fitted to a pore glass model to give the diameter of both the pools of interstitial water and the oil droplets. In the gel, information on the residual three-dimensional structure is obtained using the short time behavior of the effective diffusion coefficient to give the surface to volume ratio of the residual protein network structure. The values for the surface to volume ratio are found to be consistent with the expected increase of the surface area of monodisperse droplets forming a gel network. At long diffusion observation times, the permeability of the network structure is investigated by diffusion NMR to give a complete picture of the colloidal system considered.