Parallelised production of fine and calibrated emulsions by coupling flow-focusing technique and partial wetting phenomenon

The capacity of microfluidic technology to fabricate monodisperse emulsion droplets is well established. Parallelisation of droplet production is a prerequisite for using such an approach for making high-quality materials for either fundamental or industrial applications where product quantity matters. Here, we investigate the emulsification efficiency of parallelised drop generators based on a flow-focusing geometry when incorporating the role of partial wetting in order to make emulsion droplets with a diameter below 10 μm. Confinement intrinsically encountered in microsystems intensifies the role played by interfaces between liquids and solids. We thus take advantage of partial wetting to enhance the maximum confinement accessible due to liquid flow focusing. We compare the performances brought by partial wetting to more established routes such as step emulsification. We show that the step configuration and the partial wetting regime are both well suited for being parallelised and thus open the way to the production of fine and calibrated emulsions for further applications. Finally, this new route of emulsification that exploits partial wetting between the fluids and the channel walls opens possibilities to the formation of substantially smaller droplets, as required in many fields of application.     

Preparation of optically active poly β-propiolactones

Racemic -methyl--n-propyl--propiolactone is readily polymerized with zinc and cadmium chiral glycolates leading to optically active polyesters. The stereoelectivities observed are lower than for oxiranes and thiiranes with the same initiator system. A value of rotatory power for pure enantiomer _o, is deduced from NMR measurements in the presence of chiral solvent. The structure of polymers is discussed on the basis of NMR results.     

3He impurities on the bulk surface of liquid4He: Possible existence of excited states

We study, using a density-functional approach, the properties of the two-dimensional system formed by³He atoms on the surface of liquid⁴He, as a function of³He coverageN _s , at zero temperature. We find several types of surface states accessible to the³He atoms. For small values ofN _s , the surface tension is, as expected, linear inN _s ² . For a coverage of about half a monolayer, a new type of surface state starts being occupied, and this produces a change in the slope of as a function ofN _s ² and, more clearly, a step in the surface specific heat, which increases by a factor of almost two. Another step is predicted to occur for a coverage of 1.3 monolayer. Existing data are compatible with this structure of surface states but are not numerous enough to prove or disprove the existence of steps.Unité de Recherche des Universités Paris 11 et Paris 6 Associée au CNRS.     

Dynamic Model Adaptation for Multiscale Simulation of Hyperbolic Systems with Relaxation

In numerous industrial CFD applications, it is usual to use two (or more) different codes to solve a physical phenomenon: where the flow is a priori assumed to have a simple behavior, a code based on a coarse model is applied, while a code based on a fine model is used elsewhere. This leads to a complex coupling problem with fixed interfaces. The aim of the present work is to provide a numerical indicator to optimize to position of these coupling interfaces. In other words, thanks to this numerical indicator, one could verify if the use of the coarser model and of the resulting coupling does not introduce spurious effects. In order to validate this indicator, we use it in a dynamical multiscale method with moving coupling interfaces. The principle of this method is to use as much as possible a coarse model instead of the fine model in the computational domain, in order to obtain an accuracy which is comparable with the one provided by the fine model. We focus here on general hyperbolic systems with stiff relaxation source terms together with the corresponding hyperbolic equilibrium systems. Using a numerical Chapman–Enskog expansion and the distance to the equilibrium manifold, we construct the numerical indicator. Based on several works on the coupling of different hyperbolic models, an original numerical method of dynamic model adaptation is proposed. We prove that this multiscale method preserves invariant domains and that the entropy of the numerical solution decreases with respect to time. The reliability of the adaptation procedure is assessed on various 1D and 2D test cases coming from two-phase flow modeling.     

Binarised regression tasks: methods and evaluation metrics

Some supervised tasks are presented with a numerical output but decisions have to be made in a discrete, binarised, way, according to a particular cutoff. This binarised regression task is a very common situation that requires its own analysis, different from regression and classification—and ordinal regression. We first investigate the application cases in terms of the information about the distribution and range of the cutoffs and distinguish six possible scenarios, some of which are more common than others. Next, we study two basic approaches: the retraining approach, which discretises the training set whenever the cutoff is available and learns a new classifier from it, and the reframing approach, which learns a regression model and sets the cutoff when this is available during deployment. In order to assess the binarised regression task, we introduce context plots featuring error against cutoff. Two special cases are of interest, the \( UCE \) and \( OCE \) curves, showing that the area under the former is the mean absolute error and the latter is a new metric that is in between a ranking measure and a residual-based measure. A comprehensive evaluation of the retraining and reframing approaches is performed using a repository of binarised regression problems created on purpose, concluding that no method is clearly better than the other, except when the size of the training data is small.     

On matrices, automata, and double counting in constraint programming

Matrix models are ubiquitous for constraint problems. Many such problems have a matrix of variables $\mathcal{M}$ , with the same constraint C defined by a finite-state automaton $\mathcal{A}$ on each row of $\mathcal{M}$ and a global cardinality constraint $\mathit{gcc}$ on each column of $\mathcal{M}$ . We give two methods for deriving, by double counting, necessary conditions on the cardinality variables of the $\mathit{gcc}$ constraints from the automaton $\mathcal{A}$ . The first method yields linear necessary conditions and simple arithmetic constraints. The second method introduces the cardinality automaton, which abstracts the overall behaviour of all the row automata and can be encoded by a set of linear constraints. We also provide a domain consistency filtering algorithm for the conjunction of lexicographic ordering constraints between adjacent rows of $\mathcal{M}$ and (possibly different) automaton constraints on the rows. We evaluate the impact of our methods in terms of runtime and search effort on a large set of nurse rostering problem instances.