Phosphorylation of Mono- or Di-octadienyl-D-xylosides. Influence towards Surfactant Properties

International audienc

Destabilisation of Water-in-Crude Oil Emulsions by Silicone Copolymer Demulsifiers

Asphaltene aggregates are known to form viscoelastic film preventing the coalescence of droplets in water-in-oil emulsions formed during crude oil exploitation. Since phase separation is necessary for oil refining process, demulsifying additives are used. It was found that formulations based on polysiloxane copolymers promote separation of water from crude oil even at very low concentration (few tens of ppm). Two alternative scenarios of emulsion destabilisation can be envisaged: (i) dissolution of asphaltene aggregates or (ii) displacement of the asphaltene network by adsorption of the more surface active copolymer into void sites at the oil/water interface. In order to reveal the mechanism of destabilisation, interactions between asphaltene aggregates and copolymer were explored. For that purpose various techniques have been employed: small angle X-ray scattering allowing the determination of the influence of copolymer on the size of asphaltene aggregate; capacity of copolymer to displace asphaltene aggregates initially adsorbed on silica particles (which simulate water droplets); Atomic Force Microscopy (AFM) was used to observe the influence of copolymer on the interfacial structure of asphaltene films spread on water surface

Semiparametrically efficient inference based on signs and ranks for median-restricted models

Since the pioneering work of Koenker and Bassett, median-restricted models have attracted considerable interest. Attention in these models, so far, has focused on least absolute deviation (auto-)regression quantile estimation and the corresponding sign tests. These methods use a pseudolikelihood that is based on a double-exponential reference density and enjoy quite attractive properties of root "n" consistency (for estimators) and distribution freeness (for tests). The paper extends these results to general, i.e. not necessarily double-exponential, reference densities. Using residual signs and ranks (not "signed ranks") and a general reference density "f", we construct estimators that remain root "n" consistent, irrespective of the true underlying density "g" (i.e. also for "g"  /="f"). However, instead of reaching semiparametric efficiency bounds under double-exponential "g", they reach these bounds when "g" coincides with the chosen reference density "f". Moreover, we show that choosing reference densities other than the double-exponential in applications can lead to sizable gains in efficiency. The particular case of median regression is treated in detail; extensions to general quantile regression, heteroscedastic errors and time series models are briefly described. The performance of the method is also assessed by simulation and illustrated on financial data. Copyright (c) 2008 The Authors.