报告题目：Recent Advances in the use of Pickering Emulsions/Bubbles in Foods and Related Materials

主  讲 人 ：Rammile Ettelaie  英国利兹大学  副教授

报告时间：2018年5月15日（周二）上午10:00

报告地点：下沙校区食品学院257学术报告厅

报告人简介

        Rammile Ettelaie于1987年在英国曼彻斯特的理论凝聚态物理研究所（Theoretical Condensed Matter Physics）获得博士学位，现任职于英国利兹大学食品物理化学方向副教授；1987-1989年作为博士后研究科学家在帝国学院（Imperial College）中央电力研究实验室从事博士后研究；1989-1993年和1993-2001年分别在ICI公司胶体科学集团、威尔顿中心任高级研究科学家。2001年起在利兹大学任教，历任食品物理化学的讲师、食品化学物理高级讲师。2013年至今为食品化学物理副教授。Rammile Ettelaie教授曾是液体和复杂流体物理研究所（IOP）指导委员会成员（2001-2010）和英国皇家化学会胶体与界面科学组委员（1999-2006）。

报告摘要

       Emulsions and foams, stabilised by nano-particles, continue to be the subject of a great deal of interest in foods and related industries, mainly due to their exceptional stability.  However, the droplets can be destabilised when subjected to sufficient external forces on the particles to cause their displacement from the surface.  This can set limits on the stability of bubbles/emulsions when subjected to shear in many applications.  We present a theoretical analysis of the problem of displacing a particle from an oil-water interface.  We show that the work of displacement is several times greater than the adsorption energy of the particle, often mistakenly quoted in the literature as the amount energy needed to remove the particle from the surface.  The discrepancy is due to substantial degree of energy dissipation in this process. Next, we give examples of our theoretical contributions during various collaborations with several different experimental groups.  In each case we discuss how the understanding and insight provided by our theoretical input has opened up new and interesting ways of approaching these problems.  The examples given will include the use of nanoparticles to produce long lasting (~ month) microbubbles and use of Pickering emulsions to retard digestion of lipids.  Depending on time availability, we also will discuss the use of Pickering based emulsions in continuous flow enzyme catalyst systems, with these providing efficiencies that are at least an order of magnitude better than that available presently by other means.

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