Sandrine Hoppe

Effect of Nucleating Fillers on the Structure and Properties of Polypropylene Blends

The effect of three different fillers on the structure and properties of polypropylene blends were investigated. The β nucleating agent can successfully induce β crystal in modified iPP blends and the turning point of the properties appears at the content of 0.5 wt%. For the properties of carbon nanotube modified iPP blends, there is no turning point because of its dominant α-crystalline morphology. The β NA supported carbon nanotube shows better induction ability than the single β NA or single carbon nanotube, there is also a turning point existing at the content of 0.5 wt%.

Kaolinite dispersion in cassava starch-based composite films: a photonic microscopy and X-ray tomography study

Abstract In the present study, a combined use of photonic microscopy, scanning electron microscopy and 3D X-ray tomography is carried out in order to analyze the dispersion and the distribution of raw and dimethyl sulfoxide (DMSO)-intercalated kaolinite used as filler in cassava starch-based films. It is shown that the association of these techniques allows a valuable analysis of clay dispersion in polymer-clay composite films. In the case of kaolinite-starch composite films on which this study is focused, it is obvious that previous intercalation of kaolinite with DMSO is an efficient way to improve dispersion and distribution of kaolinite in a starch polymer matrix.

A New Catalytic System for the Photodegradation of Endocrinal Disruptors: Synthesis and Efficiency Modeling and Optimization

Nowadays, the challenge of understanding relationships between catalysts properties and performance in the context of heterogeneous catalysis is a hot topic. Indeed, catalytic processes are generally affected by many different operational parameters that need to be modeled and optimized. The challenge can be addressed using artificial neural networks due to their flexibility to work without mathematical description of the process. The present work enters within the framework of the photodegradation of water contaminants using ZnO- based catalysts. ZnO is a non-toxic cheap material with an interesting photocatalytic potential. However, its application is reduced because of its poor efficiency, photocorrosion and difficulties for recovery. The objective of this work is to improve this efficiency, regarding particularly the photodegradation of an endocrinal disruptor: bisphenol-A (BPA), via the synthesis of a new catalytic system based on ZnO and the modeling of both the synthesis process and photocatalytic performance of this new catalytic system. Modeling and optimization will be carried out using artificial neural network tools coupled to an evolutionary algorithm. The connection between the two artificial neural network models will make it possible to identify the optimal synthesis parameters that lead to the maximum photocatalytic efficiency (within the studied domain), thus shedding light on the association of the system structure with its photocatalytic performance