Organic and mineral synthesis

Biomass conversion into chemical products and intermediates is increasingly being adopted to reduce the carbon footprint of the industry concerned. Among biomass-based resources, sugars are extremely attractive since they contain a lot of functional groups enabling their conversion to products of interest (alcohols, acids, etc.). For example, through fructose dehydration, it is possible to obtain 5-hydroxymethylfurfural (5-HMF), a molecule that can be used to produce polymers. Since the conversion of fructose to 5-HMF is catalyzed by Brønsted acids1, liquid acid catalysts in solution are currently used, but without the possibility of recycling this catalyst. The development of a solid acid catalyst is thus crucially important...

The dehydration of bio-based alcohols to form alkenes is a key reaction to obtain major chemical intermediates from biomass. It is efficiently catalyzed by zeolites presenting Brønsted acid sites and a crucial challenge is the control of its selectivity...

Although the climate crisis makes the reduction of CO2 emissions a matter of urgency, some industries will have difficulty in avoiding them, such as cement plants (where the core process is currently based on the calcination of CaCO3) or refineries, which are currently highly energy-intensive. Hence the huge potential interest in procedures that could capture the CO2 released directly from the plant (...) then recover it at a fraction of the energy cost...

Zeolites are microporous crystalline aluminosilicates that exist in the natural state and can also be synthesised for a wide range of applications, from the biomedical industry to the production of renewable energy. (...) This work was carried out entirely at IFPEN as part of a doctoral thesis and has resulted in a new stable zeolite...

Semantic segmentation conducted on microscopy images is a processing operation carried out to quantify a material’s porosity and its heterogeneity. It is aimed at classifying every pixel within the image (on the basis of degree of heterogeneity and porosity). However, for some materials (such as aluminas employed for catalysis), it is very difficult or even impossible using a traditional image processing approach, since porosity differences are characterized by small contrasts and complex textural variations. One way of overcoming this obstacle is to tackle semantic segmentation via deep learning, using a convolutional neural network.

Platinum nanoparticles supported on chlorinated γ-alumina are used in bifunctional heterogeneous catalysts, which are central to numerous industrial processes. An atomic-scale study...