Analysis and characterization

The photocatalytic reduction of CO2 to “C1/C2 solar fuels” is an attractive conversion reaction since it has the advantage of eliminating this greenhouse gas while providing a usable product. However, despite extensive research efforts, the level of CO2 conversion remains very low, even for the most active catalysts on the market, and the reaction mechanisms involved is still poorly understood. In order to remedy this, this PhD research focused on the study of a model Pt/TiO2-type photocatalytic system. For the purpose of the study, an operando methodology was deployed combining several techniques...

Heterogeneous catalysts are materials that make chemically possible and economically viable countless industrial processes in the service of energy-related and environmental problems, such as fuel production and air quality (vehicle emissions, building interiors). Their implementation involves two major scientific challenges: (1) the identification of the chemical, structural and morphological properties of an optimal catalyst , (2) the evolution of these properties over the catalyst’s life-cycle (synthesis, preparation, use, recycling)...

The increase in the level of atmoshpheric CO2 and the resulting climate change are a global concern. Despite this, the use of fossil fuels continues to grow, in response to high energy demand. Combined with storage and CO2 conversion solutions, Chemical Looping Combustion processes (CLC) offer a medium-term solution to reducing the impact of energy production from fossil fuels, or even biomass...

Faced with the current climate challenges, alternative fuels are attracting a growing interest for the mobility of the future. Of the various possible alternatives, hydrocarbons could be synthesised via a well-known process: the Fischer-Tropsch (FT) process, based on Syngas (CO and H2) produced, in particular, by biomass gasification. (...) However, the deactivation of FT catalysts is a major issue that directly impacts the costs of the process. (...) To identify these mechanisms, a multiple-stage methodology was implemented as part of a doctoral thesis...

Salt precipitation in permeable rocks is a risk faced by some energy sectors, particularly for gas storage in geological formations during operational phases (injection and extraction), when there is contact with saline aquifers. (...) This precipitation reduces the space where fluids can circulate, altering rock permeability, or even leading to plugging under certain conditions. In order to understand the underlying mechanisms behind this damaging phenomenon, experiments examining gas flow in a brine-saturated porous medium were conducted on IFPEN’s CAL-X flow test bench...

Today, air quality is an issue of major concern when it comes to public health. To protect it, it is necessary to reduce emissions but also monitor global chemical changes taking place in the atmosphere. From the economic and safety points of view, it is also important to monitor industrial gas emissions. It was to tackle these different aspects that IFPEN’s researchers began developing a range of technological solutions, within the context of the Flair Suite™ project.

One way to decrease the amount of atmospheric CO2 is to reduce greenhouse gas emissions of fossil origin, but it is also possible to increase the quantities of carbon stored in the soil. (...) In the current context of climate change, understanding the evolution of carbon in the ground is critically important. It is for this reason that IFPEN wanted to study the potential offered by Rock-Eval®, a flagship of oil research...

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.