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Ana Teresa FIALHO BATISTA

Research Engineer in heterogeneous catalysis | PhD in Chemistry
After a Master’s Degree in Chemical Engineering (Instituto Superior Técnico, Lisbon) I pursued a PhD in heterogenous catalysis developing a multi-technique approach to the characterization and
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Malika BOUALLEG

Project manager, Research Engineer in Heterogeneous Catalysis
Malika Boualleg joined IFP New Energy after a thesis in synthesis of materials and heterogeneous catalysis (CP2M, ex-LCOMS 2006-2009), during which she developed new syntheses of mesostructured
Experimentation and modelling combined to study the catalytic conversion of biomass-derived sugars
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Experimentation and modelling combined to study the catalytic conversion of biomass-derived sugars

Driven by the global challenge of switching to a more sustainable economic and energy model, IFPEN has been studying for a number of years biosourced products with high added value and working to develop processes for biomass recovery, as an alternative to conventional petrochemistry.
Photocatalysis, a lighted pathway for CO2 conversion
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Photocatalysis, a lighted pathway for CO2 conversion

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...
Loss of selectivity in Fischer-Tropsch synthesis: a high-throughput study
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Loss of selectivity in Fischer-Tropsch synthesis: a high-throughput study

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...
Bifunctional catalysis deployed for the production of biofuels
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Bifunctional catalysis deployed for the production of biofuels

The production of biofuels, renewable diesel or sustainable aviation fuel can be achieved through lipid feedstocks conversion, such as vegetable oils, used cooking oils or animal fats. Through a hydrotreatment stage, we obtain long-chain normal paraffinsa, which must then be isomerised or cracked in order to adjust the properties of the effluent, resulting in the specifications required according to the targeted fuel type (particularly cold flow properties and/or final distillation temperature)...
New acid zeolites obtained from silicogermanates
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New acid zeolites obtained from silicogermanates

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...
Molecular modelling: a key tool for current and future heterogeneous catalysis
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Molecular modelling: a key tool for current and future heterogeneous catalysis

Chemical theory at quantum level (density functional theory or DFT) is an essential tool in rationalising the reaction mechanisms involved in the preparation of catalysts, as well as in their use, thanks to the optimisation of their activity [1,2]. IFPEN has carried out a number of projects aiming to shed light on these catalysts, which are of particular interest to industrial processes...
Issue 45 of Science@ifpen
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Semantic segmentation through deep learning in materials sciences

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.
Issue 45 of Science@ifpen
News in brief

Acceleration of chemical kinetics calculations through Machine Learning methods

Numerical simulations are now widely employed in the industrial world to help design systems and predict complex phenomena. Reactive flow simulation, for example, is important for numerous applications, such as vehicle and aircraft propulsion and processes in the chemicals industry.