Integrated Calendar

A crucial aspect in our ability to adapt to environments is our learning of their characteristic  stimuli summary statistics. But what should be the dynamics of this learning to obtain optimal predictions?I will present a series of behavioral, computational and imaging studies which show that people with dyslexia and people with autism manifest opposite atypicalities in their learning dynamics.In Dyslexia learning is fast but perceptual memories decay quickly, disrupting their acquisition of fixed language statistics (e.g. structure and frequency of syllables). By contrast, in autism, learning lasts long, but is difficult to modify, hampering flexibility, which is needed in dynamic social and non-social environments.

MXenes are the fastest-growing family of two-dimensional (2D) materials. Unlike most other 2D materials, they lack bulk analogues when restacked because of their unique structure and surface terminations. They represent a new class of 2D transition-metal carbides/nitrides, not merely exfoliated van der Waals solids. They have a general formula Mn+1XnTx, where M is a transition metal, X is carbon and/or nitrogen, T represents surface terminations (O, OH, halogen, chalcogen, etc.), and n = 2—5. About 50 stoichiometric MXene compositions and dozens of solid solutions on M and X sites have already been reported. Given the infinite number of possible solid-solution compositions and combinations of surface terminations, MXenes offer an opportunity for computationally driven atomistic design of inorganic 2D structures with unique properties. MXenes exhibit electronic, optical, mechanical, and electrochemical properties that clearly distinguish them from other materials. Moreover, these properties are tunable by design and can be modulated using an ionotronic approach, leading to breakthroughs in fields ranging from optoelectronics and communication to electrochemical energy storage, catalysis, sensing, and medicine. In this talk, I’ll discuss methods for MXene synthesis and processing, the effects of MXene chemistry on their properties, and provide examples of important applications where MXenes outperform other materials. 

Most efficient chemical processes used in industry rely on heterogeneous catalysis. While the search for more sustainable processes and the changes in environmental policies impose the continuous development of more efficient catalysts, we have currently little understanding of the structure of the actives in these processes. Hence, due to their inherent complexity, heterogeneous catalysts have been mostly developed empirically.Here, we will show how constructing active sites, one atom at a time on surfaces, enables molecular-level understanding and implementation of rational approaches for the improvement of catalytic processes. We will first illustrate how this approach enables to generate selective single-site catalysts. We will next show how from these isolated (single) sites, one can generate and understand far more complex systems such as supported nanoparticles, where interfaces, alloying… play a critical role. This lecture will be developed around these themes and will show how the development of advanced characterization tools augmented by computational approaches can provide useful information to bridge the gap between fundamental and applied (industrial) catalysis.