Chemical Society Seminar: Lluis Blancafort -Computational studies of excited states - from conical intersections to smart biomaterials

Tuesday, April 16, 2024 13:00to14:30
Maass Chemistry Building OM 10, 801 rue Sherbrooke Ouest, Montreal, QC, H3A 0B8, CA


This presentation will illustrate some of our recent computational studies in photophysics and photochemistry, focusing on two topics: (1) molecules showing aggregation induced emission (AIE), where we will introduce a restricted access to a conical intersection (RACI) model to explain photophysics in solution and discuss the importance of through-space interactions in modulating the emission; (2) melanin and its components, where we follow two threads: the study of some key components in collaboration with the groups of J. P. Lumb (McGill) and B. Kohler (Ohio State), and the generation of theoretical models with a library-based approach and a kinetic melanization model.



Lluís Blancafort is a Professor of Chemistry at the University of Girona in Spain since 2007. He graduated in Chemical Engineering in 1991 at the Institut Químic de Sarrià (Barcelona) and took his PhD in 1996at the University of Würzburg (Germany), under the supervision of Waldemar Adam, working on the chemistry of alpha-peroxylactones. Between 1997 and 2002 he was a post-doctoral researcher at the group of Michael Robb at King's College London (UK) working on excited state computational chemistry. In 2002 he joined the University of Girona as a Ramón y Cajal fellow and became professor in 2007. He is a computational chemist whose main interest is the photophysics and photochemistry of systems of biological and technological relevance, focusing on the mechanistic role of conical intersections. The group's interests span both the molecular level and larger systems including photocatalysis on surfaces, aggregation induced emission and melanin as a biomaterial. The group's expertise covers different areas of computational chemistry including quantum chemical methods, ab initio and molecular mechanics based dynamics, and machine learning.

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