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Professor Marco Lattuada was born in Cittiglio (VA), Italy. He obtained his Master's degree in Chemical Engineering (laurea in Ingegneria Chimica) from Politecnico di Milano with full marks and honors in 1998. He then joined the group of Prof. Morbidelli at ETH Zurich for his PhD, which he completed in 2003, and was awarded with the ETH medal for outstanding doctoral thesis. He then carried out a two-year post-doctoral work at MIT in the group of Prof. Alan Hatton, where he worked on the preparation of magnetic Janus nanoparticles. In 2006 he joined the group of Prof. Morbidelli again, this time as a senior scientist. In 2011 he obtained a SNSF professorship, and he joined the Adolphe Merkle Institute, at the University of Fribourg, in January 2012, where he created the Nanoparticles Self-Assembly group. In August 2016 he became associate professor in the Department of Chemistry, at the University of Fribourg. Starting from August 2017, he is head of the chemistry department.
Prof. Marco Lattuada's research activity aims at designing and synthesizing nano- and micro-particles with complex patterns and interactions and at understanding their interactions using both advanced characterization methods and sophisticated modeling approaches. This resulting information is then combined knowledge to design complex materials through self-organization of the particles, which are used as building blocks. One line of research aims at synthesizing Janus and patchy particles, and to use them to create Pickering emulsions, to create nanomotors, or to simply investigate their self-assembly behavior according to their specific interactions. A second research line makes use of superparamagnetic nanoparticles to control the structure of composite materials using magnetic fields. In a third research diraction, stimuli responsive particles are used to create materials with the ability to control their structural coloration in response to external stimuli, so as to mimic the behavior of chameleons and squids. From the theoretical perspective, the use of particle simulations, through Monte-Carlo methods or Brownian Dynamics, is used to guide the experimental work and rationalize the results.