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Burés Amat, Jordi
ICREA
Experimental Sciences & Mathematics
Short biography
Jordi obtained his undergraduate degree in Chemistry and his PhD at the University of Barcelona under the supervision of Prof. Jaume Vilarrasa, focusing on the development of synthetic methodologies. In 2010, he was awarded a Spanish postdoctoral fellowship to join the group of Prof. Donna Blackmond at The Scripps Research Institute in California, where he worked on mechanistic studies of aminocatalytic reactions. In 2013, he began his independent research career as a Junior Research Fellow at Imperial College London. In 2016, he was appointed Lecturer at The University of Manchester, progressing to Full Professor of Organic Chemistry in 2023. In 2024, he joined the Institute of Chemical Research of Catalonia (ICIQ) as an ICREA Research Professor.
In 2018, Jordi was honored with the Thieme Chemistry Journals Award. In 2019, he received the Young Researcher Award from the Spanish Royal Society of Chemistry, and in 2020 the Hickinbottom Award from the Royal Society of Chemistry.
Research interests
Our objective is to acquire chemical knowledge that provides a competitive advantage in innovating chemical processes, focusing on mechanistic studies, chemical kinetics, organic chemistry, and catalysis.
We advance mechanistic analysis by developing methods such as Variable Time Normalization Analysis (VTNA) to extract valuable information from reaction profiles and employ machine learning to enhance kinetic analyses.
We conduct experimental mechanistic studies of complex catalytic reactions involving various metals and organocatalysts, impacting industries such as pharmaceuticals, polymers, fragrances, and flavors. Our insights lead to innovative synthetic solutions that offer improved reproducibility, efficiency, and sustainability.
We also collaborate with engineers and spectroscopists to develop advanced data-collection methods and devices that gather high-quality kinetic data, even under challenging conditions, thereby improving reaction profile analysis.