Author: The Szekely Group

Congrats to the MS graduates from our group: Claudia, Shaden and Aron! 🎓

#commencement #kaust

The special edition of EuroMembrane2022 to celebrate the 40th EMS Anniversary was organized in Sorrento during 20-24 November 2022. It was a great event in a great location!

Congratulations to Gergo Ignacz for earning both the Best Paper Award, and a Travel Award! 🏆 Gergo talked about his research on big data (1) and machine learning (2) in OSN. Well done, Gergo! Rifan, Jiahui and Fadhilah presented their research on crosslinking (3), carbon molecular sieves (4) and fluorinated TFCs (5). Gyorgy gave a Keynote Talk on biomass-based membranes for OSN. Thank you for the invitation! 🙏

Gyorgy was elected as a new EMS Council Member for the period of 2023-2026. Very excited, and looking forward to serving the membrane community!

An ad hoc meeting with the participating OSN groups (Ivo Vankelecom, Andrew Livingston, Suzana Nunes, Jeong Kim, Murielle Rabiller-Baudry, Anthony Szymczyk, Pieter Vandezande) was held, and an important decision was made… watch this space!

Join us at the AIChE Annual Meeting at the Phoenix Convention Center, Phoenix, Arizona, during 13–18 November 2022. Gyorgy will talk about bridging the gap between fillers and polymers in mixed matrix membranes for OSN. It is a plesure to be invited for the honorary session organized for Suzana Nunes! Read our corresponing article, which was featured on the front cover of JMCA, here.

It was great experience to give a seminar at the Department of Chemistry, Colorado State University 🌄, and meet present and future collaborators, as well as students from various groups. 📣 📚 Many thanks to Profs Chen and Kim for hosting me! Read our first joint article here.
#science #collaboration #academia #kaust

Join us at the IOM3 Materials Informatics: Machine Learning in Advanced Sustainable Materials webinar series organized by the Institute of Materials, Minerals and Mining, London, UK, 18th of October 2022. The invited talk on ‘The silver bullet for sustainable materials development’ will cover our latest research on the exploitation of machine learning in developing sustainable materials such as MOFs (1) and COFs (2), and employing machine learning for the prediction of OSN membrane separation performance (3,4).