New preprint alert!

“Bayesian Optimized Electrosynthesis of Azobenzenes in a Self- Optimizing Flow Set-up: from DoE-guidance to Gram-Scale Preparation,” is now available as a preprint on ChemRxiv (DOI: 10.26434/chemrxiv-2026-8rd5c), co-led by Alexander Lohmann and Taline Kerackian from Prof Siegfried Waldvogel’s group. This collaboration took place during Shi Xuan’s postdoctoral research, and demonstrates a self-optimizing platform for azobenzene electrosynthesis by combining Design of Experiments (DoE) and Bayesian Optimization to efficiently explore and optimize reaction conditions.