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“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.

“Computer Vision for High-Throughput Materials Synthesis: A Tutorial for Experimentalists,” has been accepted and published as an Advance Article in Digital Discovery (DOI: 10.1039/D5DD00384A). This work was carried out during Shi Xuan’s postdoctoral research, in collaboration with Prof Omar Farha’s group. We hope this tutorial will serve as a practical guide for materials scientists and chemists seeking to integrate computer vision techniques into their experimental workflows.