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TEMPO has been widely explored as one of the most promising catholyte redox scaffolds in aqueous redox flow batteries, but the often-observed performance degradation raises concern with respect to its chemical instability. In this work, we demonstrate that the charged TEMPO species (i.e., TEMPO+) lack sufficient stability and also determine the major decomposition pathways. The decay products of TEMPO+ are experimentally analyzed using combined tools including nuclear magnetic resonance and mass spectroscopy. Reductive conversion to 2,2,6,6-tetramethylpiperidine (TEMPH) is commonly observed for a variety of 4-O-substituted TEMPO derivatives. The general detection of alkene and related carbonyl signals, in conjunction with the electrolyte acidification, reveals a deprotonation-initiated ring opening route that proceeds towards TEMPO decay. The protons on the β carbon are susceptible to chemical extraction by nucleophilic agents such as hydroxyl and the formed piperidine. This finding highlights the intrinsic structural factors for TEMPO degradation and will shed light on the potential stabilization strategies to afford long-cycling TEMPO-based flow batteries. 

The inaugural AI4EIC Hackathon unfolded as a high-point satellite event during the second AI4EIC Workshop at William & Mary. The workshop itself boasted over two hundred participants in a hybrid format and delved into the myriad applications of Artificial Intelligence and Machine Learning (AI/ML) for the Electron-Ion Collider (EIC). This workshop aimed to catalyze advancements in AI/ML with applications ranging from advancements in accelerator and detector technologies—highlighted by the ongoing work on the ePIC detector and potential development of a second detector for the EIC—to data analytics, reconstruction, and particle identification, as well as the synergies between theoretical and experimental research. Complementing the technical agenda was an enriched educational outreach program that featured tutorials from leading AI/ML experts representing academia, national laboratories, and industry. The hackathon, held on the final day, showcased international participation with ten teams from around the globe. Each team, comprising up to four members, focused on the dual-radiator Ring Imaging Cherenkov (dRICH) detector, an integral part of the particle identification (PID) system in ePIC. The data for the hackathon were generated using the ePIC software suite. While the hackathon presented questions of increasing complexity, its challenges were designed with deliberate simplifications to serve as a preliminary step toward the integration of machine learning and deep learning techniques in PID with the dRICH detector. This article encapsulates the key findings and insights gained from this unique experience.