Researchers used infrared spectroscopy at the ALS to detect the molecular behaviors of ionic liquids—which serve as high performance electrolytes in energy storage devices—under varying charge bias conditions. Their insights define a direction for targeted design of ionic liquid-based electrolytes with optimized properties for energy storage applications. Read more »
Aerosol Chemistry Offers Clues to the Arctic’s Future
Researchers used scanning transmission x-ray microscopy to analyze Arctic aerosols, which strongly influence cloud formation and overall climate. Understanding what these particles are and how they change as they travel could help improve climate models and yield more accurate predictions of the changing Arctic environment’s global impact. Read more »
Disrupting Cancer’s Broken Molecular Switch
Researchers identified a compound that disrupts a hard-to-target tumor growth pathway in breast, lung, and other cancers and used the ALS to characterize the chemical interactions critical to its potency. This work contributed to the development of a similar compound currently undergoing clinical trials in cancer patients, and informs hypotheses for designing better drug candidates. Read more »
AI Delivers Rapid, Precise Design of Tumor-Targeting Protein
A new protein designed using AI can precisely recognize a key therapeutic target for cancer. X-ray crystallography data collected at the ALS confirmed the new protein’s specificity for its target, demonstrating a configurable and scalable approach to cancer therapy. Read more »
Dynamic Surface Restructuring in Ag–Cu Boosts CO2 Conversion
Multimodal in situ x-ray experiments at the ALS revealed how copper–silver nanoparticle catalysts evolve during CO2 photoreduction. The findings, which demonstrate dynamic catalyst restructuring at the atomic level, provide crucial insights for enhancing the selectivity and efficiency of CO2 conversion into high-value chemicals. Read more » 
Robotics Project Pushes Toward Self-Driving Materials Optimization
A new multi-disciplinary team aims to automate complex sample handling at Beamline 7.3.3, leveraging AI and robotics to speed up material optimization and discovery. Read more »