Author: Rodriguez, Hollie

The RPL has developed a groundbreaking method that leverages robotics to automate the preparation, characterization, and disposal of complex liquid samples for synchrotron coherent X-ray scattering experiments. Using a pendant drop technique shielded from air turbulence, the automated experiments achieved results consistent with traditional containers, making this approach suitable for high-precision studies. The robotic system, featuring an electronic pipette mounted on a robotic arm, enables precise sample handling and high-throughput exchange. By integrating a single Python script with beamline and robot control libraries, this enables seamless automation, paving the way for AI-driven, fully autonomous material design at large-scale scientific facilities. This approach enhances experimental efficiency and consistency, revolutionizing the study of complex fluids. Read the related paper.

The RPL introduced a compact, fully automated robotic system designed for the precise assembly of small liquid/gel chambers with advanced robotics at its core. This system automates the entire process, from sample preparation to data collection, for X-ray and neutron scattering experiments. Featuring transparent polycarbonate windows and a metallic body for temperature control, it minimizes human interference, improving reliability, particularly for delicate biological samples. Its seamless integration into experimental stations like synchrotrons and X-ray free electron lasers enables efficient, automated sample exchange during beamtime, while AI-driven data analysis ensures a fully autonomous workflow. This robotic platform revolutionizes soft material discovery and biomaterial development through advanced automation and robotics.

Automating the design of antimicrobial peptides, which can be used to combat antimicrobial-resistant bacterial strains, was the key motivation for developing Argonne’s BIO-Workcell. This proof of concept was demonstrated on E. coli bacterial strains using high-throughput screening techniques in concert with state-of-the-art artificial intelligence. This approach shows promise for accelerating the drug discovery process, making it faster and more efficient to identify potential therapeutic compounds.