Multi-Tool Robotics Enables In-Situ Sample Manipulation for Time-Resolved Synchrotron Measurements
Authors: Aditya Bondada, Elizabeth M. Wall, Eric Yuan Xiao, Quinn C. Burlingame, Yueh-Lin Loo, Esther H. R. Tsai, Ruipeng Li
Organizations: Center for Functional Nanomaterials Brookhaven National Laboratory Upton, NY 11973, USA · Department of Chemical and Biological Engineering Princeton University Princeton, NJ 08540, USA · Department of Mechanical Engineering Stony Brook University Stony Brook, NY 11790, USA · National Synchrotron Light Source II Brookhaven National Laboratory Upton, NY 11973, USA
The high photon flux at synchrotron beamlines allows for the measurement of fast dynamical processes. However, beamline radiation-safety protocols prohibit human intervention during X-ray experiments, limiting the ability to perform versatile real-time sample manipulations during continuous data acquisition. Here we present a robotic platform at an X-ray scattering beamline to enable real-time sample handling and processing in the experimental hutch, revealing previously inaccessible transient in-situ dynamics in perovskite thin films. This modular multi-tool robotic architecture enables in-hutch sample manipulation beyond human-access constraints, establishing a foundation for automated and autonomous synchrotron experimentation.
Figures & tables
Figure 1: (a) Robotic platform on a modular table, including 1. robotic arm with camera, 2. sample garage, 3. annealing stage, 4. spin-coater and solutions for materials processing, 5. tool-exchange zone, and 6. Pilatus800k detector. The yellow line indicates the X-ray beam path. (b) RViz visualization of the planning scene with collision objects representing beamline hardware. The inset (c) shows the live camera view and vision-based sample detection marked by ArUco tags in green.
Figure 2: System architecture overview. Layered design spanning the application layer for experimental workflows and control, a middleware layer based on ROS 2 and MoveIt 2 for coordination and motion planning, and a hardware layer comprising robotic systems, motion stages, and beamline instrumentation.
Figure 3: Task definition schema. Each task file specifies the starting end-effector, named joint poses (6-DOF), and a sequence of task primitives. Supported task types include motion commands ( moveto ), vision-guided positioning ( vision_moveto ), end-effector control, and tool exchange operations.
Figure 4: Comparison of thermal annealing process characterized by in-situ GIWAXS on (a-e) robotic platform and (f-j) conventional thermal stage, including (a, f) scattering patterns at initial states of wet films and (b, g) final dry films, and (c-e, h-j) evolution of integrated intensities over time from wet to dry film. In (j) are the temperature (top) and the intensities (bottom) vs time. The δ -FAPbI 3 2H (100) reflection is marked as blue *, the PbI 2 (100) in purple #. In (d, i), the indexing present the cubic phase of α -FAPbI 3 , where the arrows mark the reflections of precursor-solvent complexes and the δ -FAPbI 3 6H (101), in (d) and (i) respectively.