Robotic Nanoparticle Synthesis via Solution-based Processes
Authors: Dasharadhan Mahalingam, Michael Gallagher, Nilanjan Chakraborty, Stanislaus S. Wong
Organizations: Department of Mechanical Engineering, Stony Brook University, Stony Brook, NY 11794, USA · Department of Chemistry, Stony Brook University, Stony Brook, NY 11794, USA
We present a screw geometry-based manipulation planning framework for the robotic automation of solution-based synthesis, exemplified through the preparation of gold and magnetite nanoparticles. The synthesis protocols are inherently long-horizon, multi-step tasks, requiring skills such as pick-and-place, pouring, turning a knob, and periodic visual inspection to detect reaction completion. A central challenge is that some skills, notably pouring, transferring containers with solutions, and turning a knob, impose geometric and kinematic constraints on the end-effector motion. To address this, we use a programming by demonstration paradigm where the constraints can be extracted from a single demonstration. This combination of screw-based motion representation and demonstration-driven specification enables domain experts, such as chemists, to readily adapt and reprogram the system for new experimental protocols and laboratory setups without requiring expertise in robotics or motion planning. We extract sequences of constant screws from demonstrations, which compactly encode the motion constraints while remaining coordinate-invariant. This representation enables robust generalization across variations in grasp placement and allows parameterized reuse of a skill learned from a single example. By composing these screw-parameterized primitives according to the synthesis protocol, the robot autonomously generates motion plans that execute the complete experiment over repeated runs. Our results highlight that screw-theoretic planning, combined with programming by demonstration, provides a rigorous and generalizable foundation for long-horizon laboratory automation, thereby enabling fundamental kinematics to have a translational impact on the use of robots in developing scalable solution-based synthesis protocols.
Figures & tables
Figure 1: Robot performing constrained manipulation tasks as part of Magnetite Nano-Particle synthesis experiment.
Figure 2: User provided kinesthetic demonstrations for the manipulation tasks involved in the nanoparticle synthesis.
Figure 3: Automated Gold Nanoparticle Synthesis using Robots: Sequence of tasks involved in the synthesis of Gold Nanoparticles.
Figure 4: Observable differences in gold nanoparticle concentration when the robot was instructed to perform simple lab tasks, such as stirring.
Figure 5: Gold Nanoparticle growth over time.
Figure 6: Comparison of as-synthesized gold nanoparticles, with and without stirring.
Figure 7: Automated Magnetite Nanoparticle Synthesis using Robots: Sequence of tasks involved in the synthesis of Magnetite Nanoparticles.
Figure 8: Characterization of magnetite nanoparticles synthesized by the robot.
Figure 9: XRD pattern of magnetite nanoparticles synthesized by the robot. Major peaks associated with magnetite are labeled.
Figure 10: Magnetite titration curve and corresponding first derivative.
Autonomous Systems Lab, Technische Universität Wien (TU Wien), Austria · Institute of Robotics and Mechatronics, German Aerospace Center (DLR), Germany