Robotic Pendant Drop: Containerless Liquid for μs-resolved, AI-executable XPCS

Autor: Qingteng Zhang, Doga Ozgulbas, Don Jensen, Michael Irvin, Yasukazu Nakaye, Eric Dufresne, Soenke Seifert, Gyorgy Babnigg, Arvind Ramanathan
Rok vydání: 2023
DOI: 10.21203/rs.3.rs-2527169/v1
Popis: X-ray Photon Correlation Spectroscopy (XPCS) probes the spontaneous fluctuation in spatial configurations of mixed phases, which can significantly impact the material properties of complex fluid. Tailored material design, however, requires navigation through massive multi-dimensional parameter space which is beyond the bandwidth of current XPCS beamline infrastructure. Using 3.7 μs-resolved XPCS, we demonstrate that Brownian dynamics of silica colloidal nanoparticles in a shielded pendant drop suspended from an electronic pipette is consistent with a sealed capillary, validating the use of pendant drop for automated XPCS measurement. Furthermore, the electronic pipette can be mounted on a robotic arm to access different stock solutions and create samples with highly-repeatable and precisely-controlled composition profiles (Video S2). This closed-loop, AI-executable experimental protocol is the first step towards autonomous material discovery and is applicable for not only XPCS, but also other high-throughput light and x-ray scattering techniques for multimodal, collaborative material characterization.
Databáze: OpenAIRE