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Automated Solubility Screening Platform Using Computer Vision

Parisa Shiri, Veronica Lai, Tara Zepel, Daniel Griffin, Jonathan Reifman, Sean Clark, Shad Grunert, Lars P. E. Yunker, Sebastian Steiner, Henry Situ, Fan Yang, Paloma L. Prieto, Jason E. Hein

Year
2020
Citations
4
Access
Open access

Abstract

Solubility screening is an essential, routine process that is often labour intensive. Robotic platforms have been developed to automate some aspects of the manual labour involved. However, many of the existing systems rely on traditional analytic techniques such as High Performance Liquid Chromatography or HPLC, which require pre-calibration for each compound and can be prohibitively expensive. In addition, automation is not typically end-to-end, requiring user intervention to move vials, establish analytical methods for each compound and interpret the raw data. We developed a closed-loop, flexible robotics system with integrated solid and liquid dosing capabilities that relies on computer vision and iterative feedback to successfully measure caffeine solubility in multiple solvents. After initial researcher input (<2 min), the system ran autonomously, screening five different solvent systems (20-80 min each). The resulting data matched values obtained using traditional manual techniques.

Keywords

AutomationSolubilityRoboticsComputer scienceArtificial intelligenceProcess engineeringRobotEngineeringChemistryMechanical engineering

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