Automation Systems for Experimentation: Design of Open-Source Liquid Handlers and Self-Driving Liquid Handling Laboratories

Engineering notes

Modern data science tools like machine learning and Bayesian optimization can significantly enhance future laboratory productivity and alleviate the need for traditional manual trial-and-error experimentation with rational decision making. To meet this need for open-source and accessible technologies, three liquid handling systems have been designed and built with fully autonomous workflows in mind, offering practical alternatives for researchers to integrate into their workflows.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Scientific research faces growing challenges in experimentation as complexity increases in researched problems, demanding more efficient, high-throughput experimentation methods to navigate these complex, multidimensional parameter spaces. Modern data science tools like machine learning and Bayesian optimization can significantly enhance future laboratory productivity and alleviate the need for traditional manual trial-and-error experimentation with rational decision making. However, existing liquid handling solutions are prohibitively expensive, closed source, or difficult to integrate with fully autonomous self-driving lab (SDL) architectures. To meet this need for open-source and accessible technologies, three liquid handling systems have been designed and built with fully autonomous workflows in mind, offering practical alternatives for researchers to integrate into their workflows. These systems include: a pen-plotter based liquid handler repurposed from commercially available components for low-cost liquid handling; a fully custom-built gantry-style liquid handler that provides a modular foundation for multiple liquid handling approaches; and a flexible, machine vision guided 5-axis robotic arm capable of complex and dexterous laboratory interactions. An example SDL was designed, built, and validated through an enzyme optimization problem to demonstrate the ability and viability of navigating multidimensional design spaces and deciphering feature-property relationships. The resulting data demonstrates a scalable, accessible framework for scientific discovery, lowering the barrier for researchers to adopt self-driving experimentation into other biological or chemical applications.

Links and sources

Comments