发表机构
Acceleration Consortium, University of Toronto; AISCIA Informatics; Hamad Bin Khalifa University (HBKU)（多伦多大学加速联盟; AISCIA信息学公司; 哈马德·本·哈利法大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对商业液体处理系统的局限，提出将消费级3D打印机改装为低成本液体处理机器人RAINBOTTM的方法，通过数字孪生实现远程监控，结合CEIDTM框架进行目标导向实验，成本低，建立了可访问的物理 - 虚拟实验室自动化框架。
AI 中文摘要
实验室自动化虽能加速发现，但商业液体处理系统的高成本、专有设计和有限的远程可监控性限制了其应用。本文介绍了RAINBOTTM，这是一种低成本、可公开重现的液体处理机器人，由消费级笛卡尔3D打印机改装而成。通过Python控制，打印机挤出机被精密单通道移液器取代，由打印机自身的G代码驱动X - Y - Z龙门架，两个紧凑型线性执行器实现柱塞和吸头弹出动作。为使实验透明且可远程监控，实现了基于浏览器的数字孪生，与物理平台双向同步，实时镜像运动学和移液状态。作为概念验证，RAINBOTTM进行了不同颜色水溶液的顺序交换，集成颜色传感器量化混合结果，测量的红、黄、蓝（RYB）响应与预期混合行为的平均绝对误差在两个百分点以内。该平台还与CEIDTM框架耦合，将实验从迭代手动猜测转变为目标导向的逆向设计搜索。其完整硬件成本低于1300美元，为自动驾驶实验室自动化建立了可访问的物理 - 虚拟框架。
英文摘要
Laboratory automation accelerates discovery, yet its adoption is constrained by the high cost, proprietary design, and limited remote supervisability of commercial liquid-handling systems. This work presents RAINBOT\textsuperscript{TM}, a low-cost, openly reproducible liquid-handling robot built by converting a consumer-grade Cartesian 3D printer (Elegoo Neptune 4 Max). The printer extruder is replaced by a precision single-channel pipette actuated through the printer's own G-code-driven X--Y--Z gantry, with plunger and tip-eject motions effected by two compact linear actuators under Python control. To make experiments transparent and remotely supervisable, a browser-based digital twin is implemented to synchronise bidirectionally with the physical platform, mirroring kinematics and pipetting states in real time and exposing remote monitoring, intervention, and an emergency stop from any web browser. As a proof of concept, RAINBOT\textsuperscript{TM} performed sequential exchanges of differently coloured aqueous solutions while an integrated colour sensor quantified the resulting mixtures; measured red, yellow, and blue (RYB) responses agreed with expected mixing behaviour to within a mean absolute error of two percentage points, validating correct execution and real-time tracking. Closing the loop, the platform is coupled to the CEID\textsuperscript{TM} (Cooperative Explorer for Inverse Design) framework, which recasts experimentation from iterative manual guessing into a goal-directed inverse-design search while keeping a human in the loop. The complete hardware costs under US\$1300, which is roughly an order of magnitude below entry-level commercial handlers, thereby establishing an accessible physical--virtual framework for self-driving laboratory automation.
