使用自动化实验平台对材料合成过程进行定量控制与记录
Quantitative control and recording of materials-synthesis processes using an automated experimentation platform
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中文总结 AI 辅助
本研究构建了基于市售仪器和AI生成控制代码的自动化实验平台，实现材料合成过程的可靠自动化与定量记录，并成功合成ZIF-8，发现粒径分布强烈依赖移液速度，验证了可重复性。
中文摘要 AI 辅助
数据驱动的材料开发需要收集大量高质量的材料数据。材料实验的完全自主化虽被期待，但技术门槛高，应用仍有限。在本研究中，我们构建了一个简单、易于部署的自动化实验平台，其重点不在于完全自主，而在于实验过程的可靠自动化与定量记录。具体而言，该平台结合了市售仪器，如机械臂、电动移液器、网络摄像头和电子天平，使用3D打印机加工夹具等组件，并由基于大语言模型的AI智能体生成的控制代码来操作这些仪器。作为演示，我们将该平台应用于双溶液混合实验系统，并合成了金属有机框架ZIF-8。产物中观察到白色悬浮相，X射线衍射测量确认其为ZIF-8。我们还发现，其粒径分布强烈依赖于电动移液器的溶液分配速度，而这一参数在手动操作中难以控制或记录。这种依赖性在重复运行中得以重现，证实了自动化合成的可重复性。该结果很好地表明，在手动工作中很少被量化的过程参数的控制与记录，能够决定材料数据的质量。所有控制代码、CAD模型和文档均已公开，以促进实验室规模实验自动化的推广。
英文摘要
Data-driven materials development requires the collection of large amounts of high-quality materials data. Full autonomy of materials experiments is anticipated, but its technical hurdles are high and its adoption remains limited. In this study, we constructed a simple, easy-to-deploy automated experimentation platform that focuses not on full autonomy but on the reliable automation and quantitative recording of experimental processes. Specifically, commercially available instruments such as robot arms, electric pipettes, web cameras, and an electronic balance are combined, components such as fixtures are fabricated with a 3D printer, and the instruments are operated by control code generated by an AI agent based on a large language model. As a demonstration, we applied the platform to a two-solution mixing experimental system and synthesized ZIF-8, a metal-organic framework. A white suspension phase was observed in the product, and X-ray diffraction measurements confirmed that it was ZIF-8. We also found that its particle size distribution depends strongly on the solution dispensing speed of the electric pipette, which is a parameter that is difficult to control or record in manual operation. This dependence was reproduced in repeated runs, confirming the repeatability of the automated synthesis. This result is a good example showing that the control and recording of process parameters that are rarely quantified in manual work can govern the quality of materials data. All control code, CAD models, and documentation are made publicly available to encourage the spread of laboratory-scale automation of experiments.
