用自动实验平台优化温敏聚合物的相变温度，高效精准达成目标。
Self-Driving Laboratory Optimizes the Lower Critical Solution Temperature of Thermoresponsive Polymers
- 融合机器人、传感器与贝叶斯优化，闭环调控盐溶液成分。
- 仅需少量实验即实现目标相变温度，且能从偏离结果中学习。
- 低成本可复现方案，适合材料研发人员快速部署自动化实验。
为突破传统试错法在材料发现中的低效瓶颈，科学界正推动将数据驱动决策融入闭环实验流程的自主实验室建设。本文针对温敏聚合物，构建了一种低成本、可复现的“节俭孪生”平台，用于优化聚N-异丙基丙烯酰胺（PNIPAM）的下临界溶解温度（LCST）。系统集成机器人液体处理、在线传感器与贝叶斯优化（BO），在多组分盐溶液空间中导航，实现用户指定的LCST目标。平台在极少实验次数内收敛至目标，策略性探索参数空间，从有信息量的“非目标”结果中学习并自我修正。该工作提供了一种可推广的自动化实验蓝图，降低进入门槛，加速功能型聚合物的设计与发现。
原文摘要 · Abstract (English)
To overcome the inherent inefficiencies of traditional trial-and-error materials discovery, the scientific community is increasingly developing autonomous laboratories that integrate data-driven decision-making into closed-loop experimental workflows. In this work, we realize this concept for thermoresponsive polymers by developing a low-cost, "frugal twin" platform for the optimization of the lower critical solution temperature (LCST) of poly(N-isopropylacrylamide) (PNIPAM). Our system integrates robotic fluid-handling, on-line sensors, and Bayesian optimization (BO) that navigates the multi-component salt solution spaces to achieve user-specified LCST targets. The platform demonstrates convergence to target properties within a minimal number of experiments. It strategically explores the parameter space, learns from informative "off-target" results, and self-corrects to achieve the final targets. By providing an accessible and adaptable blueprint, this work lowers the barrier to entry for autonomous experimentation and accelerates the design and discovery of functional polymers.
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