A fully adaptive automated system for nanoparticle washing enabled by vision and language AI

초록

Self-driving laboratories are reshaping materials discovery by combining automated experimentation with AI-driven decision-making. However, the lack of automation in key preprocessing steps such as nanoparticle (NP) washing, remains a major barrier to achieving full experimental autonomy. Effective automation of NP washing requires visual adaptivity to detect subtle changes in the appearance of dispersions or precipitates, and cognitive adaptivity, to handle failure cases like incomplete sedimentation or phase separation. These demands make NP washing uniquely challenging despite its apparent simplicity. We introduce a fully integrated NP washing platform that combines computer vision with a large language model (LLM) to enable intelligent, end-to-end preprocessing in self-driving labs. The system employs YOLACT for real-time robotic manipulation and latent mask R-CNN for uncertainty-aware image segmentation, achieving 100% task success across 60 trials and accurately processing 45 diverse precipitate images. A retrieval-augmented LLM autonomously generates and continuously refines washing protocols based on cognitive feedback from failure detection. The platform was validated on NiFe layered double hydroxides, IrRu nanoparticles, and CdSe/CdS quantum dots. Electrochemical and photoluminescence analyses demonstrated that the automated washing achieves a quality comparable to manual processing. This work represents a critical step toward fully autonomous, closed-loop experimentation by bridging synthesis and characterization through intelligent preprocessing.

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