Agentic Preparative Thin-Layer Chromatography System for Autonomous Purification

Abstract The rapid advancement of autonomous laboratories has significantly accelerated reaction discovery and optimization, yet downstream compound purification remains a manual and poorly integrated bottleneck. Preparative thin-layer chromatography (pTLC) is a core small-scale purification method, yet full automation has been hindered by the intrinsic coupling of multistep separation reasoning and millimeter-precision robotic recovery. Here, we present PLANAR (Preparative Layer Chromatography Agentic Network for Autonomous Recovery), a supervised agentic system that provides a gated, auditable route from target-directed purification requests to isolated products. PLANAR integrates a modular robotic workcell for end-to-end plate handling, development, and recovery; a two-stage predictive model (A2P-Cascade) that ranks candidate mobile phases based on planned band center and bandwidth priors; and an agentic controller that orchestrates these tools through explicit review gates. Trained on standardized TLC and pTLC records, A2P-Cascade achieves pooled R2 values of 0.835 and 0.673 for normalized band center and width on a compound-level formal-validation split. Across three proof-of-workflow cases spanning structural formula, reaction-image, and fixed-condition analytical TLC inputs, PLANAR delivered NMR-verified isolated products while grounding recovery decisions in evidence from preparative plates. This framework transforms purification into a traceable module for self-driving laboratories, supporting the generation of standardized run records for iterative model refinement and prospective integration with online process analytical technologies. By bridging the gap between synthesis design and verified isolated products, PLANAR moves autonomous chemical workflows toward complete end-to-end experimental discovery cycles.