Automated Workflow for Ion-Selective Electrodes via Mixed-Ion Inference.

Selective electrochemical extraction of Li+ from brines requires repeated solution exchange and quantitative tracking of Li+/Na+ composition across capture and release steps. Here, we develop an automated platform that couples lithium manganese oxide-based electrochemical Li+ extraction, programmable fluid handling, and paired Li+/Na+ ion-selective electrode analysis. To enable quantification in mixed solutions, established pure-ion, separate-solution, and fixed-interference characterization is combined with a physically interpretable dual-sensor model trained across mixed LiCl/NaCl compositions. Rather than evaluating sensor performance through a single selectivity coefficient, the model is tested directly through simultaneous recovery of Li+ and Na+ concentrations within the experimentally calibrated composition domain. The platform automates solution delivery, electrochemical capture, washing, release, sample collection, and subsequent at-line potentiometric quantification over repeated extraction cycles, enabling cycle-resolved determination of Li+/Na+ separation performance. This application-driven workflow connects automated electrochemical separation with mixed-ion composition inference and provides a transparent route toward autonomous evaluation and optimization of Li+ recovery processes.