A low-cost automated platform for fast and accurate pH control via physics-informed active learning.

The precise adjustment of pH in complex buffered systems represents a critical process in chemical synthesis and biopharmaceutical development. However, the intricate multi-buffer equilibria pose significant challenges for conventional methods, leading to modeling difficulties and low optimization efficiency. We have developed a low-cost automated titration platform (total hardware cost < 100 USD) and established a hybrid physics-informed active learning framework that achieves target pH values in as few as 3-5 experimental iterations. Validation across diverse buffer systems, including phosphate, acetate, citrate, and ammonium buffers, demonstrates substantial efficiency improvements over purely data-driven methods, with rapid convergence to the target pH achieved in minimal experimental iterations. Beyond its scientific contributions, this work also offers important pedagogical value by providing a low-cost, transparent, and modular platform that allows students and early-stage researchers to gain hands-on experience in automated experimentation, chemical equilibria, and machine learning.