pHbot: self-driven robot for pH adjustment of viscous formulations via physics-informed-ML**

Abstract

pH adjustment is crucial for many industrial products, yet this step is typically performed by manual trial-and-error. A particularly industrially relevant yet challenging titration is that of adjusting viscous liquid formulations using weak, polyprotic titrants (usually citric acid). Handling of viscous, non-Newtonian formulations, with such polyprotic acids preferred for their chelation and buffering effects make a robotic solution challenging. We present a self-driving pH robot integrated with physics-informed learning; this hybrid physical-ML model enables automated titration with weak-strong acid/base pairs. To deal with the high viscosities of these formulations, we developed specific automated mixing and cleaning protocols. We hit the target pH within two to five iterations over 250 distinct formulations in lab-scale small-batch (~10 mL and 12 samples) titrations. In the interest of scaling up to match industrial processes, we also demonstrate that our hybrid algorithm works at ~25× scale-up. The method is general, and we open-source our algorithm and designs.