Laboratory Automation
pHbot: Self-driven robot for pH adjustment of viscous formulations via physics-informed-ML
Aniket Chitre, Yusuf Hamied Department of Chemistry, University of Cambridge
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 labscale 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 ~25x scale-up. The method is general, and we open-source our algorithm and designs.
Modular automation of reactors
Dr Nicholas Jose, Accelerated Materials Ltd
Reactor technologies can be a limiting factor in discovery, scale-up and manufacturing. Automation can greatly increase R&D throughput by decreasing manual labour, increasing quality of data and enabling the integration of AI; however, the actual process of automating reactors often requires costly equipment, time and engineering expertise. In this work we present the use of FLAB. FLAB is a flexible, python-based framework for developing laboratory automation applications. In this talk, Nicholas will explore the trials and tribulations of reactor automation in several case studies in academia and industry at the iDMT, and share key insights into the automation process.