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Buffer solutions have tremendous importance in biological systems and in formulated products. Whilst the pH response upon acid/base addition to a mixture containing a single buffer can be described by the Henderson-Hasselbalch equation, modelling the pH response for multi-buffered poly-protic systems after acid/base addition, a common task in all chemical laboratories and many industrial plants, is a challenge. Combining predictive modelling and experimental pH adjustment, we present an active machine learning (ML)-driven closed-loop optimization strategy for automating small scale batch pH adjustment relevant for complex samples (e.g., formulated products in the chemical industry). Several ML models were compared on a generated dataset of binary-buffered poly-protic systems and it was found that Gaussian processes (GP) served as the best performing models. Moreover, the implementation of transfer learning into the optimization protocol proved to be a successful strategy in making the process even more efficient. Finally, practical usability of the developed algorithm was demonstrated experimentally with a liquid handling robot where the pH of different buffered systems was adjusted, offering a versatile and efficient strategy for a pH adjustment processes.

The first demonstration of aqueous surfactant-enabled Suzuki–Miyaura couplings run under flow conditions is described. In addition, use of an even more challenging heterogeneous nanoparticle catalyst, containing only 800 ppm of Pd (i.e., 0.08 mol%) in the form of Fe/ppm Pd nanoparticles, is sufficient using a continuous stirred-tank reactor (CSTR).