The discovery of processes for the synthesis of new materials involves many decisions about process design, operation, and material properties. Experimentation is crucial but as complexity increases, exploration of variables can become impractical using traditional combinatorial approaches. We describe an iterative method which uses machine learning to optimise process development, incorporating multiple qualitative and quantitative objectives. We demonstrate the method with a novel fluid processing platform for synthesis of short polymer fibers, and show how the synthesis process can be efficiently directed to achieve material and process objectives.

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Manganese(II) salts catalyze the chemiluminescent oxidation of organic compounds with acidic potassium permanganate. The formation of insoluble manganese(IV) species from the reaction between manganese(II) and permanganate can be prevented with sodium polyphosphate, and therefore, relatively high concentrations of the catalyst can be added to the reagent before the light-producing reaction is initiated. The rapid and intense emissions from these manganese(II) catalyzed chemiluminescence reactions provide highly sensitive detection and greater compatibility with liquid chromatography.

The significant enhancement of acidic potassium permanganate chemiluminescence by Mn(II) results from the concomitant presence of permanganate and Mn(III) in the reagent solution, which enables rapid production of the excited Mn(II) emitter with a wide range of analytes. Furthermore, the key Mn(III) co-reactant can be quickly generated by reducing permanganate with sodium thiosulfate, instead of the slow (~24 h) equilibration required when Mn(ii) is used. The emission from reactions with analytes such as tyrosine and fenoterol was over two orders of magnitude more intense than with the traditional permanganate reagent.

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