Hexafluoroisopropanol (HFIP) is an important intermediate of sevoflurane, an inhalation anesthetic for pediatric anesthesia, and it is produced industrially mainly by the catalytic hydrogenation reduction of hexafluoroacetone (HFA). In previous studies, the hydrogenation of HFA was usually performed in batch reactors with the disadvantages of harsh reaction conditions and low conversion rates. On the other hand, continuous flow technology is increasingly being used for intrinsically safe hydrogenation processes to avoid safety issues caused by the accumulation of hydrogen gas in high-pressure reactors. In this study, a continuous flow system was developed in a micropackedbed reactor, and the intrinsic kinetics of the reaction were studied. This continuous flow process minimized mass and heat transfer problems and achieved higher productivity in terms of time and space yield than traditional process methods. Under kinetically controlled conditions, the operating conditions were varied with temperature between 363 and 393 K, hydrogen pressure of 10 bar, and catalyst loading between 0.1 and 0.5 g. In the end, we achieved conversion and selectivity of up to 99% and a space−time yield of about 9 times that of the batch reactor. To further investigate the long-term stability of the reaction system, the flow system was successfully operated for 90 h at a liquid flow rate of 0.5 mL/min. In addition, residence time distribution curves at different flow rates were determined, and possible mechanistic pathways of the reaction were explored based on the Langmuir−Hinshelwood method. The reaction was found to be an adsorption−desorption type with a mechanism of competitive adsorption by H 2 dissociation, and the thermodynamic properties associated with the rate constants were estimated.

This paper describes a machine learning guided framework for screening the potential toxicity impact of amine chemistries used in the synthesis of hybrid organic-inorganic perovskites. Using a combination of a probabilistic molecular fingerprint technique that encodes bond connectivity (MinHash) coupled to non-linear data dimensionality reduction methods (UMAP), we develop an “Amine Atlas’. We show how the Amine Atlas can be used to rapidly screen the relative toxicity levels of amine molecules used in the synthesis of 2D and 3D perovskites and help identify safer alternatives. Our work also serves as a framework for rapidly identifying molecular similarity guided, structure-function relationships for safer materials chemistries that also incorporate sustainability/ toxicity concerns.