Working at the interfaces of data science and synthetic electrochemistry

Polymer up-cycling by mangana-electrocatalytic C(sp3)-H azidation without directing groups

The chemical up-cycling of polymers into value-added materials offers a unique opportunity to place plastic waste in a new value chain towards a circular economy. Herein, we report the selective up-cycling of polystyrenes and polyolefins to C(sp3)-H azidated materials under electrocatalytic conditions. The functionalized polymers were obtained with high retention of mass average molecular mass and high functionalization through chemo-selective mangana-electrocatalysis. Our strategy proved to be broadly applicable to a variety of homo- and copolymers. Polyethylene, polypropylene as well as post-consumer polystyrene materials were functionalized by this approach, thereby avoiding the use of hypervalent-iodine reagents in stoichiometric quantities by means of electrocatalysis. This study, hence, represents a chemical oxidant-free polymer functionalization by electro-oxidation. The electrocatalysis proved to be scalable, which highlights its unique feature for a green hydrogen economy by means of the hydrogen evolution reaction (HER).

Chemical Reaction Networks Explain Gas Evolution Mechanisms in Mg-Ion Batteries

Out-of-equilibrium electrochemical reaction mechanisms are notoriously difficult to characterize. However, such reactions are critical for a range of technological applications. For instance, in metal-ion batteries, spontaneous electrolyte degradation controls electrode passivation and battery cycle life. Here, to improve our ability to elucidate electrochemical reactivity, we for the first time combine computational chemical reaction network (CRN) analysis based on density functional theory (DFT) and differential electrochemical mass spectroscopy (DEMS) to study gas evolution from a model Mg-ion battery electrolyte─magnesium bistriflimide (Mg(TFSI)2) dissolved in diglyme (G2). Automated CRN analysis allows for the facile interpretation of DEMS data, revealing H2O, C2H4, and CH3OH as major products of G2 decomposition. These findings are further explained by identifying elementary mechanisms using DFT. While TFSI- is reactive at Mg electrodes, we find that it does not meaningfully contribute to gas evolution. The combined theoretical-experimental approach developed here provides a means to effectively predict electrolyte decomposition products and pathways when initially unknown.

Bayesian optimization-driven parallel-screening of multiple parameters for the flow synthesis of biaryl compounds

Traditional optimization methods using one variable at a time approach waste time and chemicals and assume that different parameters are independent from one another. Hence, a simpler, more practical, and rapid process for predicting reaction conditions that can be applied to several manufacturing environmentally sustainable processes is highly desirable. In this study, biaryl compounds were synthesized efficiently using an organic Brønsted acid catalyst in a flow system. Bayesian optimization-assisted multi-parameter screening, which employs one-hot encoding and appropriate acquisition function, rapidly predicted the suitable conditions for the synthesis of 2-amino-2'-hydroxy-biaryls (maximum yield of 96%). The established protocol was also applied in an optimization process for the efficient synthesis of 2,2'-dihydroxy biaryls (up to 97% yield). The optimized reaction conditions were successfully applied to gram-scale synthesis. We believe our algorithm can be beneficial as it can screen a reactor design without complicated quantification and descriptors.