Broadband Microwave Microfluidic Spectroscopy to Capture Product Evolution in Electrosynthesis Reactions

Electrosynthesis plays an important role in chemical manufacturing with over 2 billion dollars in annual sales worldwide of products such as chlor-alkali and aluminum. Synthesis with an electrochemical driving force can afford unique chemical products that are difficult to achieve with conventional synthesis techniques. Electrosynthesis can also provide a more sustainable alternative to energy-intensive processes. For example, the most profitable electro-organic synthesis in industry is adiponitrile production, a precursor to Nylon 6,6, and the electrochemical route consumes less energy than conventional thermochemical methods. However, electrosynthesis reaction mechanisms are often complex and involve multiple biproducts that are difficult to distinguish, limiting product yield and cost efficiency. Prior work involves characterization with techniques such as NMR spectroscopy, mass spectrometry, and optical spectroscopy to understand product evolution during the reaction. These methods tend not to capture intermolecular interactions between substances, which is important for understanding the influence of the dielectric environment on reaction outcomes. Here, we propose to analyze the dielectric environment of electrosynthesis reaction mixtures with on-chip broadband microwave microfluidic spectroscopy (MMS) from 40 kHz – 110 GHz (see attached figure). MMS has been implemented previously for capturing broadband fluid effects such as electric double layer formation, ionic conduction, ion pairing, and dipole relaxations in a single measurement. We want to use MMS to diagnose the interactions between species during an electrolysis reaction and correlate these interactions with specific electrochemical mechanisms. We plan to test our idea by analyzing a model reaction, namely the Shono oxidation of N-Boc pyrrolidine, which is a well-studied electrosynthesis reaction. The goal is to validate our method with a known electrosynthesis reaction mechanism, then extend the technique to systems with an unknown mechanism. We performed broadband MMS of the reaction mixture at different time points to capture product evolution during the electrolysis. Then, we calibrated the resulting S-parameters and fit the data to equivalent circuit models. The equivalent circuit parameters were correlated to signatures from specific entities in the product mixture and used to develop a picture of the evolving dielectric environment. After validating our models with known literature about the Shono oxidation mechanism, we plan to extend our technique to other electrolysis reactions with unknown mechanisms. The goal is to provide a high-throughput technique for identifying key species and interactions that influence the outcome of electrolysis reactions. Figure 1