Experimental and Virtual Screening of Phenazine and Phenothiazine Derivatives in Aqueous Solution for Electrochemical Carbon Capture

Abstract

Molecular redox-active compounds, including quinone and aza-aromatics, have emerged as tools for capturing carbon dioxide using renewable electricity. Given the vast and highly diverse chemical space of the candidate compounds, accessing their electrochemical properties in a rapid way is attractive for both experimental and virtual screening approaches.Here we present a study on the experimental screening of a series of commercial redox-active compounds. Phenazine and phenothiazine dyes have been experimentally tested for applications in electrochemical carbon capture, for which they are finding increasing applications as redox mediators due to their unique redox properties in aqueous solution. Nicotinamide has been used as a redox-inactive solubilizing agent to increase the solubilities of general phenazine and phenothiazine derivatives for rapid experimental screening of commercial compounds without time-consuming synthetic modifications. Cyclic voltammetry in aqueous solutions under nitrogen and carbon dioxide indicated reversible behavior in the pH swing of an aqueous solution. Bulk electrolysis experiments demonstrated the chemical stability of phenazine and phenothiazine derivatives. The redox properties and the chemical stability make phenazine and phenothiazine derivatives interesting candidates for future use as redox-active materials in electrochemical carbon capture.We also present progress towards a virtual screening for the optimization of electrochemical and physicochemical properties of redox-active compounds. Based on our experimental screening of commercial phenazine and phenothiazine compounds identifying neutral red as the lead compound, a virtual screening was performed for optimization of electrochemical and physicochemical properties using machine learning. The virtual screening development steps include (1) chemical library generation using neutral red as a lead compound, (2) molecular property prediction based on quantum chemical calculations, (3) aqueous solubility prediction using machine learning, (4) data processing and database creation, and (5) experimental validation of the optimized compounds for the use of electrochemical carbon capture. Figure 1