The most recent IPCC report stressed that achieving international climate goals will require the widespread use of technologies that capture CO2 from flue gas and ambient air. Electrochemical approaches that capture and concentrate CO2 have received significant attention because they can theoretically be more energy efficient than existing thermal capture approaches, which have energy demands bounded by the Carnot cycle. Experimental studies conducted to date, however, have not been able to approach theoretical energy demands, even when inefficiencies from the reactor designs are considered. These studies were often performed as proof-of-concepts, with little attention given to how the solution composition (e.g., the concentration and properties of the CO2 sorbent in water) influenced energy demands, even though the solution composition places a fundamental limit on both the energy demand and capture rate. Our working hypothesis is that low energy demand electrochemical CO2 capture can be achieved by rationally optimizing the solution composition and sorbent properties. We have collected preliminary evidence supporting this hypothesis for the simplest electrochemical CO2 capture approach, in which proton-coupled electron transfer reactions are used to modulate the pH of water to drive CO2 uptake and release in a four-stage process. We created a computational simulation to identify the ideal properties of an electrochemically active sorbent and the concentrations of acid or base added to optimize energy demands and CO2 capture rates using an adaptive sampling method rate, and we have recently submitted a proof-of-concept paper to Chemical Engineering Science. In the work, we found that both CO2 capture energy demands and rates can be substantially improved to values that outperform thermal capture approaches by optimizing the solution chemistry.
Our aim is to computationally simulate more complex electrochemical capture approaches to identify which ones are most promising in terms of energy demands and/or capture rates based on their solution chemistries and sorbent properties. The issue that we face and aim to address with this proposal is that simulating these more complex approaches is too computationally demanding when using our current approach.
