Bibliographic Details
| Title: |
Theoretical performance optimization of enzymatic electrochemical CO2 reduction to formate: Voltage, concentration, temperature, pressure, and flow rate |
| Authors: |
Daniel Moreno |
| Source: |
Journal of CO2 Utilization, Vol 83, Iss , Pp 102805- (2024) |
| Publisher Information: |
Elsevier, 2024. |
| Publication Year: |
2024 |
| Collection: |
LCC:Technology |
| Subject Terms: |
Computational, Enzymatic, Electrochemical CO2, Flow cell, Performance optimization, Technology |
| More Details: |
Formic acid (FA) is a notable fuel product due to its atom economy, low activation energy, and applications in flow cells and hydrogen storage. While metal catalysts are typically used, selectivity remains a challenge. Here, an enzymatic catalyst is employed to selectively convert CO2 to FA as formate. This study documents the development of a computational model to examine the conversion of CO2 to formate under a wide range of conditions. The model examines the electrochemical reduction of a charge carrier, ethyl viologen (EV), and its subsequent use in an enzymatic catalyst to convert CO2 and protons in solution to formate. The model was first developed for a small-scale batch reactor, then later expanded to a dual-cell flow system, where the reduction of EV and production of formate are kept in separated cells, and flow rate is introduced as an additional variable parameter. While no studies have directly used all parameters addressed in the computations presented here, many of the conditions selected align with what has previously been used in experiments, and similar production rates and efficiencies are obtained. The most challenging parameters to study were charge carrier concentration and applied voltage, which showed optimal ranges in the cases studied for the batch and flow cell. While the study gives guidance toward which conditions would be favored experimentally to increase production rate and efficiency experimental studies should nonetheless be run at suggested optimal conditions to better adapt parameters made in both models. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2212-9839 |
| Relation: |
http://www.sciencedirect.com/science/article/pii/S2212982024001409; https://doaj.org/toc/2212-9839 |
| DOI: |
10.1016/j.jcou.2024.102805 |
| Access URL: |
https://doaj.org/article/713ed4817e1e44dd9792bfee11002099 |
| Accession Number: |
edsdoj.713ed4817e1e44dd9792bfee11002099 |
| Database: |
Directory of Open Access Journals |
