Decision tree analysis for efficient CO2 utilization in electrochemical systems. (December 2018) - Günay, M. Erdem; Türker, Lemi; Tapan, N. Alper
Highlights: A database of 471 experimental data points was analyzed to determine the significance of catalytic variables and operational conditions. Cu content was the main criterion for high faradaic efficiency and production rate and Sn content was the main criterion for selectivity. Sn content higher than 15% and Cu content less than 52% was determined as the most generalizable path for highest selective product and highest rate. Exploratory data analysis and decision trees can provide useful information that may guide future studies for CO2 -electroreduction. Abstract: In this work, a database of 471 experimental data points excerpted from 34 different publications on electrocatalytic reduction of CO2 was formed. Firstly, the database was examined by exploratory data analysis using box and whiskers plots. Then, decision tree analysis was applied to determine the significance of the variables and to reveal the conditions leading to higher faradaic efficiency, production rate and product selectivity. It was found that Cu content smaller than 71% resulted high faradaic efficiencies depending on the amount of Sn, catholyte type, applied potential and pH of electrolyte. In this case, applied potential and Cu content were found to have the highest significance among all the input variables. On the other hand, the most generalizable combination of variables leading to high level of rate occurred when the Cu content being less than 13%, using a membrane other than Selemion AMV, employing a backing layer such as TGP-H-60 and keeping the applied potential between −1.5 and −2.6 V; for which the applied potential and CO2 flow rate were determined as the highest significant variables. Finally, the most generalizable path for the case of selectivity was obtained with Sn content higher than 15% and Cu content less than 52%, which leaded to formic acid production having the highest production rates. It was then concluded that, exploratory data analysis and decision trees can provide useful information to determine the conditions leading to higher CO2 − electroreduction performance that may guide the future studies in this area.