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.

在这项工作中，数据库中有471个实验数据点，这些数据摘自34种关于电催化还原CO _2的出版物。成立了。首先，通过探索性数据分析，使用箱形图和晶须图对数据库进行了检查。然后，应用决策树分析来确定变量的重要性，并揭示导致法拉第效率，生产率和产品选择性更高的条件。发现小于71％的Cu含量导致高的法拉第效率，这取决于Sn的量，阴极电解质类型，施加的电势和电解质的pH。在这种情况下，发现在所有输入变量中施加的电位和Cu含量具有最高的显着性。另一方面，使用Selemion AMV以外的膜，当Cu含量低于13％时，会出现导致率较高的变量的最通用的组合。采用诸如TGP-H-60之类的背衬层，并将施加电势保持在-1.5至-2.6 V之间；为其施加的电势和CO_2个流速被确定为最高显着变量。最后，在Sn含量高于15％且Cu含量低于52％的情况下，获得了对于选择性而言最通用的途径，这使得甲酸的生产具有最高的生产率。然后得出的结论是，探索性数据分析和决策树可以为确定导致更高的CO _2-电还原性能的条件提供有用的信息，这可以指导该领域的未来研究。