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A research team has identified critical factors influencing the electrochemical reduction of carbon dioxide (CO₂RR) using tin monoxide (SnO)-based electrocatalysts.

Their study provides a deeper understanding of how structural changes in SnO affect the production of valuable chemicals such as formic acid (HCOOH) and carbon monoxide (CO), both of which play significant roles in fuel production and industrial applications.

is published in the journal ACS Catalysis.

While Sn-based materials are widely recognized for their cost-effectiveness and non-toxic nature in COâ‚‚RR, existing studies have primarily focused on tin dioxide (SnO₂), which predominantly produces HCOOH.

Through large-scale data mining of experimental CO₂RR literature, the research team identified a significant trend: SnO-based catalysts demonstrate the ability to generate both HCOOH and CO in comparable amounts. However, despite this potential, the structure-activity relationships of SnO in CO₂RR remain underexplored.

To address this gap, the team employed a constant-potential method alongside surface coverage and reconstruction analyses to simulate CO₂RR intermediates under reaction conditions. Their findings reveal that the active surface of SnO undergoes electrochemistry-induced oxygen vacancy formation, a process that directs the distribution of C1 products.

Comparative simulations between pristine and reconstructed SnO surfaces further highlight how these structural changes influence electrocatalytic performance.

Hao Li, associate professor at Tohoku University's Advanced Institute for Materials Research (WPI-AIMR) and corresponding author of the paper, says, "This study provides new insights into how SnO-based catalysts can be optimized for CO₂ conversion. Understanding how surface modification influences product distribution is an essential step towards designing more efficient and selective electrocatalysts."

The research team intends to build on these results by tailoring Sn-based catalysts at the atomic level, with the goal of achieving precise synthesis of CO₂RR products. Future efforts will also integrate machine learning techniques to accelerate the prediction of effective electrocatalysts and optimize reaction conditions.