Litcius/Paper detail

Screening Nitrogen-Coordinated Single Atom Catalysts on Armchair Carbon Nanotubes for Enhanced Electrochemical CO<sub>2</sub> Reduction to C<sub>1</sub> Products

Wuyang Lin, Devis Di Tommaso

2025ACS Catalysis11 citationsDOIOpen Access PDF

Abstract

High Resolution Image Download MS PowerPoint Slide Single atom catalysts (SACs) are widely used in electrocatalytic reactions due to their superior stability and selectivity. The electrochemical reduction of carbon dioxide (eCO 2 R) has garnered significant attention in recent years due to the global warming crisis and its potential as a route for the utilization of CO 2 . However, eCO 2 R is still limited by low activity, poor selectivity, and an elusive reduction mechanism. In this work, the reduction of CO 2 to C 1 products catalyzed by nitrogen-coordinated SACs supported on armchair carbon nanotubes (SAC–CNTs) is investigated using a protocol combining density functional theory and machine learning simulations to predict the thermodynamic and electrochemical stability, as well as the catalytic performance of SAC–CNTs toward eCO 2 R. Our analysis also included the effects of surface curvature and axial strain of the CNTs on catalytic performance. The results indicate that Cr–N 4 –CNT and Co–N 4 –CNT exhibit enhanced selectivity toward CH 4 and CH 3 OH, while Zn–N 4 –CNT is the best candidate for formic acid formation. Both surface curvature and axial strain influence the relative position of the metal atom on the CNT surface, thereby modulating the catalytic activity.

Topics & Concepts

CatalysisCarbon nanotubeMaterials scienceElectrochemistryElectrochemical reduction of carbon dioxideFormic acidElectrocatalystDensity functional theorySelectivityAtom (system on chip)Faraday efficiencyChemical engineeringCarbon fibersNanotechnologyMetalInorganic chemistryChemical physicsNanotubeTransition metalReduction (mathematics)Noble metalCarbon dioxideSelective chemistry of single-walled nanotubesPhotochemistryCO2 Reduction Techniques and CatalystsElectrocatalysts for Energy ConversionMachine Learning in Materials Science