@article{Song2026, 
author = {Xiaofei Song and Xinyu Sui and Baowen He and Xiaoxue Sang and Chi Ding and Yangyang Song and Zean Xie and Yu Ren and Zhen Zhao},
title = {Validation of nitrate adsorption energy as an activity descriptor for nitrate electroreduction on single-atom catalysts supported by chiral carbon nanotubes: DFT calculations and machine learning},
year = {2026},
journal = {Nano Research},
keywords = {NO3- electroreduction, single-atom catalysts, activity descriptor, Density functional theory, N-doped nanotube},
url = {https://www.sciopen.com/article/10.26599/NR.2026.94909085},
doi = {10.26599/NR.2026.94909085},
abstract = {Single-atom catalysts have attracted extensive attention for the electrochemical nitrate reduction reaction (NO3RR). However, effective catalyst design principles, particularly transferable activity descriptors across different structures, remain limited. Based on single-atom systems supported on chiral carbon nanotubes (CNTs), a total of 71 SAC models were constructed, comprising different metal centers, coordination environments, and CNT diameters. After stability, nitrate-adsorption, and selectivity screening, 45 systems were selected for complete free-energy pathway calculations. Our results show that V-C2N2/CNT and Os-C2N2/CNT exhibit favorable catalytic performance with limiting potentials of -0.36 and -0.37 V. A consistent volcano-type relationship was identified between catalytic activity and the adsorption energy of NO3-. This relationship remains consistent across different doping configurations and CNT sizes. The adsorption energy of NO3- exhibits a linear relationship with key intermediate species, providing a thermodynamic rationale for using nitrate adsorption energy as an activity descriptor. Machine-learning analysis identifies the valence-electron count and local TM–N coordination as influential features for predicting ΔG*NO3 within the present dataset. These findings provide useful insights into the design of chiral-CNT-supported NO3RR catalysts.}
}