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oai:figshare.com:article/33974252 2026-09-23T15:46:24Z category_1 category_4 category_146 category_915 category_21 category_873 category_734 category_931 portal_63 item_type_3 month_year_09_2026

Decoding CO₂ Adsorption Modes and CO Yield Variation on TM–N₄ Single-Atom Catalysts via Constant-Potential Computation and Machine-Learning Investigation Linguo Lu (11612068) Weibin Chen (1505503) Jingsong Huang (1263312) Ian Street (15104444) Bobby G. Sumpter (537235) Alexey Serov (522651) Robert L. Sacci (1550779) Gabriel M. Veith (368520) William E. Mustain (2002276) Ju Li (354128) Zhongfang Chen (1288152) Biophysics Biochemistry Space Science Physical Sciences not elsewhere classified Biotechnology Chemical Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified unusual σ back offers valuable guidance novel predictive parameter new active sites electron transfer rates critical electrode potential tm – n tm active site ></ sub >–< ></ sub >) ni – n applied electrode potential increasingly negative potentials chemisorption modes leads toward co formation co yield variation co yield performance co yield declines trans </ sub 4 </ sub >< sub >∞< 2 </ sub negative potentials applied potentials potential method potential computation chemisorption (< v </ u </ h </ c </ Transition metal-based TM–N₄ single-atom catalysts (SACs) are promising materials for electrochemical CO₂ reduction reactions (CO₂RR) toward CO formation. In this study, we employed the constant-potential method to investigate CO₂ adsorption and reduction on 10 TM–N₄ SACs (TM = Sc–Zn), with a particular focus on Ni–N₄. We first evaluated the CO₂ adsorption mode on the TM active site as a function of the applied electrode potential, <i>U</i>. Landau free energy analysis revealed a transition in CO₂ adsorption mode from physisorption (<i>D</i>_∞<i>h</i>–<i>D</i>_2<i>h</i>) to chemisorption (<i>C</i>_2<i>v</i>–<i>C</i>_2<i>v</i>) at a critical electrode potential, referred to as the “transorption” potential (<i>U</i>_trans). Walsh diagram and machine-learning analyses revealed significant π*-orbital splitting and an unusual σ back-donation governing <i>U</i>_trans. We then examined the CO₂RR processes by considering both adsorption modes of CO₂. The transition from CO₂ physisorption to chemisorption modes leads to a shift in the rate-limiting step from CO₂ hydrogenation to CO desorption, the emergence of new active sites for competing hydrogen evolution reactions, and a decrease in electron transfer rates at more negative potentials. Taken together, these observations align with the experimental CO yield profile under increasingly negative potentials, capturing key performance trends of rise, plateau, and decline. Notably, the calculated <i>U</i>_trans values are consistent with the applied potentials at which CO yield declines for five SACs for which experimental data are available (Mn, Fe, Ni, Cu, and Zn). Thus, this work establishes <i>U</i>_trans as a novel predictive parameter for CO yield performance and offers valuable guidance for the design of advanced SACs in future studies. 2026-09-23T00:00:00Z Dataset Dataset 10.1021/jacs.6c13568.s002 https://figshare.com/articles/dataset/Decoding_CO_sub_2_sub_Adsorption_Modes_and_CO_Yield_Variation_on_TM_N_sub_4_sub_Single-Atom_Catalysts_via_Constant-Potential_Computation_and_Machine-Learning_Investigation/33974252 CC BY-NC 4.0