Rational design of Pt-anchored single-atom alloy electrocatalysts for NO-to-NH3 conversion by density functional theory and machine learning

doi:10.1016/S1872-2067(24)60078-1

Abstract

Abstract:

Electrochemical NO reduction reaction (NORR) toward NH₃ synthesis emerges as a promising approach to eliminate NO pollution and generate high-value-added products simultaneously. Therefore, exploring suitable NORR electrocatalysts is of great importance. Here, we present a design principle to evaluate the activity of single-atom alloy catalysts (SAACs), whose excellent catalytic performance and well-defined bonding environments make them suitable candidates for studying structure-activity relationships. The machine learning (ML) algorithm is chosen to unveil the underlying physics and chemistry. The results indicate that the catalytic activity of SAACs is highly correlated with the local environment of the active center, that is, the atomic and electronic features. The coeffect of these features is quantitatively verified by adopting a data-driven method. The combination of density functional theory (DFT) and ML investigations not only provides an understanding of the complex NORR mechanisms but also offers a strategy to design highly efficient SAACs with specific active centers rationally.

Key words: NO reduction reaction, Ammonia synthesis, Single-atom alloy catalyst, Machine learning, Density functional theory

Jieyu Liu, Haiqiang Guo, Yulin Xiong, Xing Chen, Yifu Yu, Changhong Wang. Rational design of Pt-anchored single-atom alloy electrocatalysts for NO-to-NH₃ conversion by density functional theory and machine learning[J]. Chinese Journal of Catalysis, 2024, 62: 243-253.

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URL: https://www.cjcatal.com/EN/10.1016/S1872-2067(24)60078-1

https://www.cjcatal.com/EN/Y2024/V62/I7/243

Figures/Tables 5

Fig. 1. (a) Side view of the structural prototype of Pt/TM catalysts is shown on the left. All TM elements considered are shown in blue on the right. (b) Five-step screening method for selecting promising Pt/TM catalysts for NORR to NH3. (c) The stability of different Pt/TM SAACs, Eb represents the metal binding energy, Ec represents the metal cohesive energy. (d) The charge accumulation of Pt in Pt/TM SAACs and the electronegativity of TMs.

Fig. 2. (a) Different adsorption configurations of NO on the catalyst. (b) The adsorption free energy of NO, atomic H and NH3, respectively. (c) Differential charge density of Pt/Cu catalyst with an isosurface of 0.005 e ?-3. The charge accumulation and depletion are depicted by yellow and cyan, respectively. (d) Correspondence between the amount of NO charge transfer and the length of the N-O bond. (e) The partial density of states (PDOS) of Pt/Cu-total and NO-2p states before and after NO adsorption on Pt/Cu. The Fermi level is set to be 0 eV. (f) Projected crystal orbital Hamilton population (-pCOHP) for N-O interaction of free NO molecule and NO on Pt/Cu. (g) Linear relationship between NO charge transfer and integrated COHP (ICOHP). (h-j) Band structures of Pt/Cu, Ni, and Zn. The grey lines show the total contribution of the system. Orange, green, and blue points represent the contribution of NO’s 2p orbitals, TMs’ s and d orbitals, respectively. The point size denotes the contribution ratio. Black dash lines denote the Fermi level.

Fig. 3. (a) Schematic diagram of 6 NORR reaction pathways. (b) Heat map of reaction Gibbs free energy for each elementary step of the optimal path of catalysts.

Fig. 4. (a) The NORR limiting potentials of catalysts. (b) Reaction Gibbs free energy diagrams of Cu and Pt/Cu catalysts. Their limiting potentials are 0.03 and -0.06 V, respectively. (c) Comparison between HER limiting potentials and NORR limiting potentials of catalysts.

Fig. 5. (a) Schematic of input data acquisition and ML progress. (b) Heat map inferred by Pearson’s correlation coefficient (p). (c) Comparison between DFT and ML predicted limiting potential UL. (d) Feature importance analysis. (e) Comparison between DFT and SISSO predicted limiting potential UL.

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Rational design of Pt-anchored single-atom alloy electrocatalysts for NO-to-NH₃ conversion by density functional theory and machine learning

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