Modification of metals and ligands in two-dimensional conjugated metal-organic frameworks for CO2 electroreduction: A combined density functional theory and machine learning study

被引:3
|
作者
Xing, Guanru [1 ]
Liu, Shize [2 ]
Sun, Guang-Yan [3 ]
Liu, Jing-yao [1 ]
机构
[1] Jilin Univ, Inst Theoret Chem, Coll Chem, Lab Theoret & Computat Chem, Changchun 130023, Peoples R China
[2] Inner Mongolia Univ Technol, Sch Mat Sci & Engn, Hohhot 010051, Peoples R China
[3] Yanbian Univ, Fac Sci, Dept Chem, Jilin 133002, Peoples R China
关键词
2D conjugated metal-organic frameworks; Density functional theory; Machine learning; CO2 reduction reaction; TOTAL-ENERGY CALCULATIONS; CARBON-DIOXIDE; ELECTROCHEMICAL REDUCTION; CONVERSION; CATALYSTS; APPROXIMATION; FUNDAMENTALS; EVOLUTION;
D O I
10.1016/j.jcis.2024.08.069
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
Electrochemical carbon dioxide reduction reaction (CO2RR) 2 RR) is a promising technology to establish an artificial carbon cycle. Two-dimensional conjugated metal-organic frameworks (2D c-MOFs) with high electrical conductivity have great potential as catalysts. Herein, we designed a range of 2D c-MOFs with different transition metal atoms and organic ligands, TMNxO4-x-HDQ x O 4-x-HDQ (TM = Cr Cu, Mo, Ru Ag, W Au; x = 0, 2, 4; HDQ = hexadipyrazinoquinoxaline), and systematically studied their catalytic performance using density functional theory (DFT). Calculation results indicated that all of TMNxO4-x-HDQ x O 4-x-HDQ structures possess good thermodynamic and electrochemical stability. Notably, among the examined 37 MOFs, 6 catalysts outperformed the Cu(2 1 1) surface in terms of catalytic activity and product selectivity. Specifically, NiN4-HDQ 4-HDQ emerged as an exceptional electrocatalyst for CO production in CO2RR, 2 RR, yielding a remarkable low limiting potential (UL) U L ) of-0.04 V. CuN4- 4- HDQ, NiN2O2-HDQ, 2 O 2-HDQ, and PtN2O2-HDQ 2 O 2-HDQ also exhibited high activity for HCOOH production, with U L values of-0.27,-0.29, and-0.27 V, respectively, while MnN4-HDQ, 4-HDQ, and NiO4-HDQ 4-HDQ mainly produced CH4 4 with U L values of-0.58 and-0.24 V, respectively. Furthermore, these 6 catalysts efficiently suppressed the competitive hydrogen evolution reaction. Machine learning (ML) analysis revealed that the key intrinsic factors influencing CO2RR 2 RR performance of these 2D c-MOFs include electron affinity (EA), E A ), electronegativity (chi), the first ionization energy (Ie), I e ), p-band center of the coordinated N/O atom (epsilon p), epsilon p ), the radius of metal atom (r), r ), and d-band center (epsilon d). epsilon d ). Our findings may provide valuable insights for the exploration of highly active and selective CO2RR 2 RR electrocatalysts.
引用
收藏
页码:111 / 119
页数:9
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