Computational Screening of Single-Metal-Atom Embedded Graphene-Based Electrocatalysts Stabilized by Heteroatoms

Cho, Ara; Park, Byoung Joon; Han, Jeong Woo

doi:10.3389/fchem.2022.873609

Cited by 13 publications

(6 citation statements)

References 33 publications

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“…We modeled the active structures of different transition metals as MN 2 O 2 (O) (M=Mn, Fe, Co, and Ni) based on our FT‐EXAFS fitting results (Table S5, Supporting Information) of all MNOC SAC electrocatalysts, with metal coordinating with two nitrogen and two oxygen atoms in a graphene molecule embedded in epoxy close to the active metal center (see Figure a). The metal binding energies of the MN 2 O 2 (O) active structure models were determined to evaluate thermodynamic stability [ 7b,37 ] (Figure S29, Supporting Information). The metal binding energy values between −3.6 and −6.31 eV show that MN 2 O 2 (O) structures are stable on the carbon support.…”

Section: Resultsmentioning

confidence: 99%

Mimicking Metalloenzyme Microenvironments in the Transition Metal‐Single Atom Catalysts for Electrochemical Hydrogen Peroxide Synthesis in an Acidic Medium

et al. 2023

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Electrochemical reduction of oxygen into hydrogen peroxide in an acidic medium offers an energy‐efficient and green H2O2 synthesis as an alternative to the energy‐intensive anthraquinone process. Unfortunately, high overpotential, low production rates, and fierce competition from traditional four‐electron reduction limit it. In this study, a metalloenzyme‐like active structure is mimicked in carbon‐based single‐atom electrocatalysts for oxygen reduction to H2O2. Using a carbonization strategy, the primary electronic structure of the metal center with nitrogen and oxygen coordination is modulated, followed by epoxy oxygen functionalities close to the metal active sites. In an acidic medium, CoNOC active structures proceed with greater than 98% H2O2 selectivity (2e−/2H+) rather than CoNC active sites that are selective to H2O (4e−/4H+). Among all MNOC (M = Fe, Co, Mn, and Ni) single‐atom electrocatalysts, the CoNOC is the most selective (> 98%) for H2O2 production, with a mass activity of 10 A g−1 at 0.60 V vs. RHE. X‐ray absorption spectroscopy is used to identify the formation of unsymmetrical MNOC active structures. Experimental results are also compared to density functional theory calculations, which revealed that the structure‐activity relationship of the epoxy‐surrounded CoNOC active structure reaches optimum (ΔG*OOH) binding energies for high selectivity.

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Section: Resultsmentioning

confidence: 99%

Mimicking Metalloenzyme Microenvironments in the Transition Metal‐Single Atom Catalysts for Electrochemical Hydrogen Peroxide Synthesis in an Acidic Medium

et al. 2023

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“…Commonly, the difference in adsorption energy between *OH and *OOH remains constant with a value of 3.2 � 0.2 eV, 106 and ΔG OH is effective in predicting reaction free energies and overpotentials for carbon-supported transition metal single-atom structures, such as single-atom active sites supported on heteroatomcoordinated graphene (MN 4 and MYN 3 ) (Figure 3A), carbon nanocone (CNC) (Figure 3B), graphdiyne, C 2 N, C 3 N 4 , and phthalocyanine. 107,108,110,111 One remarkable discovery is that the carbon nanocone (CNC) embedded with MN 3 groups reported by Deng et al achieved a spectrum of *OH adsorption energy modulation, providing a great possibility in climbing the apex of catalytic activity (Figure 3B). 108 This suggests that carbon nanocone brings new opportunities in exploiting electrocatalysts.…”

Section: Adsorption Energy-based Descriptorsmentioning

confidence: 99%

“…Therefore, the adsorption energy of *OH (Δ G OH ) is a common activity descriptor for H 2 O generation. Commonly, the difference in adsorption energy between *OH and *OOH remains constant with a value of 3.2 ± 0.2 eV, 106 and Δ G OH is effective in predicting reaction free energies and overpotentials for carbon‐supported transition metal single‐atom structures, such as single‐atom active sites supported on heteroatom‐coordinated graphene (MN 4 and MYN 3 ) (Figure 3A), carbon nanocone (CNC) (Figure 3B), graphdiyne, C 2 N, C 3 N 4 , and phthalocyanine 107,108,110,111 . One remarkable discovery is that the carbon nanocone (CNC) embedded with MN 3 groups reported by Deng et al.…”

Section: Reactivity Descriptors For Orrmentioning

confidence: 99%

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Catalytic reactivity descriptors of metal‐nitrogen‐doped carbon catalysts for electrocatalysis

Liu,

Li,

Arbiol

et al. 2023

EcoEnergy

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Metal‐nitrogen‐doped carbon material have sparked enormous attentions as they show excellent electrocatalytic performance and provide a prototype for mechanistic understandings of electrocatalytic reactions. Researchers spare no effort to find catalytic reactivity “descriptor”, which is correlated with catalytical properties and could be utilized for guiding the rational design of high‐performance catalysts. In recent years, benefited from the development of computational technology, theoretical calculation came into being as a powerful tool to understand catalytic mechanisms from an atomic level as well as to accelerate the process of finding a catalytic reactivity descriptor and promoting the development of effective catalysts. In the present review, we provide the latest theoretical research toward energetic and electronic descriptors for metal‐nitrogen‐doped carbon (M‐N‐C) materials, which have shown excellent electrocatalytic performance and provide a prototype for the mechanistic understanding of electrocatalytic reactions. This review uses density functional theory calculation and the most advanced machine learning method to describe the exploration of four kinds of electrocatalytic reaction descriptors, namely oxygen reduction reaction, carbon dioxide reduction reaction, hydrogen evolution reaction, and nitrogen reduction reaction. The aim of this review is to inspire the future design of high‐efficiency M‐N‐C catalysts by providing in‐depth insights into the electrocatalytic activity of these materials.

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“…Inaccurate predictions, especially when the sign of the energy difference is wrong, can lead to opposite trends in reaction activity and ultimately the failure of the screening process 30,41 . The challenge of determining the correct sign of the energy difference is heightened when comparing similar adsorption energies.…”

Section: Correct Sign Prediction In Energy Differencementioning

confidence: 99%

Beyond Independent Error Assumptions in Large GNN Atomistic Models

Janghoon¹,

Tian²,

Kitchin³

et al. 2023

Preprint

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The practical applications of determining the relative difference in adsorption energies are extensive, such as identifying optimal catalysts, calculating reaction energies, and determining the lowest adsorption energy on a catalytic surface. Although Density Functional Theory (DFT) can effectively calculate relative values through systematic error cancellation, the accuracy of Graph Neural Networks (GNNs) in this regard remains uncertain. To investigate this issue, we analyzed approximately 483 million pairs of energy differences predicted by DFT and GNNs using the Open Catalyst 2020 -Dense dataset. Our analysis revealed that GNNs exhibit a correlated error that can be reduced through subtraction, thereby challenging the naive independent error assumption in GNN predictions and leading to more precise energy difference predictions. To assess the magnitude of error cancellation in chemically similar pairs, we introduced a new metric, the subgroup error cancellation ratio (SECR). Our findings suggest that state-of-the-art GNN models can achieve error reduction up to 77% in these subgroups, comparable to the level of error cancellation observed with DFT. This significant error cancellation allows GNNs to achieve higher accuracy than individual adsorption energy predictions, which can otherwise suffer from amplified error due to random error propagation.

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Computational Screening of Single-Metal-Atom Embedded Graphene-Based Electrocatalysts Stabilized by Heteroatoms

Cited by 13 publications

References 33 publications

Mimicking Metalloenzyme Microenvironments in the Transition Metal‐Single Atom Catalysts for Electrochemical Hydrogen Peroxide Synthesis in an Acidic Medium

Mimicking Metalloenzyme Microenvironments in the Transition Metal‐Single Atom Catalysts for Electrochemical Hydrogen Peroxide Synthesis in an Acidic Medium

Catalytic reactivity descriptors of metal‐nitrogen‐doped carbon catalysts for electrocatalysis

Beyond Independent Error Assumptions in Large GNN Atomistic Models

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