Die Arbeitsgemeinschaft Psychoanalytische Pädagogik

Kraushofer, Tanja; Neudecker, Barbara

doi:10.1007/978-3-658-38751-8_5

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…To address these challenges and accelerate SAC design, quantum chemistry methods, especially at the first-principles density functional theory (DFT) level, 16,17 have been increasingly applied as powerful toolkits to enable a more systematic approach to predict and understand the catalytic behaviors of catalysts. 18,19 These techniques empower researchers to explore a wide range of potential catalyst configurations and identify promising candidates for experimental synthesis, reducing the number of trial-and-error experiments and streamlining the design process. For example, Deng et al adopted DFT to develop Co-based SACs on defective boron nitride (Co/BN) for efficient ORR.…”

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confidence: 99%

Fine-Tuning Dual Single-Atom Metal Sites on Graphene toward Enhanced Oxygen Reduction Reaction Activity

Xie,

Wang

2023

J. Phys. Chem. Lett.

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The oxygen reduction reaction (ORR) remains at the forefront of research in diverse energy and sustainability domains. While graphene-supported single-atom catalysts (SACs) have garnered attention for optimizing ORR efficiency, tailoring the interactions between adjacent single-atom sites presents intricate challenges. In this study, we leveraged density functional theory (DFT) calculations and cutting-edge machine learning (ML) techniques to explore 144 graphene-supported SACs, featuring interacting M1–N4 and M2–N4 moieties (M1, M2 = Mn, Fe, Co, Ni, Cu, Ru, Rh, Pd, Ag Ir, Pt, Au), denoted as M1–M2. By tailoring these interactions, we discovered 13 exceptional SACs outperforming the benchmark catalyst Fe(OH)–N4, including the best-performing Fe–Pd and several non-noble-metal SACs like Fe–Ag, Ag–Cu, and Ag–Ag. Venturing further, our ML models effectively capture the correlation between single-atom metal properties and overpotential, offering tools for rational electrocatalyst design. Our study illuminates the path to efficient SAC-catalyzed ORR, fostering a sustainable, energy-efficient future.

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confidence: 99%

Fine-Tuning Dual Single-Atom Metal Sites on Graphene toward Enhanced Oxygen Reduction Reaction Activity

Xie,

Wang

2023

J. Phys. Chem. Lett.

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show abstract

“…To address these challenges and accelerate SAC design, quantum chemistry methods, especially at the first-principles density functional theory (DFT) level, 17 have been increasingly applied as powerful toolkits to enable a more systematic approach to predict and understand the catalytic behaviors of catalysts. 18,19 These techniques empower researchers to explore a wide range of potential catalyst configurations and identify promising candidates for experimental synthesis, reducing the number of trial-and-error experiments and streamlining the design process. For example, Deng et al adopted DFT to develop Co-based SACs on defective boron nitride (Co/BN) for efficient ORR.…”

mentioning

confidence: 99%

Computational and AI-Assisted Design of Dual-Metal Single-Atom Catalyst for Oxygen Reduction Reaction

Xie,

Wang

2023

Preprint

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The electrocatalytic oxygen reduction reaction (ORR) plays a crucial role in numerous energy and sustainability systems, such as fuel cells, metal-air batteries, and water electrolysers. It holds significant potential for renewable energy generation, transportation, and storage, heralding a cleaner and more sustainable future. Recent trends have shown increased use of single-atom catalysts (SACs), particularly metal-N4 moieties grown on graphene-based 2D materials, for enhancing ORR efficiency. However, the rational design of SAC for high-performance ORR faces challenges due to unclear structure-property relationships and the limits of conventional experimental trial-and-error approaches. In this study, we harnessed the power of the density functional theory (DFT) calculations, combined with cutting-edge machine learning (ML) techniques, to explore 144 SACs featuring dual interacting M1-N4 and M2-N4 moieties (M1, M2 = Mn, Fe, Co, Ni, Cu, Ru, Rh, Pd, Ag Ir, Pt, Au), denoted as M1-M2, grown on graphene. Of all the catalysts we examined, emerged as the top performer, achieving an impressive overpotential of 0.980 V in alkaline conditions — outperforming most previously reported SACs. Even more striking, 25 of the evaluated SACs surpassed the renowned Fe-N4 SAC in catalytic efficiency, including more economically viable alternatives like Fe-Ag. Venturing further into innovation, we developed three ML models that accurately predict the overpotentials of various M1-M2 SACs, showing their strong ability to capture the relationship between single-atom metal site properties and overpotential. These models provide useful navigation toolkits for the rational design of effective electrocatalysts. Our study sheds light on the path toward achieving efficient SAC-catalyzed ORR, contributing to a more sustainable and energy-efficient future.

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Die Arbeitsgemeinschaft Psychoanalytische Pädagogik

Cited by 2 publications

References 7 publications

Fine-Tuning Dual Single-Atom Metal Sites on Graphene toward Enhanced Oxygen Reduction Reaction Activity

Fine-Tuning Dual Single-Atom Metal Sites on Graphene toward Enhanced Oxygen Reduction Reaction Activity

Computational and AI-Assisted Design of Dual-Metal Single-Atom Catalyst for Oxygen Reduction Reaction

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