Computational Screening of Transition Metal-Nitrogen-Carbon Materials as Electrocatalysts for CO2 Reduction DOI
Megan C. Davis, Wilton J. M. Kort-Kamp, Edward F. Holby

et al.

Electrochimica Acta, Journal Year: 2024, Volume and Issue: 510, P. 145357 - 145357

Published: Nov. 14, 2024

Language: Английский

In-situ characterization technologies and theoretical calculations in carbon dioxide reduction: In-depth understanding of reaction mechanisms and rational design of electrocatalysts DOI
Rutao Wang, Xiaokun Yang, Jianpeng Zhang

et al.

Coordination Chemistry Reviews, Journal Year: 2025, Volume and Issue: 533, P. 216541 - 216541

Published: Feb. 28, 2025

Language: Английский

Citations

1

Exploring the Catalytic Performance of Oxygen-Coordinated Single-Atom Catalysts for Nitric Oxide Electroreduction DOI
Shiya Zhu, Yu Zhang, Wenbin Liu

et al.

The Journal of Physical Chemistry C, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 9, 2025

Language: Английский

Citations

0

Application of COF Materials in Carbon Dioxide Electrocatalytic Reduction DOI Open Access
Haiping Wang, Xin Wang, Yaping Jiang

et al.

The Chemical Record, Journal Year: 2025, Volume and Issue: unknown

Published: March 24, 2025

Abstract COFs have become the most attractive frontier research area in heterogeneous catalysis. Since geometry and electronic structure of are largely determined by their microenvironment, which turn determines performance electrocatalytic processes, precise integration atoms COF building blocks to achieve pre‐designed composition, components functions is core. This paper focuses on structural design, synthesis, mechanism application CO 2 RR (types RR, evaluation indicators relationship between performance). In addition, we also explore challenges faced corresponding solution strategies. Finally, highlighting prospects regulation, hope provide inspiration for further development applications.

Language: Английский

Citations

0

Applications of machine learning in surfaces and interfaces DOI Open Access
Shaofeng Xu, Jing‐Yuan Wu, Ying Guo

et al.

Chemical Physics Reviews, Journal Year: 2025, Volume and Issue: 6(1)

Published: March 1, 2025

Surfaces and interfaces play key roles in chemical material science. Understanding physical processes at complex surfaces is a challenging task. Machine learning provides powerful tool to help analyze accelerate simulations. This comprehensive review affords an overview of the applications machine study systems materials. We categorize into following broad categories: solid–solid interface, solid–liquid liquid–liquid surface solid, liquid, three-phase interfaces. High-throughput screening, combined first-principles calculations, force field accelerated molecular dynamics simulations are used rational design such as all-solid-state batteries, solar cells, heterogeneous catalysis. detailed information on for

Language: Английский

Citations

0

Oxygen Atom Migration in Ni2P/TiO2 Heterostructures Dynamically Regulates the Electrocatalytic CO2 Reduction Pathway DOI

Dailing Jia,

Jingying Wei,

Dongfen Hou

et al.

Inorganic Chemistry, Journal Year: 2025, Volume and Issue: unknown

Published: March 28, 2025

Transition metal phosphides (TMPs) are widely applied in electrocatalytic reactions, such as the hydrogen evolution reaction (HER), due to their excellent physicochemical properties. However, when utilized CO2 reduction severe limits activation of molecules. In this study, oxygen atoms were successfully migrated from TiO2 into Ni2P nanoparticles through a simple impregnation and low-temperature phosphidation process, constructing an O–Ni2P/TiO2 nanowire array electrode that modulates surface electronic structure, inhibits evolution, promotes activation. At potential −0.4 V (vs RHE), CH4 production rate reached 1.46 μmol·h–1·cm–2, with Faraday efficiency 11.8%, maintained long-term stability during 36-h process. situ infrared spectroscopy revealed CO* CH3* intermediates easily formed on material, which key directly related CH4. Further density functional theory (DFT) calculations indicated oxygen-doped has lower barrier for formation CHO*, thereby facilitating conversion

Language: Английский

Citations

0

A Pulsed Tandem Electrocatalysis Strategy for CO2 Reduction DOI
Hao Sun, Jing‐yao Liu

Journal of the American Chemical Society, Journal Year: 2025, Volume and Issue: unknown

Published: April 18, 2025

Electroreduction of CO2 to value-added C2 products remains hindered by sluggish C-C coupling kinetics and competing side reactions. Inspired the tandem catalytic mechanisms multienzyme systems, we designed a dual-site single-atom nanozyme (DSAN) comprising FeN4 FeO4 sites (FeN4-FeO4). Density functional theory (DFT) calculations under constant potential reveal that site functions as CO generator, while facilitates migration, coupling, subsequent product formation. To further optimize efficiency, introduced pulsed electrocatalysis strategy alternating between zero -0.7 V. This approach dynamically modulates active-site functions: at -0.70 V, adsorption *CH3CH2OH formation are facilitated, 0 migration enhanced due spin-state transitions during switching. Additionally, suppresses excessive hydrogenation key intermediates, thereby improving CH3CH2OH selectivity. These findings highlight synergistic integrating catalysis control, offering novel effective for CO2-to-C2 conversion using SAN catalysts.

Language: Английский

Citations

0

Identification of Topological Metal g-C2N with High Activity and Selectivity for Versatile Oxygen Electrocatalysis DOI
Rui Tan, Zehou Li, Zhe Xue

et al.

Langmuir, Journal Year: 2025, Volume and Issue: unknown

Published: May 7, 2025

Two-dimensional (2D) carbon nitride materials are emerging as ideal supports for single-atom catalysts (SACs) due to their excellent physicochemical stability, abundant active sites, and ample capacity metal loading. However, intrinsic semiconducting properties constrain electrical conductivity, thereby hindering charge transfer during catalytic processes. Herein, we propose a graphene-like 2D structure, g-C2N, derived from first-principles calculations theoretical analysis. This structure is identified topological metal, featuring symmetry-protected Dirac cone. Its topologically nontrivial nature evidenced by distinct edge states, nonzero Berry curvature, quantized Zak phase. Remarkably, g-C2N exhibits Fermi velocity exceeding that of graphene. Furthermore, the constructed Co@C2N2 highly selective catalyst hydrogen peroxide (H2O2) electrosynthesis, with low thermodynamic overpotential 0.08 V. Additionally, Co@C2N2-N developed through N-doping strategies demonstrates outstanding bifunctional 4e- OER/ORR activity overpotentials 0.27 0.32 V, respectively. These findings not only broaden scope but also offer foundational insights rational design oxygen electrocatalysis.

Language: Английский

Citations

0

Design and performance analysis of multi-enzyme activity-doped nanozymes assisted by machine learning DOI

Fuguo Ge,

Yonghui Gao,

Yujie Jiang

et al.

Colloids and Surfaces B Biointerfaces, Journal Year: 2024, Volume and Issue: 248, P. 114468 - 114468

Published: Dec. 20, 2024

Language: Английский

Citations

1

N-heterocyclic carbene coordinated single atom catalysts on C2N for enhanced nitrogen reduction DOI Open Access

Wenming Lu,

Dian Zheng,

Daifei Ye

et al.

Journal of Materials Informatics, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 28, 2024

Single-atom catalysts (SACs) with N-heterocyclic carbene (NHC) coordination provide an effective strategy for enhancing nitrogen reduction reaction (NRR) performance by modulating the electronic properties of metal active sites. In this work, we designed a novel NHC-coordinated SAC embedding transition metals (TM) into two-dimensional C2N-based nanomaterial (TM@C2N-NCM) and evaluated NRR catalytic using combination density functional theory machine learning. A multi-step screening identified eight high-performance (TM = Nb, Fe, Mn, W, V, Ta, Zr, Ti), Nb@C2N-NCM showing best (limiting potential -0.29 V). All demonstrated lower limiting values compared to their TM@graphene-NCM counterparts, revealing effectiveness C2N substrate in activity. Machine learning analysis achieved high predictive accuracy (coefficient determination 0.91; mean absolute error 0.19) final step protonation (S6), Mendeleev number (Nm), d-electron count (Nd) as key factors influencing performance. This study offers valuable insights rational design SACs highlights nanomaterials advancing electrocatalysts.

Language: Английский

Citations

1

Computational Screening of Transition Metal-Nitrogen-Carbon Materials as Electrocatalysts for CO2 Reduction DOI
Megan C. Davis, Wilton J. M. Kort-Kamp, Edward F. Holby

et al.

Electrochimica Acta, Journal Year: 2024, Volume and Issue: 510, P. 145357 - 145357

Published: Nov. 14, 2024

Language: Английский

Citations

0