Dual Doping in Precious Metal Oxides: Accelerating Acidic Oxygen Evolution Reaction

International Journal of Molecular Sciences, Journal Year: 2025, Volume and Issue: 26(4), P. 1582 - 1582

Published: Feb. 13, 2025

Developing a highly active and stable catalyst for acidic oxygen evolution reactions (OERs), the key half-reaction proton exchange membrane water electrolysis, has been one of most cutting-edge topics in electrocatalysis. A dual-doping strategy optimizes electronic environment, modifies coordination generates vacancies, introduces strain effects through synergistic effect two elements to achieve high catalytic performance. In this review, we summarize progress dual doping RuO2 or IrO2 OERs. The three main mechanisms OERs are dicussed firstly, followed by detailed examination development history catalysts, from experimentally driven systems machine learning (ML) theoretical screening systems. Lastly, provide summary remaining challenges future prospects, offering valuable insights into

Language: Английский

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AI in single-atom catalysts: a review of design and applications

Journal of Materials Informatics, Journal Year: 2025, Volume and Issue: 5(1)

Published: Feb. 12, 2025

Single-atom catalysts (SACs) have emerged as a research frontier in catalytic materials, distinguished by their unique atom-level dispersion, which significantly enhances activity, selectivity, and stability. SACs demonstrate substantial promise electrocatalysis applications, such fuel cells, CO2 reduction, hydrogen production, due to ability maximize utilization of active sites. However, the development efficient stable involves intricate design screening processes. In this work, artificial intelligence (AI), particularly machine learning (ML) neural networks (NNs), offers powerful tools for accelerating discovery optimization SACs. This review systematically discusses application AI technologies through four key stages: (1) Density functional theory (DFT) ab initio molecular dynamics (AIMD) simulations: DFT AIMD are used investigate mechanisms, with high-throughput applications expanding accessible datasets; (2) Regression models: ML regression models identify features that influence performance, streamlining selection promising materials; (3) NNs: NNs expedite known structural models, facilitating rapid assessment potential; (4) Generative adversarial (GANs): GANs enable prediction novel high-performance tailored specific requirements. work provides comprehensive overview current status insights recommendations future advancements field.

Language: Английский

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Advanced carbon as emerging energy materials in lithium batteries: A theoretical perspective

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

Published: Jan. 14, 2025

Abstract Lithium batteries are becoming increasingly vital thanks to electric vehicles and large‐scale energy storage. Carbon materials have been applied in battery cathode, anode, electrolyte, separator enhance the electrochemical performance of rechargeable lithium batteries. Their functions cover storage, catalysis, electrode protection, charge conduction, so on. To rationally implement carbon materials, their properties interactions with other probed by theoretical models, namely density functional theory molecular dynamics. This review summarizes use models guide employment advanced batteries, providing critical information difficult or impossible obtain from experiments, including lithiophilicity, barriers, coordination structures, species distribution at interfaces. under discussion include zero‐dimensional fullerenes capsules, one‐dimensional nanotubes nanoribbons, two‐dimensional graphene, three‐dimensional graphite amorphous carbon, as well derivatives. electronic conductivities explored, followed applications cathode anode performance. While role is emphasized, experimental data also touched upon clarify background show effectiveness strategies. Evidently, prove promising achieving superior density, rate performance, cycle life, especially when informed endeavors. image

Language: Английский

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Machine Learning as a “Catalyst” for Advancements in Carbon Nanotube Research

Nanomaterials, Journal Year: 2024, Volume and Issue: 14(21), P. 1688 - 1688

Published: Oct. 22, 2024

The synthesis, characterization, and application of carbon nanotubes (CNTs) have long posed significant challenges due to the inherent multiple complexity nature involved in their production, processing, analysis. Recent advancements machine learning (ML) provided researchers with novel powerful tools address these challenges. This review explores role ML field CNT research, focusing on how has enhanced research by (1) revolutionizing synthesis through optimization complex multivariable systems, enabling autonomous reducing reliance conventional trial-and-error approaches; (2) improving accuracy efficiency characterizations; (3) accelerating development applications across several fields such as electronics, composites, biomedical fields. concludes offering perspectives future potential integrating further into highlighting its driving forward.

Language: Английский

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Hydrogen encapsulation properties of heteroatoms doped fullerenes: A DFT study for potential hydrogen storage application

Diamond and Related Materials, Journal Year: 2025, Volume and Issue: unknown, P. 111994 - 111994

Published: Jan. 1, 2025

Language: Английский

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Machine learning assisted Co3O4/NiO popsicle sticks-infused electrospun nanofibers for efficient oxygen evolution reaction

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 28, 2025

Abstract Wide range of noble metal free bimetallic and trimetallic based electrocatalysts have been synthesized to develop efficient oxygen evolution reaction (OER) systems to-date, however, determine which part plays a significant role in controlling OER efficacy remains very challenging. To address this issue, herein we employed machine learning (ML) for the first time element, thus leading development an optimized electrocatalyst. Briefly, designed novel, simple ML sustainable electrocatalyst on Co 3 O 4 /NiO popsicle sticks (CNPS) infused polyaniline/cellulose acetate (a biopolymer) (PNCA) electrospun nanofibers supported nickel foam (NF). CNPS PNCA (CNPS@PNCA) electrode offers maximum homogenous exposition active sites shows high activity by exhibiting low onset potential (1.41 V vs. RHE), overpotential (237 mV at 10 mA cm −2 ) Tafel slope 62.1 dec −1 . Additionally, it better stability more than 100 h is consistent with reported literature.

Language: Английский

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Parametric Optimization of Transition Metal-Based Nanocomposite Electrocatalysts for Oxygen Evolution Reaction in Alkaline Media

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

Published: April 11, 2025

Language: Английский

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First-principles calculations insight into non-noble-metal bifunctional electrocatalysts for zinc–air batteries

Applied Energy, Journal Year: 2025, Volume and Issue: 391, P. 125925 - 125925

Published: April 13, 2025

Language: Английский

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Engineering the regulation strategy of active sites to explore the intrinsic mechanism over single‑atom catalysts in electrocatalysis

Journal of Colloid and Interface Science, Journal Year: 2025, Volume and Issue: 693, P. 137595 - 137595

Published: April 14, 2025

Language: Английский

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Advancing electrocatalyst discovery through the lens of data science: State of the art and perspectives☆

Journal of Catalysis, Journal Year: 2025, Volume and Issue: unknown, P. 116162 - 116162

Published: April 1, 2025

Language: Английский

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