Theoretical progress of MXenes as electrocatalysts for the hydrogen evolution reaction

Wan, Pifang; Tang, Qing

doi:10.1039/d3qm00783a

Cited by 9 publications

(2 citation statements)

References 121 publications

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“…[96] For the widely studied HER, the stronger capture capacity of proton for MXene may impede optimal catalyst designing according to the Sabatier principle, while a wide range of TM from 3d to 5d and a series of non-metal (X = C, [97,98] N, [99,100] B, [44,101] O, S, Se, Te [102,103] ) constructed TMX, TM 2 X, TM 3 X 2 , TM a X a-1 (a > 3) enlarge the screening categories largely, making potential optimal HER catalysts existed among them. [104,105] Considering the nearly unified crystal of the MXene materials, it is evident that ML algorithms should be employed to filtrate optimal HER catalyst among many candidates and build the 'structure-property' relationship between MXene materials and their HER performance. The Ordinary linear regression, tree ensemble method, kernel method, etc., could also be used for descriptor finding and novel electrocatalysts predicting, and the trend between adsorption energy and their corresponding overpotential also shows a volcano shape with strong correlation.…”

Section: D Electrocatalystsmentioning

confidence: 99%

Theoretical Calculation Assisted by Machine Learning Accelerate Optimal Electrocatalyst Finding for Hydrogen Evolution Reaction

Zhang,

Liu,

Wang

2024

ChemElectroChem

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Electrocatalytic hydrogen evolution reaction (HER) is a promising strategy to solve and mitigate the coming energy shortage and global environmental pollution. Searching for efficient electrocatalysts for HER remains challenging through traditional trial‐and‐error methods from numerous potential material candidates. Theoretical high throughput calculation assisted by machine learning is a possible method to screen excellent HER electrocatalysts effectively. This will pave the way for high‐efficiency and low‐price electrocatalyst findings. In this review, we comprehensively introduce the machine learning workflow and standard models for hydrogen reduction reactions. This mainly illustrates how machine learning is used in catalyst filtration and descriptor exploration. Subsequently, several applications, including surface electrocatalysts, two‐dimensional (2D) electrocatalysts, and single/dual atom electrocatalysts using machine learning in electrocatalytic HER, are highlighted and introduced. Finally, the corresponding challenge and perspective for machine learning in electrocatalytic hydrogen reduction reactions are concluded. We hope this critical review can provide a comprehensive understanding of machine learning for HER catalyst design and guide the future theoretical and experimental investigation of HER catalyst findings.

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Section: D Electrocatalystsmentioning

confidence: 99%

Theoretical Calculation Assisted by Machine Learning Accelerate Optimal Electrocatalyst Finding for Hydrogen Evolution Reaction

Zhang,

Liu,

Wang

2024

ChemElectroChem

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“…Up to this point, the Pt-group precious metals have been identified as the most efficient HER electrocatalysts, demonstrating negligible overpotential in acidic solutions. , Nevertheless, their scarcity and high cost pose significant limitations on their widespread commercial application. Consequently, the search for alternative, low-cost catalysts is an ongoing area of research, with numerous materials possessing a large surface area, high active sites, , and nonprecious transition metals doped − having been undertaken in this pursuit.…”

Section: Introductionmentioning

confidence: 99%

Dense and Uniform Integration of Cu-BDC Particles on a Nafion Film for a High-Performance Hydrogen-Producing Device

Xiao,

Wang,

et al. 2024

ACS Appl. Energy Mater.

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The incorporation of metal–organic frameworks onto conductive substrates demonstrates considerable potential in applications related to the hydrogen evolution reaction. In this work, we present a streamlined synthesis approach involving atomic layer deposition pretreatment to facilitate the controlled growth of Cu-BDC particles on a conductive Nafion film. This methodology preserves a substantial loading of Cu-BDC with a hierarchically porous structure, accompanied by a notable surface area of 547 m2 g–1. The composite is subsequently utilized as an electrocatalyst for hydrogen generation in an alkane electrolyte. The structural advances in the composite result in a superior electrochemical hydrogen production efficiency with a low overpotential of 84 mV and a low Tafel slope of 58 mV dec–1 in 1.0 M NaOH. Notably, the composite showed good cycling performance during the 18 h test with 2000 cycles. This research introduces a design of a catalyst electrode with promising implications for the hydrogen production industry.

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Review of Extrinsic Factors That Limit the Catalytic Performance of Transition Metal Dichalcogenides (TMDs) in Hydrogen Evolution Reactions (HER)

Muthu,

Khurshid,

Hofmann

et al. 2024

ChemElectroChem

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Transition metal dichalcogenides (TMDs) have garnered attention as potential catalysts for water splitting owing to their unique structures, diverse electronic properties, and composition from earth‐abundant elements. While certain TMD catalysts, notably MoS2, have shown promising activity for hydrogen evolution reactions (HER), achieving performance comparable to traditional platinum catalysts remains a challenge. While significant effort has been invested into understanding the effect of TMD's structural properties, such as defectiveness and crystalline phases, recent work has emphasized the role of extrinsic factors on HER. This review summarizes the current understanding of the impact of commonly overlooked electrocatalytic effects that exhibit an enhanced importance in TMD‐based HER. By combining recent advances in theoretical modeling and experimental work, we review the dominating effects of extrinsic factors including electronic resistance, interfacial barriers, surface roughness, oxidation, and valence impurities. Our work aims to provide insights into optimizing TMDs as highly efficient catalysts for HER, facilitating future advancements in hydrogen generation technology.

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Theoretical progress of MXenes as electrocatalysts for the hydrogen evolution reaction

Abstract: We reviewed recent theoretical progress in MXenes as HER electrocatalysts, covering structural and electronic properties, terminal group tunability, heteroatom doping, crucial activity descriptors, and advances in theoretical computational methods.

Cited by 9 publications

References 121 publications

Theoretical Calculation Assisted by Machine Learning Accelerate Optimal Electrocatalyst Finding for Hydrogen Evolution Reaction

Theoretical Calculation Assisted by Machine Learning Accelerate Optimal Electrocatalyst Finding for Hydrogen Evolution Reaction

Dense and Uniform Integration of Cu-BDC Particles on a Nafion Film for a High-Performance Hydrogen-Producing Device

Review of Extrinsic Factors That Limit the Catalytic Performance of Transition Metal Dichalcogenides (TMDs) in Hydrogen Evolution Reactions (HER)

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