Thermochemical analysis of Mo-C-H system for synthesis of molybdenum carbides

Nayak, Shanti Kiran; Benavidez, Angelica; Matanović, Ivana; Garzón, Fernando H.

doi:10.1016/j.tca.2019.03.033

Cited by 4 publications

(2 citation statements)

References 21 publications

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“…Previous theoretical studies of α-MoC 1– x usually used the stoichiometric α-MoC (i.e., x = 0) model to study electronic structure, ,, thermodynamic stability , as well as catalytic properties. ,,− However, it is well established that experimentally prepared samples are nonstoichiometric with abundant carbon vacancies which were reported to tune the electronic structure and catalytic performance, − and therefore the stoichiometric α-MoC model is likely to be insufficient to represent α-MoC 1– x under experimental conditions. Typically, carbide carbon atoms on the surfaces of TMCs do not form an ordered structure and vary under different carbon chemical potentials .…”

Section: Introductionmentioning

confidence: 99%

Revealing Carbon Vacancy Distribution on α-MoC_1–xSurfaces by Machine-Learning Force-Field-Aided Cluster Expansion Approach

Xie

Jiang

2023

J. Phys. Chem. C

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α-MoC1–x has recently attracted extensive attention in heterogeneous catalysis for its unique catalytic properties. As a nonstoichiometric material, the performance of α-MoC1–x is closely related to its intrinsic carbon vacancy, but the knowledge regarding the structural nature associated with the vacancy is still lacking. In this work, we perform a cluster expansion (CE) study to reveal the vacancy distribution on α-MoC1–x (001) and (111) surface. Considering that a large number of symmetrically distinct clusters are needed to build accurate CE models for multicomponent surface systems, we adopt the strategy of utilizing the machine-learning force field to efficiently generate thousands of training data. With the effective cluster interactions on the α-MoC1–x (001) and (111) surfaces, Monte Carlo simulations are conducted to model the surface vacancy distribution behaviors. The surface-bulk difference as well as the surface effects on configurational properties are revealed. It is found that the carbon atom tends to segregate to the topmost layer of the α-MoC1–x (001) surface and the vacancy tends to segregate to the topmost layer of the α-MoC1–x (111) surface. We also predict the structures of the topmost layer of (001) and (111) surface under the simulated reaction condition, which can provide important insights into catalyst modeling and design for α-MoC1–x -related catalytic systems. The scheme to build surface CE models developed in this work can be generally used in theoretical modeling of heterogeneous catalytic surfaces with configurational disorder.

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Section: Introductionmentioning

confidence: 99%

Revealing Carbon Vacancy Distribution on α-MoC_1–xSurfaces by Machine-Learning Force-Field-Aided Cluster Expansion Approach

Xie

Jiang

2023

J. Phys. Chem. C

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“…Thermochemistry plays a key role in determining durability of the catalysts and catalytic supports and is also crucial in rational design of synthesis conditions that lead to materials with advanced properties. 15,16 Unwanted instability of the surfaces is particularly troublesome as the surface properties of the materials usually dictate the catalytic behavior. In this work, we use the thermochemical data available in the HSC Chemistry software 17 to study the stability regions of several transition metal nitrides in aqueous media as a function of cell potential, pH, and temperature.…”

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confidence: 99%

Assessing Stability of Transition Metal Nitrides in Aqueous Environments: The Case of Molybdenum, Iron, Vanadium and Nickel Nitride

Matanović

Garzón

2020

J. Electrochem. Soc.

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Analysis of the MoN, Mo2N, VN, Fe2N, Fe4N, Ni3N stability in the aqueous media was performed based on the thermodynamic data available in HSC Chemistry software. Our analysis show that among all the studied nitrides, cubic Mo2N is the most resistant while cubic VN is the least resistant to oxidation and decomposition. The change in temperature up to 100 °C does not significantly influence the formation potentials of transition metal oxides from nitrides; however, pH significantly affects the stability of the studied transition metal nitrides. In the case of Mo2N, MoN, and VN changing pH from acidic to neutral and alkaline decreases the stability of the nitrides and the oxide formation potentials shift to more negative values. Based on thermodynamics, orthorhombic Fe2N should be stable only in alkaline conditions, while cubic Fe4N and hexagonal Ni3N should be the most stable at pH ∼ 7 and pH ∼ 9. Changing pH from these values decreases the stability of both Fe4N and Ni3N and the formation of oxy-, hydroxy- species becomes thermodynamically more favorable. The limited stability of the metal nitrides places severe constraints for their use in aqueous electrocatalytic applications.

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Development of an Experimentally Derived Model for Molybdenum Carbide (Mo2C) Synthesis in a Fluidized-Bed Reactor

Chorney

Downey

Sudhakar

2023

The Minerals, Metals &Amp; Materials Series

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Thermochemical analysis of Mo-C-H system for synthesis of molybdenum carbides

Cited by 4 publications

References 21 publications

Revealing Carbon Vacancy Distribution on α-MoC_1–xSurfaces by Machine-Learning Force-Field-Aided Cluster Expansion Approach

Revealing Carbon Vacancy Distribution on α-MoC_1–xSurfaces by Machine-Learning Force-Field-Aided Cluster Expansion Approach

Assessing Stability of Transition Metal Nitrides in Aqueous Environments: The Case of Molybdenum, Iron, Vanadium and Nickel Nitride

Development of an Experimentally Derived Model for Molybdenum Carbide (Mo2C) Synthesis in a Fluidized-Bed Reactor

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Thermochemical analysis of Mo-C-H system for synthesis of molybdenum carbides

Cited by 4 publications

References 21 publications

Revealing Carbon Vacancy Distribution on α-MoC1–xSurfaces by Machine-Learning Force-Field-Aided Cluster Expansion Approach

Revealing Carbon Vacancy Distribution on α-MoC1–xSurfaces by Machine-Learning Force-Field-Aided Cluster Expansion Approach

Assessing Stability of Transition Metal Nitrides in Aqueous Environments: The Case of Molybdenum, Iron, Vanadium and Nickel Nitride

Development of an Experimentally Derived Model for Molybdenum Carbide (Mo2C) Synthesis in a Fluidized-Bed Reactor

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Revealing Carbon Vacancy Distribution on α-MoC_1–xSurfaces by Machine-Learning Force-Field-Aided Cluster Expansion Approach

Revealing Carbon Vacancy Distribution on α-MoC_1–xSurfaces by Machine-Learning Force-Field-Aided Cluster Expansion Approach