Exploring structural properties of small carbon clusters Cn (n = 1, 2, 3) using molecular mechanics and energy minimization

Miswan, M. A.; Gopir, Geri Kibe; Anas, M. M.

doi:10.1063/1.4966792

Cited by 2 publications

(1 citation statement)

References 12 publications

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“…Energy calculations using computational methods is not very precise for some molecules [50]. In his book, Concise Physical Chemistry, Roger stated: 'Results from some computational kinetic studies are roughly comparable to measured values, but this is a difficult field because computed activation enthalpies are very sensitive to the estimated structure of the activated complex and the rate constant is an exponential function of an H.' A discovery made by Miswan et al [51] in 2017 adds weight to this statement. The results of energy calculation can be divided into two groups: in the range of hundreds and in the range of thousands.…”

Section: Based Onmentioning

confidence: 99%

Determining the Catalytic Properties of a Material Using Relative Bond Strength

Miswan

Gopir

Anas

et al. 2017

AJOCS

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Fuel cells are one of the most promising renewable energy sources. However, they are relatively expensive due to their use of platinum, a precious metal, as the catalyst. Much research has been conducted with the aim of optimizing fuel cell efficiency, including identifying alternative catalysts. In this study, we propose a new parameter known as relative bond strength as an indicator for the catalytic properties of a material. Relative bond strength is calculated based upon the relationship between bond length and bond strength. The results of this study provide evidence for the viability of this parameter through computational calculation involving different types of materials and cluster sizes.

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Section: Based Onmentioning

confidence: 99%

Determining the Catalytic Properties of a Material Using Relative Bond Strength

Miswan

Gopir

Anas

et al. 2017

AJOCS

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Growth Patterns of Carbon Clusters C_n (n = 2–60) Identified via ABCluster Searching and DFT Benchmarking

Jia,

Wang,

Tian

et al. 2024

J. Phys. Chem. A

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Recently, novel algorithms and enhanced computational capabilities have created unprecedented opportunities to precisely determine the geometric structures of clusters through theoretical calculations. In this study, we extensively investigated and characterized the geometric arrangements of the carbon clusters C n (n = 2–60), employing the efficient ABCluster algorithm in conjunction with the gradient-corrected PBE and higher-accuracy B3LYP hybrid functional in density functional theory (DFT). New structures and a discernible structural growth pattern have been discovered. We observed a distinct preference in carbon clusters that transform from the linear chains (n = 2–9) to closed single-ring and planar structures (n = 10–27) and finally evolve to carbon cages (n = 28–60). A shortcut to construct the cage clusters was unveiled by inserting or rotating specific atoms within a distinct structural unit. The research results obtained from combining ABCluster with DFT calculations offer valuable new insights into the growth mechanisms and evolutionary trajectories of carbon clusters, providing a crucial theoretical framework for the development of innovative carbon-based materials and their potential applications.

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Exploring structural properties of small carbon clusters Cn (n = 1, 2, 3) using molecular mechanics and energy minimization

Cited by 2 publications

References 12 publications

Determining the Catalytic Properties of a Material Using Relative Bond Strength

Determining the Catalytic Properties of a Material Using Relative Bond Strength

Growth Patterns of Carbon Clusters C_n (n = 2–60) Identified via ABCluster Searching and DFT Benchmarking

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Exploring structural properties of small carbon clusters Cn (n = 1, 2, 3) using molecular mechanics and energy minimization

Cited by 2 publications

References 12 publications

Determining the Catalytic Properties of a Material Using Relative Bond Strength

Determining the Catalytic Properties of a Material Using Relative Bond Strength

Growth Patterns of Carbon Clusters Cn (n = 2–60) Identified via ABCluster Searching and DFT Benchmarking

Contact Info

Product

Resources

About

Growth Patterns of Carbon Clusters C_n (n = 2–60) Identified via ABCluster Searching and DFT Benchmarking