Understanding High-Temperature Chemical Reactions on Metal Surfaces: A Case Study on Equilibrium Concentration and Diffusivity of CxHy on a Cu(111) Surface

Li, Pai; Zeng, Xiongzhi; Li, Zhenyu

doi:10.1021/jacsau.1c00483

Cited by 15 publications

(15 citation statements)

References 51 publications

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“…However, CH dehydrogenation with a weak steric effect at high temperatures is much easier than the minimum energy path prediction. Li et al [57] trained a machine learning potential with DFT data to speed up MD simulation and thermodynamic samplings. Their results also revealed some big deviations in both species concentration and diffusivity when the temperature is considered explicitly.…”

Section: Progress and Challenges Of Theoretical Study On Graphene Cvd...mentioning

confidence: 99%

“…Li et al. [ 57 ] trained a machine learning potential with DFT data to speed up MD simulation and thermodynamic samplings. Their results also revealed some big deviations in both species concentration and diffusivity when the temperature is considered explicitly.…”

Section: Progress and Challenges Of Theoretical Study On Graphene Cvd...mentioning

confidence: 99%

“…The concentration of carbon adatoms on a Cu surface cannot be measured experimentally. [ 58 ] Predictions based on both KMC simulations [ 38a ] and thermodynamic methods [ 25a,57 ] give extremely low concentrations of carbon species (≈10 –5 ML) on pure Cu at CVD growth conditions. Such a small concentration is difficult to support the observed rapid growth of graphene in experiments.…”

Section: Progress and Challenges Of Theoretical Study On Graphene Cvd...mentioning

confidence: 99%

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Achievements and Challenges of Graphene Chemical Vapor Deposition Growth

Liu

et al. 2022

Adv Funct Materials

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Graphene, since the first successful exfoliation of graphite, has continuously attracted attention due to its remarkable properties and applications. Recently, the research focus on graphene synthesis has been directed to the controllable synthesis of large‐area and high‐quality graphene. In the last decade, there has been great progress in the chemical vapor deposition (CVD) growth of graphene. Theoretical investigations have led to an enhanced understanding of puzzles on hydrocarbon species stability, key reaction pathways, the role of hydrogen gas, the morphology of graphene islands, and the alignment of graphene on substrates. Experimentally, high‐quality graphene is epitaxially grown on both insulating and metal substrates. Progress has also been reported on low‐temperature graphene growth and on controlling the thickness and stacking of graphene layers. In this review, the authors summarize the previous theoretical and experimental studies on graphene CVD growth and discuss the future challenges on the growth of graphene i) on insulating substrates, ii) at low temperature, iii) with controllable thickness, and iv) with selected stacking twist angles. The authors assert that the key to the continuous advancement of graphene growth is the synergy of experimental and theoretical investigations.

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Section: Progress and Challenges Of Theoretical Study On Graphene Cvd...mentioning

confidence: 99%

Section: Progress and Challenges Of Theoretical Study On Graphene Cvd...mentioning

confidence: 99%

Section: Progress and Challenges Of Theoretical Study On Graphene Cvd...mentioning

confidence: 99%

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Achievements and Challenges of Graphene Chemical Vapor Deposition Growth

Liu

et al. 2022

Adv Funct Materials

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“…MLFF-MD simulations were realized with large-scale atomic/molecular massively parallel simulator (LAMMPS) ( 27 ) and atomic simulation environment (ASE) ( 28 ). We adopted an iterative scheme with a series of training/MLFF-MD/labeling loops ( 29 ) to generate the complete training set, for which four sets of structures are built (more details can be found in Methods). A total of 11,008 structures were labeled, containing about 3 million force components.…”

Section: Resultsmentioning

confidence: 99%

“…For the first part, we built a bulk model, a perfect pristine Au(111) surface model with five atomic layers, and seven similar Au(111) models with different defects or steps on the top layer. We performed a series of MD simulations for each model at more than 10 temperatures ranging from 3000 to 10 K. Here, an iterative scheme is adopted to repeatedly train coarse MLFF for generating new structures ( 29 ). In each circle, we run MD with the updated coarse MLFF, extract structures from MLFF-MD trajectories to perform DFT static calculations to enrich the training set, and then retrain a better MLFF with the enlarged training set.…”

Section: Methodsmentioning

confidence: 99%

Origin of the herringbone reconstruction of Au(111) surface at the atomic scale

Ding

2022

Sci. Adv.

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The origin of the herringbone reconstruction on Au(111) surface has never been explained properly at the atomic level because the large periodic length (~30 nm) does not allow ab initio simulations of the system and because of the lack of highly accurate empirical force field. We trained a machine learning force field with high accuracy to explore this reconstruction. Our study shows that the lattice deformation in Au deeper layers, which allows the effective relaxation of the densified and anisotropic top layer lattice, is critical for the herringbone reconstruction. The herringbone reconstruction is energetically more favorable than the stripe reconstruction only if the slab thickness exceeds 12 atomic layers. Furthermore, we reveal the high stability of herringbone reconstruction at high temperatures and that a slight strain of about ±0.2% can induce a transition from the herringbone pattern to the stripe pattern, and both agree well with the experimental observations.

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Graphene‐Based Catalysts: Emerging Applications and Potential Impact

Alam,

Allag,

Naveed‐Ur‐Rehman

et al. 2024

The Chemical Record

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Carbon nanofillers in general and graphene in particular are considered as promising potential candidates in catalysis due to their two‐dimensional (2D) nature, zero bandwidth, single atom thickness with a promising high surface area: volume ratio. Additionally, graphene oxide via result of tunable electrical properties has also been developed as a catalytic support for metal and metal oxide nanofillers. Moreover, the possession of higher chemical stability followed by ultrahigh thermal conductivity plays a prominent role in promoting higher reinforcement of catalytically active sites. In this review we have started with an overview of carbon nanofillers as catalyst support, their main characteristics and applications for their use in heterogeneous catalysis. The review article also critically focusses on the catalytic properties originating from both functional groups as well as doping. An in‐depth literature on the various reaction catalysed by metal oxide based nanoparticles supported on GO/rGO has also been incorporated with a special focus on the overall catalytic efficiency with respect to graphene contribution. The future research prospective in the aforementioned field has also been discussed.

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Understanding High-Temperature Chemical Reactions on Metal Surfaces: A Case Study on Equilibrium Concentration and Diffusivity of C_xH_y on a Cu(111) Surface

Cited by 15 publications

References 51 publications

Achievements and Challenges of Graphene Chemical Vapor Deposition Growth

Achievements and Challenges of Graphene Chemical Vapor Deposition Growth

Origin of the herringbone reconstruction of Au(111) surface at the atomic scale

Graphene‐Based Catalysts: Emerging Applications and Potential Impact

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