Revealing the Initial Chemistry of Soot Nanoparticle Formation by ReaxFF Molecular Dynamics Simulations

Han, Song; Li, Xiaoxia; Nie, Fengguang; Mo, Zhaolan; Liu, Xiaolong; Guo, Li

doi:10.1021/acs.energyfuels.7b01194

Cited by 75 publications

(39 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The trajectory internal for analysis is 500 fs. VARxMD was developed to analyze the trajectories from ReaxFF MD simulations automatically by Liu et al 25 , which has played unique roles in investigating the reaction mechanisms of large scale reactive system with complex chemistry, including pyrolysis of coal 26 , polyethylene 27 , lignin 28 , cellulose 29 , pnitrophenol 30 as well as combustion of RP3 31 and bio-oil 32 and soot formation 33 . Therefore, VARxMD was used to obtain the at 3000 K To have an insight into the overall chemical events, the temporal evolutions of CL-20 and major intermediates and products generated during the thermal decomposition at 3000 K are depicted in Fig.…”

Section: Simulation Analysismentioning

confidence: 99%

Reaction Mechanisms in the Thermal Decomposition of CL-20 Revealed by ReaxFF Molecular Dynamics Simulations

Ren¹,

中国科学院过程工程研究所，北京²,

Li³

et al. 2018

Acta Physico-Chimica Sinica

Self Cite

View full text Add to dashboard Cite

The thermal decomposition of condensed CL-20 was investigated using reactive force field molecular dynamics (ReaxFF MD) simulations of a super cell containing 128 CL-20 molecules at 800-3000 K. The VARxMD code previously developed by our group is used for detailed reaction analysis. Various intermediates and comprehensive reaction pathways in the thermal decomposition of CL-20 were obtained. Nitrogen oxides are the major initial decomposition products, generated in a sequence of NO2, NO3, NO, and N2O. NO2 is the most abundant primary product and is gradually consumed in subsequent secondary reactions to form other nitrogen oxides. NO3 is the second most abundant intermediate in the early stages of CL-20 thermolysis. However, it is unstable and quickly decomposes at high temperatures, while other nitrogen oxides remain. At all temperatures, the unimolecular pathways of N-NO2 bond cleavage and ring-opening C-N bond scission dominate the initial decomposition of condensed CL-20. The cleavage of the N-NO2 bond is greatly enhanced at high temperatures, but scission of the C-N bond is not as favorable. A bimolecular pathway of oxygen-abstraction by NO2 to generate NO3 is observed in the initial decomposition steps of CL-20, which should be considered as one of the major pathways for CL-20 decomposition at low temperatures. After the initiation of CL-20 decomposition, fragments with different ring structures are formed from a series of bond-breaking reactions. Analysis of the ring structure evolution indicates that the pyrazine derivatives of fused tricycles and bicycles are early intermediates in the decomposition process, which further decompose to single ring pyrazine. Pyrazine is the most stable ring structure obtained in the simulations of CL-20 thermolysis, supporting the proposed existence of pyrazine in Py-GC/MS experiments. The single imidazole ring is unstable and decomposes quickly in the early stages of CL-20 thermolysis. Many C4 and C2 intermediates are observed after the initial fragmentation, but eventually convert into stable products. The distribution of the final products (N2, H2O, CO2, and H2) obtained in ReaxFF MD simulation of CL-20 thermolysis at 3000 K quantitatively agrees with the results of the CL-20 detonation experiment. The comprehensive understanding of CL-20 thermolysis obtained through this study suggests that ReaxFF MD simulation, combined with the reaction analysis capability of VARxMD, would be a promising method for obtaining deeper insight into the complex chemistry of energetic materials exposed to thermal stimuli.

show abstract

Section: Simulation Analysismentioning

confidence: 99%

Reaction Mechanisms in the Thermal Decomposition of CL-20 Revealed by ReaxFF Molecular Dynamics Simulations

Ren¹,

中国科学院过程工程研究所，北京²,

Li³

et al. 2018

Acta Physico-Chimica Sinica

Self Cite

View full text Add to dashboard Cite

show abstract

“…Traditional quantum chemical calculation methods such as the transition state theory can only study several specified reactions at a time, thus it is difficult to efficiently study such a complex system. In the past decades, molecular dynamic (MD) simulation with reactive force field (19)(20)(21) as well as ab initio MD simulations (AIMD) (22) have gradually become a main tool for the study of PAH growth and soot nucleation. AIMD is undoubtedly more rigorous and accurate.…”

Section: Introductionmentioning

confidence: 99%

Growth of Polycyclic Aromatic Hydrocarbon and Soot Inception by in silico Simulation

Wang

Zeng

Cao

et al. 2022

Preprint

View full text Add to dashboard Cite

Soot is formed resulting from incomplete combustion processes of fossil fuels and is one of the most abundant specie in the space. The early stages of soot formation are central to many ongoing studies in combustion research, but its inception and growth are still elusive and highly debated. Herein, molecular dynamic simulations with ab initio based neural network potentials were carried out to simulate the reaction process leading to the growth of PAHs and soot inception from small hydrocarbon reactants. The in silico simulation provided detailed information of reaction paths that revealed critical steps leading to the formation and growth of large PAHs. And the simulation results clearly showed that the formation and growth of possible soot inceptions are achieved through a series of reactions with small PAH radicals, particularly the two-ring PAH radicals rather than by direct combination of large polynuclear hydrocarbons.

show abstract

“…In addition, Han et al performed long time ReaxFF MD simulations of fuel-rich combustion for up to 10 ns to explore the initial formation mechanism of soot nanoparticle. 12 Large molecular system and long simulation time can produce complicit MD trajectories which contain a great number of reaction events and molecular species. Manual analysis of such trajectories is unrealistic, which motivates the development of computational algorithms that can analysis these trajectories automatically.…”

Section: Introductionmentioning

confidence: 99%

ReacNetGenerator: an Automatic Reaction Network Generator for Reactive Molecular Dynamic Simulations

Zeng¹,

Cao²,

Zhang³

et al. 2019

Preprint

View full text Add to dashboard Cite

The reactive molecular dynamics is widely used in the field of computational chemistry to study the reaction mechanisms in molecular systems. However, complex trajectories that are difficult to analyze have become a major obstacle to its application in large-scale systems. In this work, a new approach named ReacNetGen is developed to obtain reaction networks based on reactive MD simulations. Molecular species can be automatically generated from the 3D coordinates of atoms in the trajectory. The hidden Markov model is used to filter the noises in the trajectory, which makes the analysis process easier and more accurate. Compared with manual analysis, the advantage of this method in terms of efficiency is very obvious for large-scale simulation trajectories. It has been successfully used in the analysis of the simulated oxidation of 4-component RP-3 and methane.

show abstract

Revealing the Initial Chemistry of Soot Nanoparticle Formation by ReaxFF Molecular Dynamics Simulations

Cited by 75 publications

References 52 publications

Reaction Mechanisms in the Thermal Decomposition of CL-20 Revealed by ReaxFF Molecular Dynamics Simulations

Reaction Mechanisms in the Thermal Decomposition of CL-20 Revealed by ReaxFF Molecular Dynamics Simulations

Growth of Polycyclic Aromatic Hydrocarbon and Soot Inception by in silico Simulation

ReacNetGenerator: an Automatic Reaction Network Generator for Reactive Molecular Dynamic Simulations

Contact Info

Product

Resources

About