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Influence of Onion-like Carbonaceous Particles on the Aggregation Process of Hydrocarbons.

ABSTRACT: Molecular dynamics simulations are performed to characterize the nucleation behavior of organic compounds in the gas phase. Six basic molecular species are considered-ethylene, propylene, toluene, styrene, ethylbenzene, and para-xylene-in interaction with onion-like carbon nanostructures that model soot nanoparticles (NPs) at room temperature. We identify a shell-to-island aggregation process during the physisorption of aromatic molecules on the soot surface: The molecules tend to first cover the NP in a shell, on top of which additional adsorbates form island-shaped aggregates. We present results for the binding energy, suggesting that the NPs lead to the formation of more stable molecular aggregates in comparison with the pure gas phase. Our findings describe a plausible microscopic mechanism for the active role of soot in the formation and growth of organic particulate matter.

SUBMITTER: Zhou X

PROVIDER: S-EPMC8552348 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

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Influence of Onion-like Carbonaceous Particles on the Aggregation Process of Hydrocarbons.

Zhou Xiaoqi X Meng Zhisen Z Picaud Sylvain S Devel Michel M Carrete Jesús J Madsen Georg K H GKH Zhou Yulu Y Wang Zhao Z

ACS omega 20211014 42

Molecular dynamics simulations are performed to characterize the nucleation behavior of organic compounds in the gas phase. Six basic molecular species are considered-ethylene, propylene, toluene, styrene, ethylbenzene, and <i>para</i>-xylene-in interaction with onion-like carbon nanostructures that model soot nanoparticles (NPs) at room temperature. We identify a shell-to-island aggregation process during the physisorption of aromatic molecules on the soot surface: The molecules tend to first c ...[more]

PMID: 34722989

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Project description:This paper concerns the effect of unsaturation of hydrocarbons (single, double, and triple bonds) on soot particle characteristics (mass, number, and size) and on the carcinogenicity of soot particles. The soot particles were produced from oxygen-free pyrolysis of five hydrocarbons, namely: propane, propylene, ethane, ethylene, and acetylene. The characteristics of soot particles were measured with the aid of a differential mobility spectrometer (Cambustion-DMS-500) and measurement of soot mass concentration was confirmed using gravimetric filter measurements. The soot particle carcinogenicity was estimated from the emission quantities of total polyaromatic hydrocarbons (PAHs) and the toxicity equivalent factor (TEF) of each PAH. Oxygen-free pyrolysis of the hydrocarbon fuels was conducted in a laminar tube reactor within the temperature range of 1050 -1350oC at a constant nitrogen flow rate of 20 L/min and constant fuel flow rate of 1% (vol) on carbon-1 basis. The experimental results showed that increasing unsaturation of fuels from single to double and to triple bonds increased the mass concentration, particle size, number concentration, and carcinogenicity of soot particle notably at the initial temperature of 1050 oC. Increase in the pyrolysis temperature of the tube reactor from 1050 - 1350oC, increased the mass concentration and sizes of the soot particle while the number concentration and carcinogenicity of the soot particle decreased. There was a positive correlation between the soot particle number and the corresponding soot particle carcinogenicity, while a negative correlation was observed between the soot particle mass and size with soot particle carcinogenicity regardless of the pyrolysis temperature examined. The potential implication of these observations is that, low-temperature combustion (LTC) applications, aimed at reducing emissions of soot and NOx, could produce higher soot particle number concentration of higher carcinogenicity.

Dataset Information

Influence of Onion-like Carbonaceous Particles on the Aggregation Process of Hydrocarbons.

Publications

Influence of Onion-like Carbonaceous Particles on the Aggregation Process of Hydrocarbons.

Similar Datasets

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