journal article·2017-02

Transition of carbon nanostructures in heptane diffusion flames

Wei-Chieh Hu, Shuhn-Shyurng Hou, Ta-Hui Lin

Journal of Nanoparticle Research· Volume 19· Issue 2
Publisher page

Abstract

The flame synthesis has high potential in industrial production of carbon nanostructure (CNS). Unfortunately, the complexity of combustion chemistry leads to less controlling of synthesized products. In order to improve the understanding of the relation between flames and CNSs synthesized within, experiments were conducted through heptane flames in a stagnation-point liquid-pool system. The operating parameters for the synthesis include oxygen supply, sampling position, and sampling time. Two kinds of nanostructures were observed, carbon nanotube (CNT) and carbon nano-onion (CNO). CNTs were synthesized in a weaker flame near extinction. CNOs were synthesized in a more sooty flame. The average diameter of CNTs formed at oxygen concentration of 15% was in the range of 20–30 nm. For oxygen concentration of 17%, the average diameter of CNTs ranged from 24 to 27 nm, while that of CNOs was around 28 nm. For oxygen concentration of 19%, the average diameter of CNOs produced at the sampling position 0.5 mm below the flame front was about 57 nm, while the average diameters of CNOs formed at the sampling positions 1–2.5 mm below the flame front were in the range of 20–25 nm. A transition from CNT to CNO was observed by variation of sampling position in a flame. We found that the morphology of CNS is directly affected by the presence of soot layer due to the carbonaceous environment and the growth mechanisms of CNT and CNO. The sampling time can alter the yield of CNSs depending on the temperature of sampling position, but the morphology of products is not affected.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Carbon Nanotubes in Composites · Fullerene Chemistry and Applications · Rocket and propulsion systems research

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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