Image Analysis and Computer Simulation of Nanoparticle Clustering in Combustion Systems

Figures & data

TABLE 1 Methods used to validate and/or represent interactions between nanoparticle collisions

Nanoparticle system	Process used to create nanoparticles	Force identified (experimental)/force applied (simulation)	Validation method	Reference
Si	silane pyrolysis	Coulomb	experimental	Onischuk et al. (2000)
carbonaceous soot	propane combustion	Coulomb	experimental	Onischuk et al. (2000)
TiO2	flame synthesis of TiCl4	van der Waals	experimental	Froeschke et al. (2003)
Ag, Ni	spark discharge	van der Waals	experimental	Froeschke et al. (2003)
Si	simulation	van der Waals	molecular dynamics modeling	Hawa and Zacharaiah (2004)
colloidal nanoparticles	simulation	van der Waals solvation	molecular dynamics modeling	Qin and Fichthorn (2003)
colloidal nanoparticle	simulation	Coulomb	finite element modeling	Das and Bhattacharjee (2003)
CdTe	simulation	Coulomb van der Waals	Monte Carlo modeling	Sinyagin et al. (2005)
SiO2	simulation	van der Waals	moment modeling	Suh et al. (2001)

FIG. 1 Transmission electron micrograph image of flame-generated tin dioxide nanoparticle cluster.

FIG. 2 Schematic of the combustion synthesis facility used to create the tin dioxide nanoparticles.

FIG. 3 Schematic representing the (a) Case 1 binding method (van der Waals interactions) and (b) Case 2 binding method (Coulomb forces). The gray particles are particles existing within the computational boundary, and the black particle is the new particle searching for a binding site. The gray particle circled in black is the randomly chosen particle for binding.

FIG. 4 Simulation results for a _b as a function of P1 for cluster sizes N = 20, 40, 60, 80, and 100.

FIG. 5 Simulation results for notched box plots of the effect of P1 on aspect ratio a _b for N = (a) 20, (b) 60, and (c) 100.

FIG. 6 Simulation results of the effect of P1 on average main chain length normalized by particle radius, L _m /r, for cluster sizes N = 20, 40, 60, 80, and 100.

FIG. 7 Simulation results of the effect of P1 on normalized main chain length, L _m /(Nr), for cluster sizes N = 20, 40, 60, 80, and 100.

FIG. 8 Simulation results for notched box plots of the effect of P1 on L _m /r when N = (a) 20, (b) 60, and (c) 100.

FIG. 9 Simulation results for the effects of P1 on average radius of gyration, R _g /r, for cluster sizes N = 20, 40, 60, 80, and 100.

FIG. 10 Simulation results to evaluate the self-similarity of the predicted particle clusters.

FIG. 11 Simulation results for fractal dimension D _f and fractal pre-factor A as a function of P1 using 3D data and 2D projections of the 3D data.

FIG. 12 Simulation results of the effects of P1 on the ratio of L _m in 2D to R _g in 3D for cluster sizes N = 20, 40, 60, 80, and 100.

FIG. 13 TEM images of agglomerate I at different tilt angles: (a) −55°, (b) 0°, and (c) +55°.

FIG. 14 2D projections of 3D model of agglomerate I at different angles of orientation: (a) −55°, (b) 0°, (c) +55°, (d) +110°, and (e) 165°.

TABLE 2 Results of image analysis of SnO₂ nanoparticle clusters.

Parameter	Cluster I	Cluster II
N	45	16åaa
scale [pix/nm]	2.3	2.3
r [pix]	21.5	20.5
r [nm]	9.3	8.9
L m [pix]	1610.3å	536.8
L m [nm]	700.1	233.4
R g [pix]	190.2	99.3
R g [nm]	82.7	43.2
L m /r	74.9	26.2
R g /r	8.8	4.8
estimated P1 by L m /r	0–0.1	0.2–0.3
estimated P1 by R g /r	0–0.1	0–0.3

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