Pulkit Agarwal

Homogeneous nucleation with magic numbers: Aluminum

Homogeneous nucleation of clusters that exhibit magic numbers is studied numerically, using as an example aluminum at 2000 K, based on recent calculations of free energies [Li et al., J. Phys. Chem. C 111, 16227 (2007)] and condensation rate constants [Li and Truhlar, J. Phys. Chem. C 112, 11109 (2008)] that provide a database for Al(i) up to i=60. The nucleation behavior for saturation ratios greater than about 4.5 is found to be dominated by a peak in the free energy change associated with the reaction iAl-->Al(i) at i=55, making it the critical size over a wide range of saturation ratios. Calculated steady-state nucleation rates are many orders of magnitude lower than predicted by classical nucleation theory (CNT). The onset of nucleation is predicted to occur at a saturation ratio of about 13.3, compared to about 5.1 in CNT, while for saturation ratios greater than about 25 the abundance of magic-numbered clusters becomes high enough to invalidate the assumption that cluster growth occurs solely by monomer addition. Transient nucleation is also predicted to be substantially different than predicted by CNT, with a much longer time required to reach steady state: about 10(-4) s at a saturation ratio of 20, compared to about 10(-7) s from CNT. Magic numbers are seen to play an important role in transient nucleation, as the nucleation currents for clusters of adjacent sizes become equal to each other in temporally successive groups, where the largest cluster in each group is the magic-numbered one.

Sectional modeling of nanoparticle size and charge distributions in dusty plasmas

Sectional models of the dynamics of aerosol populations are well established in the aerosol literature but have received relatively less attention in numerical models of dusty plasmas, where most modeling studies have assumed the existence of monodisperse dust particles. In the case of plasmas in which nanoparticles nucleate and grow, significant polydispersity can exist in particle size distributions, and stochastic charging can cause particles of given size to have a broad distribution of charge states. Sectional models, while computationally expensive, are well suited to treating such distributions. This paper presents an overview of sectional modeling of nanodusty plasmas, and presents examples of simulation results that reveal important qualitative features of the spatiotemporal evolution of such plasmas, many of which could not be revealed by models that consider only monodisperse dust particles and average particle charge. These features include the emergence of bimodal particle populations consisting of very small neutral particles and larger negatively charged particles, the effects of size and charge distributions on coagulation, spreading and structure of the particle cloud, and the dynamics of dusty plasma afterglows. (Some figures may appear in colour only in the online journal)

Numerical Simulations of Nanodusty RF Plasmas

Results are presented of numerical simulations of the spatiotemporal evolution of nanoparticles that nucleate and grow in a nonthermal radio-frequency plasma. This paper highlights the effects of several operating parameters.

Self-consistent numerical simulations of RF dusty plasma afterglows, with and without plasma pulsing

Summary form only given. Plasma pulsing is potentially a powerful tool for controlling the properties of nanoparticles that nucleate and grow in nonthermal plasmas. Numerical simulations were conducted of a capacitively-coupled RF argon-silane dusty plasma (frequency 13.56 MHz, voltage amplitude 55 V, pressure 17 Pa, electrode gap 4 cm). The simulations used a previously reported 1D fluid model,1,2 in which the plasma equations and the aerosol general dynamics equation are selfconsistently coupled. Effects considered include particle charging by electron and ion attachment, particle coagulation, and particle transport by gas drag, ion drag, electrostatic force, gravity and Brownian diffusion. Profiles of particle nucleation and particle surface growth are treated as input parameters. Simulations consider cases both with and without gas flow through a showerhead electrode.