Aerosol particle reactions with vapor molecules and molecular clusters are often collision rate limited, hence determination of particle-vapor molecule and particle-molecular cluster collision rates are of fundamental importance. These collisions typically occur in the mass transfer transition regime, wherein the collision kernel (collision rate coefficient) is dependent upon the diffusive Knudsen number, Kn(D). While this alone prohibits analytical determination of the collision kernel, aerosol particle- vapor molecule collisions are further complicated when particles are non-spherical, as is often the case for particles formed in high temperature processes (combustion). Recently, through a combination of mean first passage time simulations and dimensional analysis, it was shown that the collision kernel for spherical particles and vapor molecules could be expressed as a dimensionless number, H, which is solely a function of Kn(D). In this work, it is shown through similar mean first passage times and redefinitions of H and Kn(D) that the H(Kn(D)) relationship found for spherical particles applies for particles of arbitrary shape, including commonly encountered agglomerate particles. Specifically, it is shown that to appropriately define H and Kn(D), two geometric descriptors for a particle are necessary: its Smoluchowski radius, which defines the collision kernel in the continuum regime (Kn(D)→0) and its orientationally averaged projected area, which defines the collision kernel in the free molecular regime (Kn(D)→∞). With these two parameters, as well as the properties of the colliding vapor molecule (mass and diffusion coefficient), the particle-vapor molecule collision kernel in the continuum, transition, and free molecular regimes can be simply calculated using the H(Kn(D)) relationship.
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http://dx.doi.org/10.1063/1.3617251 | DOI Listing |
J Environ Manage
December 2024
College of Life Sciences, Nanjing Forestry University, Nanjing, Jiangsu, 210037, China. Electronic address:
Phys Rev Lett
August 2024
Mathematics Institute, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, United Kingdom.
We develop an action formalism to calculate probabilities of rare events in cluster-cluster aggregation for arbitrary collision kernels and establish a pathwise large deviation principle with total mass being the rate. As an application, the rate function for the number of surviving particles as well as the optimal evolution trajectory are calculated exactly for the constant, sum, and product kernels. For the product kernel, we argue that the second derivative of the rate function has a discontinuity.
View Article and Find Full Text PDFPhys Rev E
July 2024
Department of Physics and Astronomy, Purdue University, West Lafayette, Indiana 47906, USA.
One-dimensional systems, such as nanowires or electrons moving along strong magnetic field lines, have peculiar thermalization physics. The binary collision of pointlike particles, typically the dominant process for reaching thermal equilibrium in higher-dimensional systems, cannot thermalize a 1D system. We study how dilute classical 1D gases thermalize through three-body collisions.
View Article and Find Full Text PDFPhys Rev E
June 2024
Institute for Multiscale Themofluids, School of Engineering, University of Edinburgh EH9 3FB, United Kingdom.
The impact of nanoscale wall roughness on rarefied gas transport is widely acknowledged, yet the associated scattering dynamics largely remain elusive. In this paper, we develop a scattering kernel for surfaces having nanoscale roughness that distinctly characterizes the two major types of interactions between gas molecules and rough surfaces. Namely these are (a) the weak perturbations arising from the thermal motion of wall atoms, essentially gas-phonon collisions, which are captured by the well-established Cercignani-Lampis model, and (b) the hard collisions owing to the irregularities of the rough, static potential energy surface, which are generally described by the fully diffuse model.
View Article and Find Full Text PDFFront Neurosci
June 2024
Academy for Engineering and Technology, Fudan University, Shanghai, China.
Intuition plays a crucial role in human driving decision-making, and this rapid and unconscious cognitive process is essential for improving traffic safety. We used the first proposed multi-layer network analysis method, "Joint Temporal-Frequency Multi-layer Dynamic Brain Network" (JTF-MDBN), to study the EEG data from the initial and advanced phases of driving intuition training in the theta, alpha, and beta bands. Additionally, we conducted a comparative study between these two phases using multi-layer metrics as well as local and global metrics of single layers.
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