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Particle shape strongly influences the diffusion charging of aerosol particles exposed to bipolar/unipolar ions and accurate modeling is needed to predict the charge distribution of non-spherical particles. A prior particle-ion collision kernel β_i model including Coulombic and image potential interactions for spherical particles is generalized for arbitrary shapes following a scaling approach that uses a continuum and free molecular particle length scale and Langevin dynamics simulations of non-spherical particle-ion collisions for attractive Coulomb-image potential interactions. This extended β_i model for collisions between unlike charged particle-ion (bipolar charging) and like charged particle-ion (unipolar charging) is validated by comparing against published experimental data of bipolar charge distributions for diverse shapes. Comparison to the bipolar charging data for spherical particles shows good agreement in air, argon, and nitrogen, while also demonstrating high accuracy in predicting charge states up to ±6. Comparisons to the data for fractal aggregates reveal that the LD-based β_i model predicts within overall ±30% without any systematic bias. The mean charge on linear chain aggregates and charge fractions on cylindrical particles is found to be in good agreement with the measurements (~±20% overall). The comparison with experimental results supports the use of LD-based diffusion charging models to predict the bipolar and unipolar charge distribution of arbitrary shaped aerosol particles for a wide range of particle size, and gas temperature, pressure. The presented β_i model is valid for perfectly conducting particles and in the absence of external electric fields; these simplifications need to be addressed in future work on particle charging.
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Getting over the hump with KAMEL-LOBE: Kernel-averaging method to eliminate length-of-bin effects in radial distribution functions
Radial distribution functions (RDFs) are widely used in molecular simulation and beyond. Most approaches to computing RDFs require assembling a histogram over inter-particle separation distances. In turn, these histograms require a specific (and generally arbitrary) choice of discretization for bins. We demonstrate that this arbitrary choice for binning can lead to significant and spurious phenomena in several commonplace molecular-simulation analyses that make use of RDFs, such as identifying phase boundaries and generating excess entropy scaling relationships. We show that a straightforward approach (which we term Kernel-Averaging Method to Eliminate Length-Of-Bin Effects) mitigates these issues. This approach is based on systematic and mass-conserving mollification of RDFs using a Gaussian kernel. This technique has several advantages compared to existing methods, including being useful for cases where the original particle kinematic data have not been retained, and the only available data are the RDFs themselves. We also discuss the optimal implementation of this approach in the context of several application areas.
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- PAR ID:
- 10469633
- Publisher / Repository:
- American Institute of Physics
- Date Published:
- Journal Name:
- The Journal of Chemical Physics
- Volume:
- 158
- Issue:
- 22
- ISSN:
- 0021-9606
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1063/5.0138068
